Predicting interactions of sperm and killer whales with industrial fisheries in the Southern Ocean: a spatiotemporal modelling approach for conflict mitigation

Predicting interactions of sperm and killer whales with industrial fisheries in the Southern Ocean: a spatiotemporal modelling approach for conflict mitigation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting interactions of sperm and killer whales with industrial fisheries in the Southern Ocean: a spatiotemporal modelling approach for conflict mitigation Margaux Mollier, Christophe Guinet, Clara Péron, Félix Massiot-Granier, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7009043/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 2026 Read the published version in Biodiversity and Conservation → Version 1 posted 9 You are reading this latest preprint version Abstract Toothed whale depredation of fish caught on fishing gear raises socioeconomic and conservation concerns. It can lead to substantial losses for fishers and impacts on depredating species, but effective solutions remain limited. In this study, we implemented a spatiotemporal modelling approach to predict depredation occurrence and intensity, based on natural distribution of predators involved and fishing practices, to support mitigation strategies. Using 11 years of data from the Patagonian toothfish ( Dissostichus eleginoides ) longline fisheries operating around Crozet and Kerguelen islands, and generalized additive models (GAMs), we assessed the environmental and operational factors influencing depredation by sperm whales ( Physeter macrocephalus ) and two killer whale ( Orcinus orca ) ecotypes: Crozet and Type D. All models indicated strong seasonal patterns in depredation, particularly for sperm whales, whose presence decreased in winter and was primarily driven by high abundance of large toothfish. Crozet type killer whales were associated with shallow, low-slope areas near the continental shelf, whereas Type D killer whales were more frequent in deeper waters and near seamounts, suggesting a more offshore distribution. Longer soak times and line lengths increased killer whale depredation, likely by increasing gear detectability. Crucially, vessels that moved more than 70 km after a depredation event significantly reduced the likelihood of further interactions with both predator types. The results suggest spatial overlap between fishing grounds and whale-preferred habitats, but highlight clear depredation hotspots within that overlap. Avoiding these areas provides fishers and managers with easy-to-implement, cost-effective options for mitigating depredation while maintaining the socio-economic viability of the activity. Human-wildlife interaction depredation mitigation large marine predators predictive modelling conservation Southern Ocean Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Over the past sixty years, the increasing exploitation of marine living resources associated with major changes in fishing techniques have intensified conflicts between fisheries and marine top predators (Guerra, 2019 ; Nyhus, 2016 ; Tixier, Lea, et al., 2021 ). These changes have altered the foraging behaviour of some predators, with many species of sharks and marine mammals now directly interacting with fisheries to feed on fish caught on fishing gear (Augé et al., 2012 ; Donoghue et al., 2003 ; Hamer et al., 2012 ; Kaschner & Pauly, 2005 ; Northridge, 1991 ; Read, 2008 ). This behaviour, termed “depredation”, has recently emerged as leading to a major global human-wildlife conflict, affecting marine socio-ecological systems at multiple levels (Mitchell et al., 2018 ; Read, 2005 ). Depredation has indeed been reported worldwide, affecting a wide range of fishing gear with negative impacts on fishing activities, fish stocks and marine predators (Clavareau et al., 2023 ; Gilman et al., 2006 ; Hamer et al., 2012 ; Mitchell et al., 2018 ; Trijoulet et al., 2018 ). Among depredating species, marine mammals have been reported as the taxa with the broadest range, and their interactions with fisheries often result in significant socio-economic and ecological issues (Gilman et al., 2006 ; Tixier, Lea, et al., 2021 ). The socio-economic cost of depredation to fisheries can be both direct, such as catch loss, and indirect, including additional fishing time, fuel consumption and payroll needed to complete fishing quotas, as along with the implementation of mitigation strategies to avoid predators (Maccarrone et al., 2014 ; Peterson et al., 2014 ). Marine mammal depredation may intensify pressure on already exploited fish populations, as the amount of depredated fish is often unreported and therefore not accounted for in fish stock assessments and quota allocation processes (Gasco et al., 2015 ; Hanselman et al., 2018 ; Peterson & Hanselman, 2017 ). Depredation can also threaten marine mammal populations through increased risks of by-catch on fishing gear, potential lethal retaliation from fishers operating illegally or underreported (Cosgrove et al., 2015 ; Forney et al., 2011 ; Guinet et al., 2015 ) and habituation to human-provided food sources disrupting their natural energy balance (Esteban et al., 2016 ; Tixier, Authier, et al., 2015 ). However, despite these socio-ecological impacts, the integration of marine mammal depredation into fisheries and wildlife management through ecosystem-based approaches, and the development of effective mitigation solutions, have remained limited (Clavareau et al., 2024 ; Guénette et al., 2006 ; Morissette et al., 2012 ; Trites et al., 1999 ; Williams et al., 2011 ). Attempts to mitigate depredation involve both technological and behavioural approaches. The technological approach, including using devices aimed at deterring predators from the fishing gear or protecting the fish on hooks, has showed limited effectiveness (Hamer et al., 2012 ; Mooney et al., 2009 ; O’Connell et al., 2015 ; Tixier, Gasco, et al., 2015 ; Tixier, Burch, et al., 2019 ). The behavioural approach focuses on changing fishing practices to avoid depredation. It is often easier to implement and remains more effective than the technological approach (Straley et al., 2015 ; Tixier, Vacquie Garcia, et al., 2015 ). This approach seeks to better understand the drivers of depredating species ecology, distribution and behaviour in response to human activities to improve predictions of the occurrence of depredation (Abade et al., 2014 ; Gastineau et al., 2019 ; Linnell et al., 1999 ). Such predictions can be achieved using predictive spatial models, which establish relationships between ecological variables and spatial patterns, and are widely used to inform wildlife management strategies (Guisan & Zimmermann, 2000 ). These models can produce risk maps that forecast where human-wildlife conflicts are likely to occur, providing valuable guidance for targeted management actions to reduce depredation (Edge et al., 2011 ; Goljani Amirkhiz et al., 2018 ; Miller et al., 2015 ; Treves et al., 2004 , 2011 ). Predictive spatial models have already been used to identify times of the year and/or areas of low natural presence of marine predators and, thus, of low probability of depredation (Fader et al., 2021 ; Janc et al., 2018 ; Mollier et al., 2025 ; Straley et al., 2015 ; Tixier et al., 2016 ). The spatio-temporal occurrence of depredation can be determined by factors related to both the ecology and behaviour of marine predators (hereafter “environmental” factors), and the behaviour of fishing vessels (hereafter “operational” factors). Previous studies have showed that bio-physical and temporal factors significantly affect the variability of interaction rates between marine mammals and fisheries (Cruz et al., 2016 ; Hernandez-Milian et al., 2008 ; Mollier et al., 2025 ; Monaghan et al., 2024 ). These factors define the ecological fingerprint of the depredating species (e.g., geographical range, seasonal occurrence, prey distribution). Fishing vessels within those ecological boundaries are more likely to experience high depredation (Cruz et al., 2016 ; Goetz et al., 2015 ; Hernandez-Milian et al., 2008 ; Peterson et al., 2013 ). This is likely because depredation offers an energetically efficient foraging strategy, as hunting dead, injured or immobilised prey requires low effort (Hamer et al., 2012 ). As such, depredation is likely when spatio-temporal overlap between fisheries and marine mammals occurs. Fishing activities can both facilitate access for marine mammals to prey they naturally consume or can provide them with access to new prey (Tixier, Giménez, et al., 2019 ). However, it has also been observed that marine mammals can actively search for and follow fishing vessels, potentially even beyond their usual natural distribution range (Janc et al., 2018 ; Towers et al., 2019 ). Operational factors including fisher’s decisions about where and when to fish (Stepanuk et al., 2018 ) and how to use their gear when fishing can also influence the extent of opportunities for marine mammals to depredate (Fader et al., 2021 ; Janc et al., 2018 ; Mollier et al., 2025 ; Tixier, Vacquie Garcia, et al., 2015 ). In subantarctic waters, the expansion of industrial demersal longlining (lines bearing thousands of baited hooks deployed on the seafloor) targeting Patagonian toothfish ( Dissostichus eleginoides ) in the 1990s was concomitant with sperm whale ( Physeter macrocephalus ) and killer whale ( Orcinus orca ) depredation in most fisheries (Bestley et al., 2020 ), from southern Chile (Hucke-Gaete et al., 2004 ) to the southern Indian Ocean (Tixier, Burch, et al., 2019 ). The longline fishery operating off the Crozet and Kerguelen Islands is operated by a fleet of eight commercial vessels. It is managed by the Terres Australes et Antarctiques Françaises (TAAF) administration, under the Convention for the Conservation of Antarctic Marine Living resources (CCAMLR), with a systematic presence of fishery observers monitoring and collecting data on 100% of fishing operations since the 2000s (Gasco et al., 2019 ; Gasco & Tixier, 2023 ; Pruvost et al., 2015 ). This fishery has been identified as one of the most impacted by such depredation with sperm whales remove up to 247 tonnes per year at Kerguelen, and killer whales up to 179 tonnes of toothfish from longlines per year at Crozet (Tixier et al., 2020 ). Together, sperm and killer whales depredate an amount equivalent to 30% of the total catch at Crozet and 6% at Kerguelen, representing $ 4 M USD worth of fish depredated every year (Tixier et al., 2020 ). The sperm whales depredating on toothfish catches around Crozet and Kerguelen, like in high latitudes of both hemispheres, are mostly adult or subadult males using these areas as foraging grounds through spring to autumn (Gasco et al., 2014 ; Janc et al., 2018 ; Labadie et al., 2018 ; Roche et al., 2007 ; Tixier, Welsford, et al., 2019 ). Their seasonal presence can be attributed to reproduction, with males migrating in winter to warmer waters to breed (Best, 1979 ; Jaquet, 1996 ; Mellinger et al., 2004 ; Whitehead, 2018 ; Wong & Whitehead, 2014 ). Beyond this basic understanding of their movement, no in-depth analysis on the drivers of their spatial distribution is currently available in the southern Indian Ocean. In subantarctic waters, sperm whales have been reported to feed on both cephalopods and fish, with toothfish having been confirmed as part of their natural diet (Nemoto et al., 1988 ; Tixier, Giménez, et al., 2019 ). While sperm whale distribution in high latitudes was found to be driven by oceanographic features such as slopes, eddies and fronts (Jaquet et al., 2000 ; Jaquet & Whitehead, 1996 ; Straley et al., 2014 ; Whitehead et al., 1992 ), there is a lack of evidence for an overlap between toothfish fisheries and sperm whale occurrence (Goetz et al., 2011 ; Hucke-Gaete et al., 2004 ; Purves et al., 2004 ; Roche et al., 2007 ). Two morphologically, genetically and ecologically distinct ecotypes of killer whales are known to depredate on toothfish catches around Crozet and Kerguelen (Amelot et al., 2022; Foote et al., 2013 ; Guinet & Tixier, 2011 ; Tixier, 2012 ; Tixier et al., 2016 ). The first, referred to as the “Crozet type,” is frequently observed in both inshore and offshore waters and has been monitored for over 40 years (Guinet, 1991 ; Guinet et al., 2015 ; Guinet & Tixier, 2011 ). It is generalist in its prey preferences with a diet including seals, whales, penguins, and fish (including toothfish) (Guinet, 1992 ; Tixier, Giménez, et al., 2019 ). The second ecotype, known as “Type D”, is encountered more sporadically encountered than the Crozet type, has only been observed in offshore waters (Pitman et al., 2011 ), and its natural diet remains unknown (Tixier et al., 2016 ). Sperm and killer whale populations in this region experienced sharp declines due to historical pressures; sperm whales were commercially exploited in subantarctic waters until the early 1980s (Trathan & Reid, 2009 ), while killer whales suffered from lethal retaliation by illegal or unregulated vessels in response to depredation in the 1990s (Poncelet et al., 2010 ; Tixier et al., 2017 ). In response to these declines, strong conservation measures have been implemented for the recovery of both species. These include the creation of the Southern Ocean Sanctuary by the International Whaling Commission in 1994 and the reduction of Illegal Unreported and Unregulated (IUU) fishing (Constable et al., 2000 ; Österblom & Bodin, 2012 ; Zacharias et al., 2006 ). In this study, we used 11 years of fishing data to generate spatio-temporal models that predict the occurrence of sperm and killer whale depredation on toothfish catches of industrial longline fisheries in the southern Indian Ocean. The predictions allowed us to i) assess the environmental and operational factors influencing both the probability and the severity of depredation, and ii) identify times of the year and areas that exhibit high risk of whale depredation. This modelling approach aimed at improving our understanding of the natural distribution of sperm and killer whales in relation to fishing activities and, therefore, informed spatially explicit strategies for mitigating human-wildlife conflict, thus reducing socio-economic losses for fisheries while supporting the conservation of vulnerable cetacean populations. 2. Materials and methods 2.1. Study area and data collection During the study period (1 January 2010–31 December 2020), seven commercial fishing vessels targeting Patagonian toothfish were authorized to operate in the Exclusive Economic Zones (EEZs) surrounding Crozet (44° – 48°S and 46° – 54°W) and Kerguelen (45° – 52°S and 63° – 75°W), but also beyond the Crozet EEZ, in the adjacent international waters to the west (the Del Cano Rise), which are regulated under the Southern Indian Ocean Fisheries Agreement (SIOFA) and to the south, under CCAMLR regulation (Fig. 1 ). All vessels used auto-weighted longlines that were set between two anchors at each end of the mainline, on which 7,500 ± 2,500 hooks were positioned, with an individual hook every 1.2 m, constituting a single longline set. Hooks were automatically baited and dropped to the bottom at depths ranging from 500 to 2,300 m (i.e., legal depth range to avoid the capture of juvenile toothfish (Collins et al., 2010 ). Lines were set at night to avoid seabird bycatch (Cherel et al., 1996 ; Weimerskirch et al., 2000 ). Fishing trips usually lasted three months and were undertaken all year round in the Crozet EEZ, and all year round except between 1st February to mid-March in the Kerguelen EEZ to comply with seabirds conservation measures (CCAMLR, 2015a , 2015b ). Data on fishing effort, catch and sperm and killer whale presence around the vessel were collected by fisheries observers for 100% of longline sets within the Crozet and Kerguelen EEZs and adjacent international waters, and subsequently retrieved from the PECHEKER database, provided by the Muséum National d’Histoire Naturelle de Paris (MNHN) (Martin et al., 2021 ). For each longline set deployed from a given vessel, time and position of setting (gear deployment), hauling (gear retrieval) operations, the number of hooks and the total weight and number of toothfish were recorded, as well as the occurrence of whale depredation. The presence of sperm whales and killer whales was recorded visually from the surface during hauling only and through a 3-state information: “presence” if surface observation effort was provided and whales were sighted with an assumed depredation behaviour (whales are typically observed repeatedly surfacing in the vicinity of the vessel, performing long dives towards the longline, being surrounded by seabirds and leaving slicks of fish oil at the surface); “absence” if observation effort was provided and no whales were sighted from the vessel; and “NA” if observations were not possible due to weather, sea state and/or visibility conditions. The latter were excluded from the analysis. For sets where presence was recorded during hauling, fishery observers provided minimum and maximum estimates of the number of whales present around the vessel. Crozet type and Type D killer whales were differentiated using photographs taken by fishery observers as part of the photo-identification monitoring program implemented on fishing vessels since 2003 around Crozet and Kerguelen (Tixier, Gasco, et al., 2021 ). The two ecotypes can be reliably distinguished during surface encounters and from photographs collected during fishing operations based on visible morphological features. Type D killer whales are characterised by an extremely small postocular eye patch, a bulbous forehead, and a narrow dorsal fin with a sharply pointed tip (Pitman et al., 2011 ; Tixier et al., 2025). In contrast, Crozet type killer whales exhibit a more typical morphology similar to Antarctic type A killer whales, with a larger and elongated eye patch, and a more streamlined head profile (Pitman et al., 2011 ; Pitman & Ensor, 2003 ; Tixier, Gasco, et al., 2021 ). 2.2. Environmental and operational variables The influence of environmental factors on the occurrence of depredation was examined using 13 variables known to influence the distribution of large marine predators (Table 1 ; Lerebourg et al., 2023 ; Praca et al., 2009 ; Virgili et al., 2024 ). These included both static (bathymetry, slope, distance to coast and distance to the nearest seamount) and dynamic (temperature, currents, eddy kinetic energy, chlorophyll-a concentration, sea surface height (SSH), salinity, catch per unit of effort (CPUE) and fish size) variables. Bathymetry was extracted from the GEBCO database ( https://download.gebco.net/ ), and was used to calculate the slope (in degrees) using the “terrain” function from the “raster” package in R (Hijmans & van Etten, 2025 ). The distance to the nearest seamount was derived from the geomorphology of the oceans database (Harris et al., 2014 ) and the distance to the coast was calculated from coastline position. Due to the relatively deep-diving capacities of the species involved in depredation, and the fact that depredation on longline sets of the Crozet and Kerguelen fishery mainly occurs at depth, we extracted dynamic ocean variables at various depths (the surface, between 0 and 200 m, between 200 m and 600 m and between 600 m and 2,000 m) from the Global Ocean Reanalysis (GLOBAL_MULTIYEAR_PHY_001_030 for water temperature, current velocity, sea surface height and salinity) and Global Ocean Biogeochemistry hindcast (GLOBAL_MULTIYEAR_BGC_001_029 for chlorophyll-a concentration). Both these products are provided by Copernicus ( https://data.marine.copernicus.eu/products ). The eddy kinetic energy (EKE) was calculated from the current velocity as follows: EKE = 0.5*(U2 + V2), where U and V are the two current components. The CPUE was calculated as the mean number and weight of toothfish per hook, per 0.1° cell, and per month, using only sets without depredation. The ratio between the total weight and the number of fish caught per set was also calculated as a monthly mean per cell to reflect fish size. The influence of operational factors on the occurrence of depredation was examined using 6 variables that have been shown to affect depredation on longline catch in other regions: the spatial density of vessels operating simultaneously, the soaking time, the number of hooks on longline sets, the time spent by a vessel in a fishing patch (defined as a series of longline sets hauled successively within a 70 km range from one another), the distance from previous set, and the occurrence of depredation on the longline sets previously hauled by the same vessel during the same trip (Table 1 ; Tixier, Vacquie Garcia, et al., 2015 ; Janc et al., 2018 ; Fader et al., 2021 ). These variables were all extracted from the PECHEKER database for specific positions and dates, which were also used as covariates through the latitude and longitude of the sets’ median point and the month. The density of vessels operating simultaneously was calculated as the number of vessels that hauled longline sets within 200 km and ± 3 days of the observed longline set. The number of hooks on longline sets was the total number of hooks hauled. Soaking time was calculated as the time (in hours) between the time the last hook of a longline set was deployed and the time the last hook was hauled. For the dynamic environmental variables, data were extracted for the period spanning from January 1st 2010 to December 31st 2020 with a monthly temporal resolution, and for the four depth layers. For each depth layer and variable, monthly averages and standard deviations were calculated over the 11-year period (2010–2020) to assess inter-annual variability. Static variables were extracted at a horizontal resolution of 0.004°, and dynamic variables at a resolution of 0.08° (except for chlorophyll-a at 0.25° and toothfish CPUE and size at 0.1°). For the analyses, all variables were re-scaled to a horizontal resolution of 0.1° (i.e., spatial cells of approximately 10 x 10 km). The values of these variables were assigned to each longline set based on the date and position at which it was hauled. Table 1 Environmental, operational, and spatio-temporal variables used for modelling sperm and killer whale depredation on the catch of the Patagonian toothfish fishery operating across the study area. Variable Name Units Horizontal resolution Temporal resolution Source Bathymetry bathymetry m 0.004° Static GEBCO Slope slope ° 0.004° Static Derived from bathymetry Distance to the nearest seamount nearest_seamount_distance km Static Derived from seamounts position database (Harris et al., 2014 ) Distance to the coast coast_dist km Static Derived from coast position Temperature temp_mean_surf temp_mean_0.200m temps_mean_200.600m temps_mean_600.2000m °C 0.08° Monthly (2010–2020) COPERNICUS Currents current_mean_surf current_mean_0.200m current_mean_200.600m current_mean_600.2000m m 2 .s -2 0.08° Monthly (2010–2020) COPERNICUS EKE – Eddy Kinetic Energy eke_mean_surf eke_mean_0.200m eke_mean_200.600m eke_mean_600.2000m m 2 .s -2 0.08° Monthly (2010–2020) COPERNICUS Chlorophyll-a concentration chl_mean_surf mg.m -3 0.25° Monthly (2010–2020) COPERNICUS Sea surface height SSH m 0.08° Monthly (2010–2020) COPERNICUS Salinity sal_mean_surf sal_mean_0.200m sal_mean_200.600m sal_mean_600.2000m 0.08° Monthly (2010–2020) COPERNICUS Vessel density (number of all vessels within 200km ± 3 days) density Point data PECHEKER Soaking time soak_time hours Point data PECHEKER Number of hooks set hook_set Point data PECHEKER Presence of depredation on previous set of same vessel sw_previous kw_previous Point data PECHEKER Distance from previous set distance_from_prev Point data PECHEKER Time spent in a fishing patch time_spent hours PECHEKER CPUE in nb of fish mean_cpue_nb nb fish / hooks 0.1° Monthly (2010–2020) PECHEKER CPUE in weight mean_cpue_weight kg / hooks 0.1° Monthly (2010–2020) PECHEKER Ratio weight/nb mean_ratio_weight_nb kg 0.1° Monthly (2010–2020) PECHEKER MONTH month Monthly (2010–2020) PECHEKER Longitude lon_mid ° Point data PECHEKER Latitude lat_mid ° Point data PECHEKER 2.3. Model selection Depredation by sperm and killer whales was modelled in relation to the 13 environmental, 6 operational and 3 spatio-temporal covariates described above, through 3 response variables, using Generalized Additive Models (GAMs; Hastie and Tibshirani, 1986). Models were fitted using the “gam” function from the "mgcv" package in R (Wood, 2017 ), with different distributions specified according to the nature of the response variable. The first response variable was the occurrence of depredation (i.e., presence or absence on longline sets) and was modelled using a binomial distribution with a logit link function, with separate models fitted for sperm whales, for killer whales as a whole (regardless of the ecotype), and for each killer whale ecotype (Crozet type and Type D). The second response variable was the number of individuals depredating on the same longline set (using the median of the minimum and maximum estimates provided by fishery observers, for either sperm whales or killer whales). This variable was modelled only for sets where depredation by at least one individual occurred, using a negative binomial distribution to account for overdispersion. The third response variable was the time elapsed before sperm whale and/or killer whale depredation occurred after fishing vessels started operating in a given patch, as described before. It was modelled using a Tweedie distribution, which is appropriate for continuous, positive, and zero-inflated data. Models for killer whales used data from within the Crozet area only given that the species rarely depredate in other areas. GAMs are semi-parametric regression techniques that incorporate smooth functions to flexibly capture non-linear and non-monotonic relationships between a response variable and its predictors (Wood, 2017 ). GAMs were developed using the Restricted Maximum Likelihood method. Smoothed explanatory variables were modelled with penalised thin-plate regression splines with a limited basis size of 4 to prevent overfitting (Wood, 2017 ). For the month variable, a cyclic cubic spline was used to account for the circularity of the annual cycle. For each species and ecotype, GAMs were ranked based on Akaike information criterion (AIC) scores (Akaike, 1974 ; Burnham & Anderson, 2004 ; Symonds & Moussalli, 2011 ) and a backward stepwise procedure was used for variable selection, considering a p-value of 0.05 as the threshold for excluding non-significant covariates. As collinearity between explanatory variables is known to affect the stability of a model (Dormann et al., 2013 ), Spearman coefficients were calculated between each pair of variables. Variables with coefficients > 0.7 were removed to avoid excessive multicollinearity and to retain ecologically relevant variables in the model (Zuur et al., 2010 ; Dormann et al., 2013 ; Braunisch et al., 2013 ). 2.4. Evaluation and predictions Models were run with a 10-fold cross-validation, blocked by year to account for temporal structure (Roberts et al., 2017 ). Test-train splits were generated, where each split selected 20% of the data for model evaluation (testing data), and 80% of the data for model fitting (training data). Model performance on the training data was quantified by calculating the percentage of deviance explained. External predictive performance was evaluated on the testing data. Predictive accuracy was assessed by computing the root of mean square error (RMSE) between observed and predicted values in the testing dataset (Brodie et al., 2021). Model goodness of fit was further assessed using the percentage of mean absolute error (PMAE), calculated as the mean absolute error divided by the mean observed value, where a PMAE > 100% indicates poor model fit (i.e., errors on average larger than observed values). For binomial models, predictive discrimination capacity was assessed by computing the Area Under the Receiver Operating Characteristic Curve (AUC), which quantifies the ability of the model to correctly discriminate between presence and absence across the full range of threshold values (Swets, 1988 ). Functional response plots were generated for all significant covariates (p < 0.05) for each species-specific model (sperm whales, Crozet type killer whales, and Type D killer whales), as well as for the model using killer whales as a whole (regardless of the ecotype) and for each response variable modelled. The selected models were used to predict the spatial probability of depredation occurrence, the number of individuals depredating on the same longline set and the time elapsed before depredation occurred in a given patch. Models were runacross the entire study area for sperm whales and within the Crozet area only for killer whales. Predictions were made on a 10 x 10 km resolution grid, using the “predict” function of the “mgcv” R package (Wood, 2017 ). Prediction maps were produced using the monthly grids of environmental variables. New spatial grids were created for the vessel density and the mean CPUE (both in weight and number), calculated over a 0.1° x 0.1° spatial grid. The mean ratio between weight and number of toothfish was also calculated over the same grid and mapped by season (Figure S1 ). Other operational variables were set to their median values in the predict function and the occurrence of sperm whale or killer whale depredation on the longline set previously hauled by a given vessel was fixed at 0. Only the areas where fishing data were available were represented in the prediction maps. Monthly predictions were averaged over the entire study period (2010–2020) and the mean standard error of predictions was reported as a metric of uncertainty. Predictions were also averaged over seasons: summer (December-February), autumn (March-May), winter (June-August) and spring (September-November). Throughout the manuscript, predicted mean values are presented along with their associated standard errors, with minimum and maximum observed values in parentheses. 3. Results Data were analysed from a total of 41,671 longline sets (including sets with missing depredation data; 4, 953 sets for sperm whales and 2, 933 sets for killer whales) deployed by 7 vessels between 2010 and 2020 in the study area. Overall, out of the 41,671 sets, 15,107 were subject to depredation by sperm whales (36.3%), 3,811 sets were subject to depredation by killer whales (9.1%), including 1,102 by Crozet type killer whales (2.6%) and 196 by Type D killer whales (0.5%), and 1,943 sets (4.7%) were subject to depredation by both sperm and killer whales. In contrast, 21,613 sets showed no depredation by sperm whales, while killer whales were not observed in 34,929 sets. Out of the 29,911 sets deployed in the Kerguelen EEZ, 10,148 were subject to depredation by sperm whales (33.9%) and 56 by killer whales (0.2%). For the Crozet EEZ, a total of 10,729 sets were deployed with 4,840 sets that were depredated by sperm whales (45.1%), 3,695 sets that were depredated by killer whales (34.4%; 1,091 by Crozet type and 181 by Type D killer whales) and 1,942 sets depredated by both sperm and killer whales (18.1%). For the remaining 2,423 sets, the type of killer whale involved in depredation could not be identified using the photo-identification database. 3.1. Models selection and drivers of depredation After checking for correlations between variables (Figure S2), the models selected based on AIC and REML indicated that the variables best explaining the occurrence of depredation, the number of individuals depredating on the same longline set, and the time elapsed before depredation occurred in a given patch differed between species and ecotypes (Table 2 ). From the binomial models, the occurrence of depredation by sperm whales and killer whales as a whole (regardless of the ecotype) was primarily explained by spatio-temporal interactions (latitude, longitude, month), environmental and oceanographic features (e.g., SSH, salinity, current speed, EKE), fishing effort metrics (e.g., soaking time, toothfish CPUE, toothfish size, density of vessels, time spent in fishing patches), seabed features (e.g., distance to coast and seamounts, slope, bathymetry) and the occurrence of depredation on the set previously hauled by the vessel, with some variation in the relative importance of key predictors across taxa (Figs. 2 , S3, S8, S14, S20). From the models with negative binomial distribution outputs, the number of individuals depredating on the same longline set was influenced across all taxa by the interaction between latitude, longitude and month, and by the number of hooks set (Figs. 2 , S4, S10, S16, S23). For sperm whales, the number of individuals depredating on the same longline set was additionally influenced by toothfish CPUE and size, bathymetry, soaking time, distance to coast and seamounts and time spent in fishing patches (Figs. 2 , S4). The model fitted to killer whales as a whole showed a strong influence of distance to the coast and seamounts, temperature at 600–2,000 m depth, current speed, slope, SSH, and soaking time, similar to the model fitted to Crozet type killer whales only (Figs. 2 , S10, S23). However, the number of Type D killer whales depredating on the same longline set was primarily associated with salinity at 600–2,000m depth and distance to seamounts (Figs. 2 , S16). For sperm whales, the time elapsed before depredation occurred in a given patch was mainly influenced by toothfish CPUE, toothfish size, soaking time, slope, SSH, latitude/longitude, and number of hooks set (Figs. 2 , S7). For killer whales, the main predictors were the distance to coast, month, toothfish size, number of hooks set, currents at 600–2,000m depth and toothfish CPUE (Figs. 2 , S12, S18, S25). Across the 10-fold cross-validation, the mean percentage of deviance explained by the final models for sperm whales ranged from 15.8–35.9%, with the highest value observed for the binomial model and the lowest for the negative binomial model (Table 2 ). The binomial model had better performance metrics than the others, with a lower RMSE (0.4) and PMAE (26.9%) compared to the negative binomial (RMSE: 1.7; PMAE: 45.8%) and Tweedie models (RMSE: 2.4; PMAE: 69.8%). For killer whales, the mean percentage of deviance explained ranged from 18.6% (negative binomial) to 40.1% (Tweedie), with the binomial model having a lower RMSE (0.4) and PMAE (34.0%) than the other models. The binomial models for Crozet type and Type D killer whales showed higher performance, with RMSE and PMAE values of 0.3 and 19.2% for Crozet type, and 0.1 and 4.2% for Type D. The mean percentage of deviance explained ranged from 21% (negative binomial) to 24.4% (binomial) for Crozet type, and from 20.3% (binomial) to 44.8% (Tweedie) for Type D killer whales. AUC values were high across all taxa for binomial models: 0.89 for sperm whales, 0.79 for killer whales as a whole, 0.81 for Crozet type killer whales, and 0.76 for Type D killer whales. Due to high PMAE values for the Crozet type (140.3%) and Type D killer whales (147.6%) Tweedie models, only results from the Tweedie model fitted to the time elapsed before depredation by killer whales as a whole occurred after fishing vessels started operating in a given patch are reported in this study. Table 2 Summary of selected models for each taxon and each response variable. P-values are reported for all covariates (- correspond to non-significant covariates with a p-value > 0.05 dropped from the models). Dev. Exp: Percentage of deviance explained, indicating model explanatory power. RMSE: Root of Mean Square Error, where lower values indicate better model fit. PMAE: Percentage Mean Absolute Error, indicating model accuracy (indicative values: PMAE > 100% = poor; 50–100% = fair to good; 0.9 = outstanding). Environmental covariates: TEMP: temperature; CHL: chlorophyll-a concentration; SAL: salinity; CURR: currents velocity; EKE: eddy kinetic energy; SSH: sea surface height; CPUE: catch per unit of effort. Blank cells indicate that the covariate was not included in the model. Species/type Sperm whales KW Crozet type KW Type D KW (both ecotypes) Resp. var. Occurrence Nb indiv. Time before depredation Occurrence Nb indiv. Time before depredation Occurrence Nb indiv. Time before depredation Occurrence Nb indiv. Time before depredation Stat. distrib. Binomial Negative binomial Tweedie Binomial Negative binomial Tweedie Binomial Negative binomial Tweedie Binomial Negative binomial Tweedie Dev. Exp (%) 35.9 15.8 24 24.4 21 23.8 20.3 24.1 44.8 24.2 18.6 40.1 RMSE 0.4 1.7 2.4 0.3 1.1 2.1 0.1 6.3 2.3 0.4 4.6 2.1 PMAE (%) 26.9 45.8 69.8 19.2 33.7 140.3 4.2 37.6 147.6 34.0 35.5 65.4 AUC 0.89 0.81 0.76 0.79 Environmental covariates DEPTH < 0.001 < 0.001 - - < 0.05 - - - - < 0.05 - - SLOPE < 0.001 - < 0.01 < 0.001 < 0.05 - - - - < 0.001 < 0.01 < 0.05 TEMP surf < 0.01 - - - - - - - - < 0.05 - - 0-200m < 0.05 - - - - - - - - < 0.01 - - 200-600m < 0.05 - - - - - - - - < 0.001 - - 600-2000m < 0.001 - - - - - - - - < 0.001 < 0.001 < 0.05 CHL surf < 0.001 < 0.001 < 0.01 < 0.05 - - - - - - < 0.001 < 0.001 SAL surf - - - - - - < 0.05 < 0.05 - - < 0.001 < 0.001 0-200m - - - - - - < 0.05 < 0.05 - - < 0.01 < 0.001 200-600m - - - - - - < 0.05 < 0.05 - < 0.001 < 0.001 < 0.001 600-2000m - < 0.05 - - - - < 0.01 < 0.05 - < 0.01 - < 0.001 CURR surf < 0.001 - - - < 0.01 - < 0.05 - - < 0.01 < 0.001 - 0-200m - - - - < 0.05 - - - - - < 0.05 - 200-600m - - - - - - - - - - - < 0.01 600-2000m - - - - - < 0.05 - - - - - < 0.001 EKE surf - - - - - - - - - - < 0.01 - 0-200m - - - - - - - - - - < 0.05 - 200-600m - - - - - - < 0.05 - - < 0.05 - - 600-2000m - - - - - - < 0.05 - - < 0.001 - < 0.05 SSH < 0.001 < 0.001 < 0.001 < 0.05 < 0.01 - < 0.01 - < 0.01 < 0.001 < 0.001 < 0.001 DIST. COAST < 0.001 < 0.001 < 0.05 < 0.001 < 0.05 - - - - < 0.001 < 0.001 < 0.001 DIST. SEAMOUNT < 0.001 < 0.001 < 0.05 - < 0.001 - < 0.001 < 0.05 - - < 0.001 < 0.001 Fish abundance covariates CPUE NB < 0.001 < 0.001 - - - < 0.01 - - - - - < 0.01 CPUE PDS < 0.001 < 0.01 < 0.001 - - < 0.001 - - - < 0.01 - - RATIO PDS NB < 0.001 < 0.001 < 0.001 < 0.001 - - - - < 0.01 < 0.001 - - Temporal covariate MONTH < 0.001 < 0.001 < 0.01 < 0.001 < 0.05 < 0.001 < 0.001 - < 0.01 < 0.01 < 0.001 < 0.001 Operational covariates VESSEL DENSITY < 0.001 < 0.01 - < 0.001 - - < 0.01 - - < 0.001 - < 0.001 SOAK < 0.001 < 0.001 < 0.001 < 0.001 - < 0.01 < 0.05 - - < 0.001 < 0.001 < 0.001 NB HOOKS < 0.001 < 0.001 < 0.05 < 0.05 < 0.001 - - < 0.001 - - - < 0.01 TIME SPENT < 0.05 < 0.001 - - < 0.001 - < 0.001 - < 0.001 DIST PREV < 0.001 < 0.05 < 0.001 - < 0.05 - < 0.001 < 0.01 DEP PREV < 0.001 < 0.05 < 0.001 - < 0.05 - < 0.001 < 0.01 Spatial covariates LON < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.05 < 0.001 < 0.01 - < 0.001 < 0.001 < 0.001 LAT < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.05 < 0.001 < 0.01 - < 0.001 < 0.001 < 0.001 3.2. Predictions of sperm whale depredation hotspots For spatial predictions from the final models, the operational variables included in the models were fixed at a mean value (hook_set = 7,500; soak = 28) and a median value of distance from the set previously hauled by a given vessel. Two different values of time spent in the same fishing patch (24 hours and 100 hours) were tested to account for spatial autocorrelation. The mean predicted probability of sperm whale depredation occurrence was high (P(depredation) = 0.51 ± 0.05) throughout the study area where fishing data were available (Crozet, P(depredation) = 0.75 ± 0.1 (0.18–1); Kerguelen EEZ, P(depredation) = 0.42 ± 0.03 (0.03–1)). The standard error of predictions ranged from 0 to 0.23 throughout the prediction area, and was high (SE > 0.15) in the northern part of Crozet and the Del Cano Rise. When depredation by sperm whales occurred, the mean predicted number of depredating individuals was 2.5 ± 0.5 (1–11) in the Crozet area and 3 ± 0.2 (1–19) in the Kerguelen EEZ. The mean predicted time elapsed before sperm whale depredation occurred in a given patch was 4.2 ± 1.6 days (0.9–11 days) in the Crozet area and 1.7 ± 0.4 days (0.2–8 days) in the Kerguelen EEZ. High probabilities (> 0.75) of sperm whale depredation occurring were found in the south of Crozet with maximum probabilities in the south-west and in the western part of the Kerguelen Plateau (Fig. 3 a). Concentrations of 0.1° x 0.1° grid cells with high probabilities of depredation (> 0.6) were also visible in the north-western and north-eastern part of Kerguelen (Fig. 3 a). Seasonal predictions showed that the mean probability of sperm whale depredation occurring was highest in spring (P(depredation) = 0.59) and the lowest in winter (P(depredation) = 0.43) (Fig. 4 ) but there were more areas with a higher number of depredating individuals around Kerguelen in summer (Figure S5). When depredation occurred, the highest predicted number of depredating individuals (≥ 5 individuals) was found in the south-western and north-eastern part of Kerguelen and the southern part of Crozet (Fig. 3 b). The areas with the highest predicted numbers of depredating individuals were more prominent when the time spent in a fishing patch was 24 hours compared to 100 hours (Figure S6). In the northern and north-western parts of Kerguelen, as well as around Crozet, sperm whale depredation occurred as soon as vessels started operating in a given patch, whereas in the south-west of Kerguelen, it took between 2 and 3 days before sperm whale depredation occurred. (Fig. 3 c). 3.3. Predictions of killer whale depredation hotspots The mean predicted probability for killer whale (regardless of the ecotype) depredation occurring was 0.32 ± 0.02 in the Crozet EEZ with individual values ranging from 0.04 to 0.92. For Crozet type killer whales the probability was 0.12 ± 0.02 (0.01–0.68), while for Type D killer whales the mean was 0.02 ± 0.002 (0–0.07). High probabilities (> 0.5) of killer whale depredation occurring were found in the south-eastern part of Crozet archipelago, especially for the Crozet type (Figs. 5 a, S21). Type D killer whales were more likely to depredate in the southern part of Crozet (P(depredation) > 0.04) (Fig. 6 a). When depredation by killer whales (regardless of the ecotype) occurred, the mean predicted number of depredating individuals was 7 ± 0.6 (4.5–12) everywhere around Crozet (Figure S24). This mean was 9 ± 2.2 individuals (5–15) for the Crozet type and 10 ± 5.1 individuals (9–16) for the Type D killer whales. The highest predicted numbers (> 10 individuals) for the Crozet type were mainly located in the south-eastern and north-western parts of the Crozet archipelago (Fig. 3 b). For Type D (> 11 individuals), they were primarily in the south-eastern and northern parts of the Crozet archipelago (Fig. 4 b). Seasonal predictions showed a higher probability of depredation occurrence by killer whales, especially for Crozet type, in spring (between September and November), and a lower probability in winter (between June and August) (Figure S8). These seasonal variations were not associated with major spatial shifts in predicted depredation occurrence hotspots (Figures S9, S15, S22), nor in the number of individuals involved (Figure S11). However, the areas of high predicted number of Type D killer whales involved in depredation changed consistently over months (Figure S17). The mean predicted time elapsed before killer whale (regardless of the ecotype) depredation occurred in a given patch was 1.4 ± 0.8 days (0.3–3.5 days). In the eastern part of Crozet, killer whale depredation occurred as soon as vessels started operating in a given patch, whereas in the Del Cano Rise, killer whale depredation did not start until up to 3 days after vessels started operating in a given patch (Figs. 7 , S13, S19). 4. Discussion This study shows that sperm and killer whale depredation on Patagonian toothfish catches of the longline fishery operating in the southern Indian Ocean varied in space and time and was influenced by both environmental and operational variables. The effectiveness of the models (high percentages of deviance explained and high predictive performances) allowed for reliable predictions of the probability of depredation to occur, the number of depredating individuals and the time elapsed before depredation occurred when vessels started operating in a given patch. The results provide insights into the drivers of the natural distribution of sperm and killer whales in the region, as well as how fishers use their gear may influence depredation. Together, these insights can inform ways to better anticipate, and thus better avoid, whale depredation. Natural distribution of sperm and killer whales For sperm whales, the results indicate that depredation events are less likely to occur on sets hauled in winter, with a lower occurrence of depredation, a lower number of depredating individuals and a greater time elapsed before depredation occurred in a given patch compared to other seasons. This seasonal variation has been reported in previous studies (Janc et al., 2018 ; Labadie et al., 2018 ; Tixier, Burch, et al., 2019 ; Tixier, Welsford, et al., 2019 ) and was attributed to migration patterns of adult male sperm whales moving between feeding grounds in cold waters and reproduction grounds in tropical and sub-tropical waters (Jaquet et al., 2000 ; Madsen et al., 2002 ; Mellinger et al., 2004 ; Teloni et al., 2008 ). However, the amplitude of this seasonality was lower around Crozet than around Kerguelen, with instance model predictions showing higher probabilities of sperm whale depredation occurrence between June and November in Crozet. These differences may be explained by the smaller size of the fishing area and the higher density of depredating sperm whales in Crozet compared to Kerguelen (Labadie et al., 2018 ) or a lower proportion of individuals leaving the foraging grounds in Crozet compared to Kerguelen. These two factors may also explain the greater spatial variation in sperm whale depredation occurrence at Kerguelen than at Crozet, with a strong latitudinal gradient identified in models showing high probabilities in the northern reaches of the Kerguelen Plateau, as previously observed by Tixier, Welsford, et al. ( 2019 ). Sperm whale depredation was more likely to occur on sets hauled at greater depths (> 2,000 m), and in areas characterised by intermediate to steep bathymetric slopes (0 to 10°), suggesting a preference for deep and complex topographic habitats. This aligns with the known distribution of adult males along the outer edges of oceanic shelves (Whitehead, 2018 ), where steep slopes and greater depths typically support enhanced biological productivity and prey aggregation. Similar patterns have been reported in other high-latitude regions, where sperm whale presence is shaped by static oceanographic features, such as bathymetric gradients, and dynamic processes like eddies and oceanic fronts, which drive prey abundance and availability (Whitehead et al., 1992 ; Jaquet, 1996 ; Jaquet & Whitehead, 1996 ; Jaquet et al., 2000 ; Straley et al., 2014 ; Wong & Whitehead, 2014 ). In our study area, the probability of sperm whale occurrence was associated with depressed sea surface height (between − 1.5 and 0 m), potentially indicating mesoscale activity (e.g., eddies) or fronts (Park et al., 2019 ) that could enhance foraging opportunities (Bestley et al., 2020 ). In addition, all models highlighted an association between sperm whale depredation and seamounts. Seamounts can have particular ecological significance, as they are associated with long-lasting trophic webs and enhance biological productivity, thereby providing predictable foraging hotspots for top predators (Cascão et al., 2017 ; Morato et al., 2010 ; Pitcher et al., 2007 ; Rogers, 2018 ; Sergi et al., 2020 ). Toothfish richness was a strong predictor of sperm whale depredation, with the occurrence of depredation and the number of depredating individuals being positively associated with the number of fish caught per hook. Although not systematic, hotspots of sperm whale depredation overlapped with areas of high toothfish CPUE (Hucke-Gaete et al., 2004 ; Tixier et al., 2010 ) and areas of large toothfish size. In these areas, sperm whales started depredating sooner than in other areas when vessels operated within the same patch for prolonged periods of time. For example, sperm whales were often already present before the arrival of fishing vessels in the western and northern parts of Kerguelen, which are areas characterised by high toothfish abundance, particularly of larger individuals. Studies on the spatial distribution of Patagonian toothfish showed that as they approach maturity, large fish move downslope to deep-sea habitats (from 1,200 m to > 2,300 m) and head towards the spawning grounds on the western side of the Kerguelen Plateau (Péron et al., 2016 ; Welsford et al., 2011 ; Yates et al., 2018 ). Altogether, these results support the assumption that toothfish is an important prey for sperm whales in the region, with individuals preferentially foraging on large toothfish and therefore co-occurring with fishers in areas where large individuals are predominant. The Crozet type killer whales were most likely to depredate in spring and least likely in winter. This finding contrasts with previous studies that reported a decrease in Crozet type killer whale depredation in December (Tixier et al., 2016 ). These studies associated this decrease with prey-switching, such as to juvenile southern elephant seals ( Mirounga leonina ), which are abundant in inshore waters while breeding on the islands at this time of year (Guinet, 1992 ; Guinet & Bouvier, 1995 ; Tixier et al., 2010 ). This difference may be explained by potential shifts in the feeding behaviour of the Crozet type killer whales, which are also known to feed on prey like recovering large whales further offshore (Guinet, 1992 ; Guinet et al., 2000 ). A recent shift in fishing effort may also explain this difference, with more fishing vessels operating at Crozet and thus providing the whales with more opportunities to depredate in spring. The effect of year was not included in the models, as the study focused on spatial and seasonal patterns of depredation, but future work could explore interannual variability for all the types of predators. For Type D killer whales, depredation was most likely in autumn. Interestingly, the number of depredating individuals increased in winter, and the areas of high predicted numbers of depredating individuals changed consistently across seasons. These seasonal patterns may reflect aspects of the natural ecology of Type D killer whales, but given that the main prey of these killer whales is unknown, the underlying drivers of seasonal variation remain unclear. The two killer whale ecotypes differed in the spatial distribution of their depredation on toothfish catches, with the Crozet type killer whales being more likely to depredate in the south-eastern part of the Crozet archipelago and Type D killer whales being more likely to depredate in the southern part. For the Crozet type, the probability of depredation and the number of depredating individuals increased over low-slope grounds, and the probability of depredation increased with the distance from the coast. This suggests that the distribution of Crozet type killer whales is predominantly offshore, with inshore foraging grounds being used for limited periods of the year and driven by seasonal variation in prey abundance in the region (Guinet, 1991 ; Guinet et al., 2015 ). Consistent with previous studies, hotspots of depredation for this ecotype were detected in shallower waters over the insular shelf (Tixier et al., 2016 ; Tixier, Vacquie Garcia, et al., 2015 ). Interestingly, the occurrence of depredation by both Crozet type and Type D killer whales was elevated near seamounts, and for Type D killer whales, many hotspots of depredation were located near seamounts in deep waters. In addition, Type D killer whale depredation correlated with salinity at 600-2,000m depth, suggesting an association with specific oceanic water masses that structure prey availability in pelagic ecosystems (McMahon et al., 2019 ). This is in line with the assumed pelagic feeding ecology of this ecotype in subantarctic waters (Pitman et al., 2011 ). In contrast to sperm whales, the influence of toothfish CPUE on the occurrence of depredation by killer whales was limited for both Crozet type and Type D killer whales, with large toothfish not being specifically targeted. For the Crozet type killer whales, this can be explained by the fact that large toothfish was estimated contributing to only about 30% of their diet (Tixier, Giménez, et al. 2019 ). For Type D killer whales, this suggests that toothfish is not a primary prey item of their likely oceanic feeding ecology (Tixier et al., 2016 ), although the limited data and large uncertainty in the results for this ecotype preclude any strong conclusion. Spatial predictions revealed hotspots of killer whale depredation, with killer whales being rapidly present around fishing vessels when these started operating. However, the low correlation between toothfish abundance and killer whale depredation suggests that the action of fisheries, by offering opportunities to feed on high-calorie prey with limited foraging effort, may alter to some extent the natural distribution, foraging activity and prey intake for the species in the region. Accurately determining this extent is critical for ecosystem-based management of toothfish fisheries (Clavareau et al., 2020 ), and should be supported by further research on the diet and foraging ecology of the killer whale ecotypes involved in depredation. In addition, since both sperm and killer whales have been reported to selectively depredate Patagonian toothfish, interspecific competition for the same resource may also occur when both species depredate the same longline sets simultaneously, and could be further investigated by integrating predator types within a single model. Influence of fishing behaviour on sperm and killer whale depredation Our results show that occurrence of depredation by both sperm and killer whales is influenced by operational factors related to fishers’ behaviour, and, more specifically, the extent to which fishers provided predators with opportunities to depredate. The probability of depredation by both Crozet type and Type D killer whales increased when soaking time exceeded 25 hours. For Crozet type killer whales, both the probability of depredation occurring and the number of depredating individuals increased when more than 5,000 hooks were set per line. These results suggest that giving sperm and killer whales more time to locate the fishing gear and access the catch increases the likelihood that depredation occurs (Tixier, Vacquie Garcia, et al., 2015 ). However, the patterns observed in the model outputs may reflect both predator behaviour and fishers’ reactive strategies to depredation, potentially biasing the interpretation of the results. Increasing number of vessels operating simultaneously significantly decreased the probability of depredation by sperm whales and killer whales on longline sets. This is likely due to the limited number of depredating whales in the fishing area, whereby an increased number of vessels leads to a dilution effect, reducing the probability of any single vessel interacting with whales (Tixier, Vacquie Garcia, et al., 2015 ). However, the cumulated depredation effects on all vessels operating simultaneously in a given area remain to be properly assessed. The time spent by vessels within a patch did not lead to an increased occurrence of depredation by either sperm whales or killer whales, suggesting that fishing vessels may not act as strong attractors, but rather that the whales were already present in fishing areas. This result supports the assumption of co-occurrence between whales and fishing activities in productive areas as discussed above. Depredation hotspots often reflect areas of high fish density, where both predators and fishers co-occur. As a result, CPUE may paradoxically be higher in these zones, despite depredation, than in areas with lower predator presence but also lower fish abundance. In line with previous studies showing that sperm and killer whales are able to actively follow vessels when they travel from one fishing ground to another (Janc et al., 2018 ; Tixier, Vacquie Garcia, et al., 2015 ; Towers et al., 2019 ), in this study, vessels were significantly less likely to experience depredation on subsequent longline sets if they travelled distances greater than 70 km. While further analyses using photo-identification would be required to examine the drivers of whales deciding to follow vessels at a finer scale, these results suggest that sperm and killer whales do not typically follow vessels over large distances, supporting the use of spatial displacement as a mitigation measure. Implementing a “move-on” strategy has been shown as effective in reducing odontocete depredation in other regions (Forney et al., 2011 ; Peterson & Carothers, 2013 ), but this effectiveness may depend on local factors such as the size of fishing grounds, the density of sperm and killer whales, and variation in the motivation to depredate across individuals within populations (Auguin et al., 2024 ; Tixier et al., 2010 ; Towers et al., 2019 ). Implications for conflict mitigation The results of this study could be used to develop operational fishing strategies that minimise sperm and killer whale depredation on longline fisheries in the southern Indian Ocean. First, improved understanding of the ecology and distribution of the whales can help fishers better anticipate depredation risks and adjust their operations to avoid areas naturally used by the whales. Second, from an operational perspective, increasing distance travelled between fishing grounds, increasing the density of vessels operating simultaneously, reducing soaking times, and shortening longline sets length, which do not impact CPUE (Tixier et al., 2010 ; Tixier, Vacquie Garcia, et al., 2015 ), may work as easy-to-implement mitigation measures in anticipation of, or in response to, depredation. The models developed in this study helped identify hotspots of sperm and killer whale depredation, with high probability of occurrence located mainly in the south-west and the north of Kerguelen for sperm whales, and in the south-east of Crozet for killer whales. These areas could be avoided by fishers without severely reducing their fishing success or having to travel longer distances and spend more time at sea, the latter factors being often cited as indirect costs of depredation avoidance strategies (Gilman et al., 2006 ; Peterson et al., 2014 ; Tixier, Lea, et al., 2021 ). Indeed, alternative areas of high toothfish CPUE, though associated with smaller fish, were identified in the south-east of Kerguelen, offering fishers opportunities to maintain high catch rates while reducing the risk of depredation. While implementing avoidance strategies may impose additional constraints on fishers (Janc et al., 2021 ; Maccarrone et al., 2014 ; Peterson et al., 2014 ), comprehensive bio-socio-economic assessments of the costs and benefits associated with changes in fishing practices are necessary. For instance, the “move on” technique may result in increased non-fishing time and fuel consumption, potentially making this strategy less economically attractive or sustainable for the fishery (Richard et al., 2018 ). To ensure profitability, these additional costs should not outweigh the benefits gained from reducing depredation (Trijoulet, 2016 ; Trijoulet et al., 2018 ). In addition, the closure of the Kerguelen zone to reduce seabird mortality during February and March likely reduces depredation in this area, but also results in increased fishing effort, and therefore increased opportunities for the whales to depredate, around Crozet during this period. Further assessments are also needed to ensure that such operational adaptive measures align with fisheries regulations and resource management strategies. This alignment of regulations and outcomes remains one of the major challenges for various stakeholders (Doyen et al., 2012 , 2017 ; Gourguet et al., 2013 ; Nielsen et al., 2018 ). 5. Conclusion Depredation of Patagonian toothfish by sperm whales and killer whales in subantarctic waters of the Southern Indian Ocean is a major concern for the actors of the fishery, including fishers, managers and researchers working on the ecology and the conservation of whale populations and fish stocks. This concern is shared by the actors of many other fisheries, as documented depredation of catches by large marine predators has increased globally. Our study highlights the importance of understanding the factors driving the spatio-temporal distribution of depredation in developing effective mitigation strategies. Based on long-term data collected in a fishery with a 100% coverage by fishery observers, our results identified a clear spatial overlap between predator-preferred habitats and productive fishing grounds, but also highlighted operational factors that can either exacerbate or reduce depredation risk. Integrating spatio-temporal risk maps into fisheries management rules could help limit economic losses from the conflict associated with depredation, while also reducing its ecological impacts on vulnerable fish stocks and marine predator populations. The distribution models developed in this study provide valuable insights into the mechanisms that may influence and help mitigate interactions of sperm and killer whales with this fishery, with potential applicability to other fisheries facing similar challenges. Future research should explore predator behaviour at fine spatial scales, assess the long-term effectiveness of avoidance strategies, and integrate socio-economic considerations to co-develop mitigation tools that are both practical for fishers and beneficial for conservation. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was supported by the PPR Océan et Climat. Author Contribution M.M.: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization (including species illustrations), Writing – original draft, Writing – review and editing. C.G.: Conceptualization, Investigation, Writing – review and editing. C.P.: Investigation, Resources. F.MG.: Investigation, Resources, Writing – review and editing. N.G.: Data Curation, Investigation, Resources, Writing – review and editing. C.C.: Data Curation, Investigation, Resources, Writing – review and editing. S.D.: Methodology, Validation, Writing – review and editing. E.W.: Validation, Writing – review and editing. S.C.: Validation, Writing – review and editing. V.R.: Methodology, Validation. C.M.: Funding acquisition, Supervision, Validation, Writing – review and editing. P.T.: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – review and editing. Acknowledgement This work was supported by the PPR Océan et Climat. We are grateful to the Muséum National d'Histoire Naturelle of Paris and especially P. Pruvost, A. Martin and C. Chazeau, for providing the data from the “PECHEKER” database. This work could not have been possible without the extensive and rigorous contribution of all the fishery observers and scientific fieldworkers for collecting the data on-board the fishing vessels of the French Patagonian toothfish fishery. We thank the Terres Australes et Antarctiques Françaises (TAAF), with both the DPQM and the DE, for supporting the work of the fishery observers and scientific fieldworkers. We thank the French Polar Institute (IPEV Program 109, coordinator: Christophe Barbraud at CEBC-CNRS) with the help of Karine Delord and Dominique Besson (CEBC-CNRS) for support in the long-term monitoring programs of whale populations by photo-identification. We also thank the crews of fishing vessels, the toothfish fishing companies (SARPC & Fondation d’Entreprise des Mers Australes), and the Direction des Pêches Maritimes et de l’Aquaculture (DPMA) for their contribution to data collection. Data availability The authors do not have permission to share data. 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Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Stuart","middleName":"","lastName":"Corney","suffix":""},{"id":511519114,"identity":"7c8f2a5f-ef67-4a0c-ae98-04ab95a6c539","order_by":9,"name":"Vinicius Robert","email":"","orcid":"","institution":"UMR IRD-IFREMER-Université de Montpellier-CNRS","correspondingAuthor":false,"prefix":"","firstName":"Vinicius","middleName":"","lastName":"Robert","suffix":""},{"id":511519115,"identity":"ae86e1fa-bcfa-4d3f-85f6-97201e32f682","order_by":10,"name":"Camille Mazé","email":"","orcid":"","institution":"UMR 7048 CNRS-Sciences, Paris-IRN APOLIMER (CNRS INSHS)","correspondingAuthor":false,"prefix":"","firstName":"Camille","middleName":"","lastName":"Mazé","suffix":""},{"id":511519116,"identity":"a74709ed-8d1e-48e0-99ac-06cce6d75d5d","order_by":11,"name":"Paul Tixier","email":"","orcid":"","institution":"UMR IRD-IFREMER-Université de Montpellier-CNRS","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Tixier","suffix":""}],"badges":[],"createdAt":"2025-06-30 09:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7009043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7009043/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10531-026-03312-0","type":"published","date":"2026-03-30T15:59:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91080510,"identity":"3e8788b8-c7cd-4753-b3e0-48b842a1adbc","added_by":"auto","created_at":"2025-09-11 11:37:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2923141,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area in the southern Indian Ocean, showing bathymetry (in meters) and areas for which fishing data were available with black cells (a), sperm whale occurrence (b), Crozet type killer whale occurrence (c) and Type D killer whale occurrence (d) around fishing vessels targeting Patagonian toothfish. The dashed lines represent the EEZs delimitations and areas beyond these are international waters under the SIOFA and CCAMLR regulations\u003c/p\u003e","description":"","filename":"Figure1.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/ffb3ba765e24d34df5c1f7f5.jpg"},{"id":91080507,"identity":"c1158af1-61c1-498a-8eda-73b0ca2bf8ea","added_by":"auto","created_at":"2025-09-11 11:37:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3632771,"visible":true,"origin":"","legend":"\u003cp\u003eRelative importance of the ten most important covariates identified in the GAMs for each predator (sperm whales in purple; Crozet type killer whales in red; Type D killer whales in yellow and killer whales as a whole (regardless of the ecotype) in green) and each response variable. The vertical axis lists the model terms, and the horizontal axis shows their corresponding test statistic (Chi-squared or F-value)\u003c/p\u003e","description":"","filename":"Figure2.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/42be81bc488f4532b35b10fb.jpg"},{"id":91080508,"identity":"9c496be7-6a58-48fd-98d0-03e804073466","added_by":"auto","created_at":"2025-09-11 11:37:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4028379,"visible":true,"origin":"","legend":"\u003cp\u003eMean predicted probabilities of sperm whale depredation occurrence (a), predicted number of depredating sperm whales (b) and time elapsed before sperm whale depredation occurred in a given patch (c), in the Patagonian toothfish fishery over the 2010-2020 period where fishing data were available. Maps of standard errors associated with predictions are presented on the right side\u003c/p\u003e","description":"","filename":"Figure3.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/d4be0c670fa12b0e9056d08a.jpg"},{"id":91081693,"identity":"30c3b6f4-04c3-4e7a-af39-6baa1a83b311","added_by":"auto","created_at":"2025-09-11 11:45:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3608944,"visible":true,"origin":"","legend":"\u003cp\u003eMean predicted probabilities of sperm whale depredation occurrence over the 2010-2020 period across the Crozet and Kerguelen EEZs averaged over seasons: summer (December-February), autumn (March-May), winter (June-August) and spring (September-November)\u003c/p\u003e","description":"","filename":"Figure4.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/82b9d4a0a24e1fb0a081e53a.jpg"},{"id":91080511,"identity":"c48b48dd-771c-4f75-9213-e9367f738420","added_by":"auto","created_at":"2025-09-11 11:37:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2397965,"visible":true,"origin":"","legend":"\u003cp\u003eMean predicted probabilities of Crozet type killer whale depredation occurrence (a) and predicted number of depredating killer whales (b), in the Patagonian toothfish fishery over the 2010-2020 period across the Crozet area, including both the EEZ and adjacent international waters to the west (i.e., the Del Cano Rise), where fishing data were available. Maps of standard errors associated with predictions are presented on the right side\u003c/p\u003e","description":"","filename":"Figure5.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/f3a144c83bac8c8e73f4991f.jpg"},{"id":91082274,"identity":"45f24676-4de6-4e59-9f90-f86189aa3331","added_by":"auto","created_at":"2025-09-11 11:53:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2390789,"visible":true,"origin":"","legend":"\u003cp\u003eMean predicted probabilities of Type D killer whale depredation occurrence (a) and predicted number of killer whales involved in depredation (b), in the Patagonian toothfish fishery over the 2010-2020 period across the Crozet area, including both the EEZ and adjacent international waters to the west (i.e., the Del Cano Rise), where fishing data were available. Maps of standard errors associated with predictions are presented on the right side\u003c/p\u003e","description":"","filename":"Figure6.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/3d5d98577e84b5346a95dffa.jpg"},{"id":91083323,"identity":"d83d32ad-ed3a-45a2-b4ef-509b5e568e43","added_by":"auto","created_at":"2025-09-11 12:01:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1329816,"visible":true,"origin":"","legend":"\u003cp\u003eMean predicted time elapsed before killer whale (regardless of the ecotype) depredation occurred in a given patch, in the Patagonian toothfish fishery over the 2010-2020 period across the Crozet area, including both the EEZ and adjacent international waters to the west (i.e., the Del Cano Rise), where fishing data were available. Map of standard errors associated with predictions is presented on the right side\u003c/p\u003e","description":"","filename":"Figure7.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/fef9b406a14ade1ab2c380d0.jpg"},{"id":106343744,"identity":"e31497fd-f217-4244-967d-2095945384b8","added_by":"auto","created_at":"2026-04-07 16:08:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21750862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/fc9345ad-6e21-41f0-a321-b2de462fd2ce.pdf"},{"id":91080522,"identity":"d9013ed8-812e-4e84-9526-c6f4ab9d84d0","added_by":"auto","created_at":"2025-09-11 11:37:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":9745352,"visible":true,"origin":"","legend":"","description":"","filename":"MollieretalDepredationdriversTAAFSuppmat.docx","url":"https://assets-eu.researchsquare.com/files/rs-7009043/v1/c6bc3d0b7816c522ceff377f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting interactions of sperm and killer whales with industrial fisheries in the Southern Ocean: a spatiotemporal modelling approach for conflict mitigation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past sixty years, the increasing exploitation of marine living resources associated with major changes in fishing techniques have intensified conflicts between fisheries and marine top predators (Guerra, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nyhus, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tixier, Lea, et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These changes have altered the foraging behaviour of some predators, with many species of sharks and marine mammals now directly interacting with fisheries to feed on fish caught on fishing gear (Aug\u0026eacute; et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Donoghue et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Hamer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kaschner \u0026amp; Pauly, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Northridge, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Read, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This behaviour, termed \u0026ldquo;depredation\u0026rdquo;, has recently emerged as leading to a major global human-wildlife conflict, affecting marine socio-ecological systems at multiple levels (Mitchell et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Read, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Depredation has indeed been reported worldwide, affecting a wide range of fishing gear with negative impacts on fishing activities, fish stocks and marine predators (Clavareau et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gilman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hamer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mitchell et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Trijoulet et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong depredating species, marine mammals have been reported as the taxa with the broadest range, and their interactions with fisheries often result in significant socio-economic and ecological issues (Gilman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Tixier, Lea, et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The socio-economic cost of depredation to fisheries can be both direct, such as catch loss, and indirect, including additional fishing time, fuel consumption and payroll needed to complete fishing quotas, as along with the implementation of mitigation strategies to avoid predators (Maccarrone et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Peterson et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Marine mammal depredation may intensify pressure on already exploited fish populations, as the amount of depredated fish is often unreported and therefore not accounted for in fish stock assessments and quota allocation processes (Gasco et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hanselman et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Peterson \u0026amp; Hanselman, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Depredation can also threaten marine mammal populations through increased risks of by-catch on fishing gear, potential lethal retaliation from fishers operating illegally or underreported (Cosgrove et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Forney et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Guinet et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and habituation to human-provided food sources disrupting their natural energy balance (Esteban et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tixier, Authier, et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, despite these socio-ecological impacts, the integration of marine mammal depredation into fisheries and wildlife management through ecosystem-based approaches, and the development of effective mitigation solutions, have remained limited (Clavareau et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gu\u0026eacute;nette et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Morissette et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Trites et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAttempts to mitigate depredation involve both technological and behavioural approaches. The technological approach, including using devices aimed at deterring predators from the fishing gear or protecting the fish on hooks, has showed limited effectiveness (Hamer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mooney et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; O\u0026rsquo;Connell et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tixier, Gasco, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tixier, Burch, et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The behavioural approach focuses on changing fishing practices to avoid depredation. It is often easier to implement and remains more effective than the technological approach (Straley et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tixier, Vacquie Garcia, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This approach seeks to better understand the drivers of depredating species ecology, distribution and behaviour in response to human activities to improve predictions of the occurrence of depredation (Abade et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gastineau et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Linnell et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Such predictions can be achieved using predictive spatial models, which establish relationships between ecological variables and spatial patterns, and are widely used to inform wildlife management strategies (Guisan \u0026amp; Zimmermann, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These models can produce risk maps that forecast where human-wildlife conflicts are likely to occur, providing valuable guidance for targeted management actions to reduce depredation (Edge et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Goljani Amirkhiz et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miller et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Treves et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Predictive spatial models have already been used to identify times of the year and/or areas of low natural presence of marine predators and, thus, of low probability of depredation (Fader et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Janc et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mollier et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Straley et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tixier et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe spatio-temporal occurrence of depredation can be determined by factors related to both the ecology and behaviour of marine predators (hereafter \u0026ldquo;environmental\u0026rdquo; factors), and the behaviour of fishing vessels (hereafter \u0026ldquo;operational\u0026rdquo; factors). Previous studies have showed that bio-physical and temporal factors significantly affect the variability of interaction rates between marine mammals and fisheries (Cruz et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hernandez-Milian et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mollier et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Monaghan et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These factors define the ecological fingerprint of the depredating species (e.g., geographical range, seasonal occurrence, prey distribution). Fishing vessels within those ecological boundaries are more likely to experience high depredation (Cruz et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Goetz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hernandez-Milian et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Peterson et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This is likely because depredation offers an energetically efficient foraging strategy, as hunting dead, injured or immobilised prey requires low effort (Hamer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As such, depredation is likely when spatio-temporal overlap between fisheries and marine mammals occurs. Fishing activities can both facilitate access for marine mammals to prey they naturally consume or can provide them with access to new prey (Tixier, Gim\u0026eacute;nez, et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, it has also been observed that marine mammals can actively search for and follow fishing vessels, potentially even beyond their usual natural distribution range (Janc et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Towers et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Operational factors including fisher\u0026rsquo;s decisions about where and when to fish (Stepanuk et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and how to use their gear when fishing can also influence the extent of opportunities for marine mammals to depredate (Fader et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Janc et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mollier et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tixier, Vacquie Garcia, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn subantarctic waters, the expansion of industrial demersal longlining (lines bearing thousands of baited hooks deployed on the seafloor) targeting Patagonian toothfish (\u003cem\u003eDissostichus eleginoides\u003c/em\u003e) in the 1990s was concomitant with sperm whale (\u003cem\u003ePhyseter macrocephalus\u003c/em\u003e) and killer whale (\u003cem\u003eOrcinus orca\u003c/em\u003e) depredation in most fisheries (Bestley et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), from southern Chile (Hucke-Gaete et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) to the southern Indian Ocean (Tixier, Burch, et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The longline fishery operating off the Crozet and Kerguelen Islands is operated by a fleet of eight commercial vessels. It is managed by the Terres Australes et Antarctiques Fran\u0026ccedil;aises (TAAF) administration, under the Convention for the Conservation of Antarctic Marine Living resources (CCAMLR), with a systematic presence of fishery observers monitoring and collecting data on 100% of fishing operations since the 2000s (Gasco et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gasco \u0026amp; Tixier, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pruvost et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This fishery has been identified as one of the most impacted by such depredation with sperm whales remove up to 247 tonnes per year at Kerguelen, and killer whales up to 179 tonnes of toothfish from longlines per year at Crozet (Tixier et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Together, sperm and killer whales depredate an amount equivalent to 30% of the total catch at Crozet and 6% at Kerguelen, representing \u003cspan\u003e$\u003c/span\u003e4 M USD worth of fish depredated every year (Tixier et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe sperm whales depredating on toothfish catches around Crozet and Kerguelen, like in high latitudes of both hemispheres, are mostly adult or subadult males using these areas as foraging grounds through spring to autumn (Gasco et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Janc et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Labadie et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Roche et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tixier, Welsford, et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Their seasonal presence can be attributed to reproduction, with males migrating in winter to warmer waters to breed (Best, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Jaquet, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Mellinger et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Whitehead, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wong \u0026amp; Whitehead, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Beyond this basic understanding of their movement, no in-depth analysis on the drivers of their spatial distribution is currently available in the southern Indian Ocean. In subantarctic waters, sperm whales have been reported to feed on both cephalopods and fish, with toothfish having been confirmed as part of their natural diet (Nemoto et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Tixier, Gim\u0026eacute;nez, et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While sperm whale distribution in high latitudes was found to be driven by oceanographic features such as slopes, eddies and fronts (Jaquet et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Jaquet \u0026amp; Whitehead, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Straley et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Whitehead et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), there is a lack of evidence for an overlap between toothfish fisheries and sperm whale occurrence (Goetz et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hucke-Gaete et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Purves et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Roche et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTwo morphologically, genetically and ecologically distinct ecotypes of killer whales are known to depredate on toothfish catches around Crozet and Kerguelen (Amelot et al., 2022; Foote et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Guinet \u0026amp; Tixier, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tixier, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tixier et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The first, referred to as the \u0026ldquo;Crozet type,\u0026rdquo; is frequently observed in both inshore and offshore waters and has been monitored for over 40 years (Guinet, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Guinet et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Guinet \u0026amp; Tixier, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is generalist in its prey preferences with a diet including seals, whales, penguins, and fish (including toothfish) (Guinet, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Tixier, Gim\u0026eacute;nez, et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The second ecotype, known as \u0026ldquo;Type D\u0026rdquo;, is encountered more sporadically encountered than the Crozet type, has only been observed in offshore waters (Pitman et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and its natural diet remains unknown (Tixier et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Sperm and killer whale populations in this region experienced sharp declines due to historical pressures; sperm whales were commercially exploited in subantarctic waters until the early 1980s (Trathan \u0026amp; Reid, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), while killer whales suffered from lethal retaliation by illegal or unregulated vessels in response to depredation in the 1990s (Poncelet et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tixier et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In response to these declines, strong conservation measures have been implemented for the recovery of both species. These include the creation of the Southern Ocean Sanctuary by the International Whaling Commission in 1994 and the reduction of Illegal Unreported and Unregulated (IUU) fishing (Constable et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; \u0026Ouml;sterblom \u0026amp; Bodin, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zacharias et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we used 11 years of fishing data to generate spatio-temporal models that predict the occurrence of sperm and killer whale depredation on toothfish catches of industrial longline fisheries in the southern Indian Ocean. The predictions allowed us to i) assess the environmental and operational factors influencing both the probability and the severity of depredation, and ii) identify times of the year and areas that exhibit high risk of whale depredation. This modelling approach aimed at improving our understanding of the natural distribution of sperm and killer whales in relation to fishing activities and, therefore, informed spatially explicit strategies for mitigating human-wildlife conflict, thus reducing socio-economic losses for fisheries while supporting the conservation of vulnerable cetacean populations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study area and data collection\u003c/h2\u003e\u003cp\u003eDuring the study period (1 January 2010\u0026ndash;31 December 2020), seven commercial fishing vessels targeting Patagonian toothfish were authorized to operate in the Exclusive Economic Zones (EEZs) surrounding Crozet (44\u0026deg; \u0026ndash; 48\u0026deg;S and 46\u0026deg; \u0026ndash; 54\u0026deg;W) and Kerguelen (45\u0026deg; \u0026ndash; 52\u0026deg;S and 63\u0026deg; \u0026ndash; 75\u0026deg;W), but also beyond the Crozet EEZ, in the adjacent international waters to the west (the Del Cano Rise), which are regulated under the Southern Indian Ocean Fisheries Agreement (SIOFA) and to the south, under CCAMLR regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All vessels used auto-weighted longlines that were set between two anchors at each end of the mainline, on which 7,500\u0026thinsp;\u0026plusmn;\u0026thinsp;2,500 hooks were positioned, with an individual hook every 1.2 m, constituting a single longline set. Hooks were automatically baited and dropped to the bottom at depths ranging from 500 to 2,300 m (i.e., legal depth range to avoid the capture of juvenile toothfish (Collins et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Lines were set at night to avoid seabird bycatch (Cherel et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Weimerskirch et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Fishing trips usually lasted three months and were undertaken all year round in the Crozet EEZ, and all year round except between 1st February to mid-March in the Kerguelen EEZ to comply with seabirds conservation measures (CCAMLR, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eData on fishing effort, catch and sperm and killer whale presence around the vessel were collected by fisheries observers for 100% of longline sets within the Crozet and Kerguelen EEZs and adjacent international waters, and subsequently retrieved from the PECHEKER database, provided by the Mus\u0026eacute;um National d\u0026rsquo;Histoire Naturelle de Paris (MNHN) (Martin et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For each longline set deployed from a given vessel, time and position of setting (gear deployment), hauling (gear retrieval) operations, the number of hooks and the total weight and number of toothfish were recorded, as well as the occurrence of whale depredation. The presence of sperm whales and killer whales was recorded visually from the surface during hauling only and through a 3-state information: \u0026ldquo;presence\u0026rdquo; if surface observation effort was provided and whales were sighted with an assumed depredation behaviour (whales are typically observed repeatedly surfacing in the vicinity of the vessel, performing long dives towards the longline, being surrounded by seabirds and leaving slicks of fish oil at the surface); \u0026ldquo;absence\u0026rdquo; if observation effort was provided and no whales were sighted from the vessel; and \u0026ldquo;NA\u0026rdquo; if observations were not possible due to weather, sea state and/or visibility conditions. The latter were excluded from the analysis. For sets where presence was recorded during hauling, fishery observers provided minimum and maximum estimates of the number of whales present around the vessel.\u003c/p\u003e\u003cp\u003eCrozet type and Type D killer whales were differentiated using photographs taken by fishery observers as part of the photo-identification monitoring program implemented on fishing vessels since 2003 around Crozet and Kerguelen (Tixier, Gasco, et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The two ecotypes can be reliably distinguished during surface encounters and from photographs collected during fishing operations based on visible morphological features. Type D killer whales are characterised by an extremely small postocular eye patch, a bulbous forehead, and a narrow dorsal fin with a sharply pointed tip (Pitman et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tixier et al., 2025). In contrast, Crozet type killer whales exhibit a more typical morphology similar to Antarctic type A killer whales, with a larger and elongated eye patch, and a more streamlined head profile (Pitman et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pitman \u0026amp; Ensor, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tixier, Gasco, et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Environmental and operational variables\u003c/h2\u003e\u003cp\u003eThe influence of environmental factors on the occurrence of depredation was examined using 13 variables known to influence the distribution of large marine predators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Lerebourg et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Praca et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Virgili et al., \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These included both static (bathymetry, slope, distance to coast and distance to the nearest seamount) and dynamic (temperature, currents, eddy kinetic energy, chlorophyll-a concentration, sea surface height (SSH), salinity, catch per unit of effort (CPUE) and fish size) variables. Bathymetry was extracted from the GEBCO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://download.gebco.net/\u003c/span\u003e\u003cspan address=\"https://download.gebco.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and was used to calculate the slope (in degrees) using the \u0026ldquo;terrain\u0026rdquo; function from the \u0026ldquo;raster\u0026rdquo; package in R (Hijmans \u0026amp; van Etten, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The distance to the nearest seamount was derived from the geomorphology of the oceans database (Harris et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and the distance to the coast was calculated from coastline position. Due to the relatively deep-diving capacities of the species involved in depredation, and the fact that depredation on longline sets of the Crozet and Kerguelen fishery mainly occurs at depth, we extracted dynamic ocean variables at various depths (the surface, between 0 and 200 m, between 200 m and 600 m and between 600 m and 2,000 m) from the Global Ocean Reanalysis (GLOBAL_MULTIYEAR_PHY_001_030 for water temperature, current velocity, sea surface height and salinity) and Global Ocean Biogeochemistry hindcast (GLOBAL_MULTIYEAR_BGC_001_029 for chlorophyll-a concentration). Both these products are provided by Copernicus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.marine.copernicus.eu/products\u003c/span\u003e\u003cspan address=\"https://data.marine.copernicus.eu/products\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The eddy kinetic energy (EKE) was calculated from the current velocity as follows: EKE\u0026thinsp;=\u0026thinsp;0.5*(U2\u0026thinsp;+\u0026thinsp;V2), where U and V are the two current components. The CPUE was calculated as the mean number and weight of toothfish per hook, per 0.1\u0026deg; cell, and per month, using only sets without depredation. The ratio between the total weight and the number of fish caught per set was also calculated as a monthly mean per cell to reflect fish size.\u003c/p\u003e\u003cp\u003eThe influence of operational factors on the occurrence of depredation was examined using 6 variables that have been shown to affect depredation on longline catch in other regions: the spatial density of vessels operating simultaneously, the soaking time, the number of hooks on longline sets, the time spent by a vessel in a fishing patch (defined as a series of longline sets hauled successively within a 70 km range from one another), the distance from previous set, and the occurrence of depredation on the longline sets previously hauled by the same vessel during the same trip (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Tixier, Vacquie Garcia, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Janc et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fader et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These variables were all extracted from the PECHEKER database for specific positions and dates, which were also used as covariates through the latitude and longitude of the sets\u0026rsquo; median point and the month. The density of vessels operating simultaneously was calculated as the number of vessels that hauled longline sets within 200 km and \u0026plusmn;\u0026thinsp;3 days of the observed longline set. The number of hooks on longline sets was the total number of hooks hauled. Soaking time was calculated as the time (in hours) between the time the last hook of a longline set was deployed and the time the last hook was hauled.\u003c/p\u003e\u003cp\u003eFor the dynamic environmental variables, data were extracted for the period spanning from January 1st 2010 to December 31st 2020 with a monthly temporal resolution, and for the four depth layers. For each depth layer and variable, monthly averages and standard deviations were calculated over the 11-year period (2010\u0026ndash;2020) to assess inter-annual variability. Static variables were extracted at a horizontal resolution of 0.004\u0026deg;, and dynamic variables at a resolution of 0.08\u0026deg; (except for chlorophyll-a at 0.25\u0026deg; and toothfish CPUE and size at 0.1\u0026deg;). For the analyses, all variables were re-scaled to a horizontal resolution of 0.1\u0026deg; (i.e., spatial cells of approximately 10 x 10 km). The values of these variables were assigned to each longline set based on the date and position at which it was hauled.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEnvironmental, operational, and spatio-temporal variables used for modelling sperm and killer whale depredation on the catch of the Patagonian toothfish fishery operating across the study area.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHorizontal resolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTemporal resolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBathymetry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebathymetry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGEBCO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eslope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDerived from bathymetry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the nearest seamount\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enearest_seamount_distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ekm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDerived from seamounts position database (Harris et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to the coast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecoast_dist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ekm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDerived from coast position\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etemp_mean_surf\u003c/p\u003e\u003cp\u003etemp_mean_0.200m\u003c/p\u003e\u003cp\u003etemps_mean_200.600m\u003c/p\u003e\u003cp\u003etemps_mean_600.2000m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCOPERNICUS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecurrent_mean_surf\u003c/p\u003e\u003cp\u003ecurrent_mean_0.200m\u003c/p\u003e\u003cp\u003ecurrent_mean_200.600m\u003c/p\u003e\u003cp\u003ecurrent_mean_600.2000m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003em\u003csup\u003e2\u003c/sup\u003e.s\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCOPERNICUS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEKE \u0026ndash; Eddy Kinetic Energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eeke_mean_surf\u003c/p\u003e\u003cp\u003eeke_mean_0.200m\u003c/p\u003e\u003cp\u003eeke_mean_200.600m\u003c/p\u003e\u003cp\u003eeke_mean_600.2000m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003em\u003csup\u003e2\u003c/sup\u003e.s\u003csup\u003e-2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCOPERNICUS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChlorophyll-a concentration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echl_mean_surf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emg.m\u003csup\u003e-3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCOPERNICUS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSea surface height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCOPERNICUS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSalinity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esal_mean_surf\u003c/p\u003e\u003cp\u003esal_mean_0.200m\u003c/p\u003e\u003cp\u003esal_mean_200.600m\u003c/p\u003e\u003cp\u003esal_mean_600.2000m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCOPERNICUS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVessel density (number of all vessels within 200km\u0026thinsp;\u0026plusmn;\u0026thinsp;3 days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoint data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoaking time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esoak_time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoint data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of hooks set\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehook_set\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoint data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of depredation on previous set of same vessel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esw_previous\u003c/p\u003e\u003cp\u003ekw_previous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoint data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from previous set\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edistance_from_prev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoint data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime spent in a fishing patch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etime_spent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPUE in nb of fish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emean_cpue_nb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003enb fish / hooks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPUE in weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emean_cpue_weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ekg / hooks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRatio weight/nb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emean_ratio_weight_nb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ekg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMONTH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emonth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMonthly (2010\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elon_mid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoint data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003elat_mid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoint data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePECHEKER\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Model selection\u003c/h2\u003e\u003cp\u003eDepredation by sperm and killer whales was modelled in relation to the 13 environmental, 6 operational and 3 spatio-temporal covariates described above, through 3 response variables, using Generalized Additive Models (GAMs; Hastie and Tibshirani, 1986). Models were fitted using the \u0026ldquo;gam\u0026rdquo; function from the \"mgcv\" package in R (Wood, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), with different distributions specified according to the nature of the response variable. The first response variable was the occurrence of depredation (i.e., presence or absence on longline sets) and was modelled using a binomial distribution with a logit link function, with separate models fitted for sperm whales, for killer whales as a whole (regardless of the ecotype), and for each killer whale ecotype (Crozet type and Type D). The second response variable was the number of individuals depredating on the same longline set (using the median of the minimum and maximum estimates provided by fishery observers, for either sperm whales or killer whales). This variable was modelled only for sets where depredation by at least one individual occurred, using a negative binomial distribution to account for overdispersion. The third response variable was the time elapsed before sperm whale and/or killer whale depredation occurred after fishing vessels started operating in a given patch, as described before. It was modelled using a Tweedie distribution, which is appropriate for continuous, positive, and zero-inflated data. Models for killer whales used data from within the Crozet area only given that the species rarely depredate in other areas.\u003c/p\u003e\u003cp\u003eGAMs are semi-parametric regression techniques that incorporate smooth functions to flexibly capture non-linear and non-monotonic relationships between a response variable and its predictors (Wood, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). GAMs were developed using the Restricted Maximum Likelihood method. Smoothed explanatory variables were modelled with penalised thin-plate regression splines with a limited basis size of 4 to prevent overfitting (Wood, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For the month variable, a cyclic cubic spline was used to account for the circularity of the annual cycle. For each species and ecotype, GAMs were ranked based on Akaike information criterion (AIC) scores (Akaike, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Burnham \u0026amp; Anderson, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Symonds \u0026amp; Moussalli, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and a backward stepwise procedure was used for variable selection, considering a p-value of 0.05 as the threshold for excluding non-significant covariates. As collinearity between explanatory variables is known to affect the stability of a model (Dormann et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Spearman coefficients were calculated between each pair of variables. Variables with coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0.7 were removed to avoid excessive multicollinearity and to retain ecologically relevant variables in the model (Zuur et al., \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dormann et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Braunisch et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Evaluation and predictions\u003c/h2\u003e\u003cp\u003eModels were run with a 10-fold cross-validation, blocked by year to account for temporal structure (Roberts et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Test-train splits were generated, where each split selected 20% of the data for model evaluation (testing data), and 80% of the data for model fitting (training data). Model performance on the training data was quantified by calculating the percentage of deviance explained. External predictive performance was evaluated on the testing data. Predictive accuracy was assessed by computing the root of mean square error (RMSE) between observed and predicted values in the testing dataset (Brodie et al., 2021). Model goodness of fit was further assessed using the percentage of mean absolute error (PMAE), calculated as the mean absolute error divided by the mean observed value, where a PMAE\u0026thinsp;\u0026gt;\u0026thinsp;100% indicates poor model fit (i.e., errors on average larger than observed values). For binomial models, predictive discrimination capacity was assessed by computing the Area Under the Receiver Operating Characteristic Curve (AUC), which quantifies the ability of the model to correctly discriminate between presence and absence across the full range of threshold values (Swets, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Functional response plots were generated for all significant covariates (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for each species-specific model (sperm whales, Crozet type killer whales, and Type D killer whales), as well as for the model using killer whales as a whole (regardless of the ecotype) and for each response variable modelled.\u003c/p\u003e\u003cp\u003eThe selected models were used to predict the spatial probability of depredation occurrence, the number of individuals depredating on the same longline set and the time elapsed before depredation occurred in a given patch. Models were runacross the entire study area for sperm whales and within the Crozet area only for killer whales. Predictions were made on a 10 x 10 km resolution grid, using the \u0026ldquo;predict\u0026rdquo; function of the \u0026ldquo;mgcv\u0026rdquo; R package (Wood, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Prediction maps were produced using the monthly grids of environmental variables. New spatial grids were created for the vessel density and the mean CPUE (both in weight and number), calculated over a 0.1\u0026deg; x 0.1\u0026deg; spatial grid. The mean ratio between weight and number of toothfish was also calculated over the same grid and mapped by season (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Other operational variables were set to their median values in the predict function and the occurrence of sperm whale or killer whale depredation on the longline set previously hauled by a given vessel was fixed at 0. Only the areas where fishing data were available were represented in the prediction maps. Monthly predictions were averaged over the entire study period (2010\u0026ndash;2020) and the mean standard error of predictions was reported as a metric of uncertainty. Predictions were also averaged over seasons: summer (December-February), autumn (March-May), winter (June-August) and spring (September-November). Throughout the manuscript, predicted mean values are presented along with their associated standard errors, with minimum and maximum observed values in parentheses.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eData were analysed from a total of 41,671 longline sets (including sets with missing depredation data; 4, 953 sets for sperm whales and 2, 933 sets for killer whales) deployed by 7 vessels between 2010 and 2020 in the study area. Overall, out of the 41,671 sets, 15,107 were subject to depredation by sperm whales (36.3%), 3,811 sets were subject to depredation by killer whales (9.1%), including 1,102 by Crozet type killer whales (2.6%) and 196 by Type D killer whales (0.5%), and 1,943 sets (4.7%) were subject to depredation by both sperm and killer whales. In contrast, 21,613 sets showed no depredation by sperm whales, while killer whales were not observed in 34,929 sets. Out of the 29,911 sets deployed in the Kerguelen EEZ, 10,148 were subject to depredation by sperm whales (33.9%) and 56 by killer whales (0.2%). For the Crozet EEZ, a total of 10,729 sets were deployed with 4,840 sets that were depredated by sperm whales (45.1%), 3,695 sets that were depredated by killer whales (34.4%; 1,091 by Crozet type and 181 by Type D killer whales) and 1,942 sets depredated by both sperm and killer whales (18.1%). For the remaining 2,423 sets, the type of killer whale involved in depredation could not be identified using the photo-identification database.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Models selection and drivers of depredation\u003c/h2\u003e\u003cp\u003eAfter checking for correlations between variables (Figure S2), the models selected based on AIC and REML indicated that the variables best explaining the occurrence of depredation, the number of individuals depredating on the same longline set, and the time elapsed before depredation occurred in a given patch differed between species and ecotypes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From the binomial models, the occurrence of depredation by sperm whales and killer whales as a whole (regardless of the ecotype) was primarily explained by spatio-temporal interactions (latitude, longitude, month), environmental and oceanographic features (e.g., SSH, salinity, current speed, EKE), fishing effort metrics (e.g., soaking time, toothfish CPUE, toothfish size, density of vessels, time spent in fishing patches), seabed features (e.g., distance to coast and seamounts, slope, bathymetry) and the occurrence of depredation on the set previously hauled by the vessel, with some variation in the relative importance of key predictors across taxa (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S3, S8, S14, S20). From the models with negative binomial distribution outputs, the number of individuals depredating on the same longline set was influenced across all taxa by the interaction between latitude, longitude and month, and by the number of hooks set (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S4, S10, S16, S23). For sperm whales, the number of individuals depredating on the same longline set was additionally influenced by toothfish CPUE and size, bathymetry, soaking time, distance to coast and seamounts and time spent in fishing patches (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S4). The model fitted to killer whales as a whole showed a strong influence of distance to the coast and seamounts, temperature at 600\u0026ndash;2,000 m depth, current speed, slope, SSH, and soaking time, similar to the model fitted to Crozet type killer whales only (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S10, S23). However, the number of Type D killer whales depredating on the same longline set was primarily associated with salinity at 600\u0026ndash;2,000m depth and distance to seamounts (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S16). For sperm whales, the time elapsed before depredation occurred in a given patch was mainly influenced by toothfish CPUE, toothfish size, soaking time, slope, SSH, latitude/longitude, and number of hooks set (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S7). For killer whales, the main predictors were the distance to coast, month, toothfish size, number of hooks set, currents at 600\u0026ndash;2,000m depth and toothfish CPUE (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S12, S18, S25).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAcross the 10-fold cross-validation, the mean percentage of deviance explained by the final models for sperm whales ranged from 15.8\u0026ndash;35.9%, with the highest value observed for the binomial model and the lowest for the negative binomial model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The binomial model had better performance metrics than the others, with a lower RMSE (0.4) and PMAE (26.9%) compared to the negative binomial (RMSE: 1.7; PMAE: 45.8%) and Tweedie models (RMSE: 2.4; PMAE: 69.8%). For killer whales, the mean percentage of deviance explained ranged from 18.6% (negative binomial) to 40.1% (Tweedie), with the binomial model having a lower RMSE (0.4) and PMAE (34.0%) than the other models. The binomial models for Crozet type and Type D killer whales showed higher performance, with RMSE and PMAE values of 0.3 and 19.2% for Crozet type, and 0.1 and 4.2% for Type D. The mean percentage of deviance explained ranged from 21% (negative binomial) to 24.4% (binomial) for Crozet type, and from 20.3% (binomial) to 44.8% (Tweedie) for Type D killer whales. AUC values were high across all taxa for binomial models: 0.89 for sperm whales, 0.79 for killer whales as a whole, 0.81 for Crozet type killer whales, and 0.76 for Type D killer whales. Due to high PMAE values for the Crozet type (140.3%) and Type D killer whales (147.6%) Tweedie models, only results from the Tweedie model fitted to the time elapsed before depredation by killer whales as a whole occurred after fishing vessels started operating in a given patch are reported in this study.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of selected models for each taxon and each response variable. P-values are reported for all covariates (- correspond to non-significant covariates with a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 dropped from the models). Dev. Exp: Percentage of deviance explained, indicating model explanatory power. RMSE: Root of Mean Square Error, where lower values indicate better model fit. PMAE: Percentage Mean Absolute Error, indicating model accuracy (indicative values: PMAE\u0026thinsp;\u0026gt;\u0026thinsp;100% = poor; 50\u0026ndash;100% = fair to good; \u0026lt;50% = excellent). AUC: Area Under the ROC Curve (only for binomial models), measuring discriminatory power (values: 0.5\u0026thinsp;=\u0026thinsp;random; 0.7\u0026ndash;0.8\u0026thinsp;=\u0026thinsp;acceptable; 0.8\u0026ndash;0.9\u0026thinsp;=\u0026thinsp;excellent; \u0026gt;0.9\u0026thinsp;=\u0026thinsp;outstanding). Environmental covariates: TEMP: temperature; CHL: chlorophyll-a concentration; SAL: salinity; CURR: currents velocity; EKE: eddy kinetic energy; SSH: sea surface height; CPUE: catch per unit of effort. Blank cells indicate that the covariate was not included in the model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies/type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eSperm whales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003eKW Crozet type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eKW Type D\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u003cp\u003eKW (both ecotypes)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResp. var.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOccurrence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNb indiv.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTime before depredation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOccurrence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNb indiv.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTime before depredation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOccurrence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNb indiv.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTime before depredation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eOccurrence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eNb indiv.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003eTime before depredation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStat. distrib.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBinomial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNegative binomial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTweedie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBinomial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNegative binomial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTweedie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eBinomial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNegative binomial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTweedie\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eBinomial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eNegative binomial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003eTweedie\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDev. Exp (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e24.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e44.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e24.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e18.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e40.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRMSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePMAE (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e140.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e37.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e147.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e34.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e35.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e65.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSLOPE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eTEMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esurf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0-200m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esurf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0-200m\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200-600m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e600-2000m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEKE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esurf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e600-2000m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDIST. COAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDIST. SEAMOUNT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eFish abundance covariates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCPUE NB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCPUE PDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eRATIO PDS NB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTemporal covariate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMONTH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eOperational covariates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eVESSEL DENSITY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSOAK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNB HOOKS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTIME SPENT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDIST PREV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDEP PREV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSpatial covariates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLON\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eLAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Predictions of sperm whale depredation hotspots\u003c/h2\u003e\u003cp\u003eFor spatial predictions from the final models, the operational variables included in the models were fixed at a mean value (hook_set\u0026thinsp;=\u0026thinsp;7,500; soak\u0026thinsp;=\u0026thinsp;28) and a median value of distance from the set previously hauled by a given vessel. Two different values of time spent in the same fishing patch (24 hours and 100 hours) were tested to account for spatial autocorrelation. The mean predicted probability of sperm whale depredation occurrence was high (P(depredation)\u0026thinsp;=\u0026thinsp;0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05) throughout the study area where fishing data were available (Crozet, P(depredation)\u0026thinsp;=\u0026thinsp;0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 (0.18\u0026ndash;1); Kerguelen EEZ, P(depredation)\u0026thinsp;=\u0026thinsp;0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 (0.03\u0026ndash;1)). The standard error of predictions ranged from 0 to 0.23 throughout the prediction area, and was high (SE\u0026thinsp;\u0026gt;\u0026thinsp;0.15) in the northern part of Crozet and the Del Cano Rise. When depredation by sperm whales occurred, the mean predicted number of depredating individuals was 2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 (1\u0026ndash;11) in the Crozet area and 3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 (1\u0026ndash;19) in the Kerguelen EEZ. The mean predicted time elapsed before sperm whale depredation occurred in a given patch was 4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 days (0.9\u0026ndash;11 days) in the Crozet area and 1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 days (0.2\u0026ndash;8 days) in the Kerguelen EEZ. High probabilities (\u0026gt;\u0026thinsp;0.75) of sperm whale depredation occurring were found in the south of Crozet with maximum probabilities in the south-west and in the western part of the Kerguelen Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Concentrations of 0.1\u0026deg; x 0.1\u0026deg; grid cells with high probabilities of depredation (\u0026gt;\u0026thinsp;0.6) were also visible in the north-western and north-eastern part of Kerguelen (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Seasonal predictions showed that the mean probability of sperm whale depredation occurring was highest in spring (P(depredation)\u0026thinsp;=\u0026thinsp;0.59) and the lowest in winter (P(depredation)\u0026thinsp;=\u0026thinsp;0.43) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) but there were more areas with a higher number of depredating individuals around Kerguelen in summer (Figure S5). When depredation occurred, the highest predicted number of depredating individuals (\u0026ge;\u0026thinsp;5 individuals) was found in the south-western and north-eastern part of Kerguelen and the southern part of Crozet (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The areas with the highest predicted numbers of depredating individuals were more prominent when the time spent in a fishing patch was 24 hours compared to 100 hours (Figure S6). In the northern and north-western parts of Kerguelen, as well as around Crozet, sperm whale depredation occurred as soon as vessels started operating in a given patch, whereas in the south-west of Kerguelen, it took between 2 and 3 days before sperm whale depredation occurred. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Predictions of killer whale depredation hotspots\u003c/h2\u003e\u003cp\u003eThe mean predicted probability for killer whale (regardless of the ecotype) depredation occurring was 0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 in the Crozet EEZ with individual values ranging from 0.04 to 0.92. For Crozet type killer whales the probability was 0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 (0.01\u0026ndash;0.68), while for Type D killer whales the mean was 0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002 (0\u0026ndash;0.07). High probabilities (\u0026gt;\u0026thinsp;0.5) of killer whale depredation occurring were found in the south-eastern part of Crozet archipelago, especially for the Crozet type (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, S21). Type D killer whales were more likely to depredate in the southern part of Crozet (P(depredation)\u0026thinsp;\u0026gt;\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). When depredation by killer whales (regardless of the ecotype) occurred, the mean predicted number of depredating individuals was 7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 (4.5\u0026ndash;12) everywhere around Crozet (Figure S24). This mean was 9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 individuals (5\u0026ndash;15) for the Crozet type and 10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 individuals (9\u0026ndash;16) for the Type D killer whales. The highest predicted numbers (\u0026gt;\u0026thinsp;10 individuals) for the Crozet type were mainly located in the south-eastern and north-western parts of the Crozet archipelago (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). For Type D (\u0026gt;\u0026thinsp;11 individuals), they were primarily in the south-eastern and northern parts of the Crozet archipelago (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Seasonal predictions showed a higher probability of depredation occurrence by killer whales, especially for Crozet type, in spring (between September and November), and a lower probability in winter (between June and August) (Figure S8). These seasonal variations were not associated with major spatial shifts in predicted depredation occurrence hotspots (Figures S9, S15, S22), nor in the number of individuals involved (Figure S11). However, the areas of high predicted number of Type D killer whales involved in depredation changed consistently over months (Figure S17). The mean predicted time elapsed before killer whale (regardless of the ecotype) depredation occurred in a given patch was 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 days (0.3\u0026ndash;3.5 days). In the eastern part of Crozet, killer whale depredation occurred as soon as vessels started operating in a given patch, whereas in the Del Cano Rise, killer whale depredation did not start until up to 3 days after vessels started operating in a given patch (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, S13, S19).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study shows that sperm and killer whale depredation on Patagonian toothfish catches of the longline fishery operating in the southern Indian Ocean varied in space and time and was influenced by both environmental and operational variables. The effectiveness of the models (high percentages of deviance explained and high predictive performances) allowed for reliable predictions of the probability of depredation to occur, the number of depredating individuals and the time elapsed before depredation occurred when vessels started operating in a given patch. The results provide insights into the drivers of the natural distribution of sperm and killer whales in the region, as well as how fishers use their gear may influence depredation. Together, these insights can inform ways to better anticipate, and thus better avoid, whale depredation.\u003c/p\u003e\u003cp\u003e\u003cem\u003eNatural distribution of sperm and killer whales\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFor sperm whales, the results indicate that depredation events are less likely to occur on sets hauled in winter, with a lower occurrence of depredation, a lower number of depredating individuals and a greater time elapsed before depredation occurred in a given patch compared to other seasons. This seasonal variation has been reported in previous studies (Janc et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Labadie et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tixier, Burch, et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tixier, Welsford, et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and was attributed to migration patterns of adult male sperm whales moving between feeding grounds in cold waters and reproduction grounds in tropical and sub-tropical waters (Jaquet et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Madsen et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mellinger et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Teloni et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, the amplitude of this seasonality was lower around Crozet than around Kerguelen, with instance model predictions showing higher probabilities of sperm whale depredation occurrence between June and November in Crozet. These differences may be explained by the smaller size of the fishing area and the higher density of depredating sperm whales in Crozet compared to Kerguelen (Labadie et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or a lower proportion of individuals leaving the foraging grounds in Crozet compared to Kerguelen. These two factors may also explain the greater spatial variation in sperm whale depredation occurrence at Kerguelen than at Crozet, with a strong latitudinal gradient identified in models showing high probabilities in the northern reaches of the Kerguelen Plateau, as previously observed by Tixier, Welsford, et al. (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSperm whale depredation was more likely to occur on sets hauled at greater depths (\u0026gt;\u0026thinsp;2,000 m), and in areas characterised by intermediate to steep bathymetric slopes (0 to 10\u0026deg;), suggesting a preference for deep and complex topographic habitats. This aligns with the known distribution of adult males along the outer edges of oceanic shelves (Whitehead, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), where steep slopes and greater depths typically support enhanced biological productivity and prey aggregation. Similar patterns have been reported in other high-latitude regions, where sperm whale presence is shaped by static oceanographic features, such as bathymetric gradients, and dynamic processes like eddies and oceanic fronts, which drive prey abundance and availability (Whitehead et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Jaquet, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Jaquet \u0026amp; Whitehead, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Jaquet et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Straley et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wong \u0026amp; Whitehead, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In our study area, the probability of sperm whale occurrence was associated with depressed sea surface height (between \u0026minus;\u0026thinsp;1.5 and 0 m), potentially indicating mesoscale activity (e.g., eddies) or fronts (Park et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) that could enhance foraging opportunities (Bestley et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, all models highlighted an association between sperm whale depredation and seamounts. Seamounts can have particular ecological significance, as they are associated with long-lasting trophic webs and enhance biological productivity, thereby providing predictable foraging hotspots for top predators (Casc\u0026atilde;o et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morato et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pitcher et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rogers, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sergi et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eToothfish richness was a strong predictor of sperm whale depredation, with the occurrence of depredation and the number of depredating individuals being positively associated with the number of fish caught per hook. Although not systematic, hotspots of sperm whale depredation overlapped with areas of high toothfish CPUE (Hucke-Gaete et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tixier et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and areas of large toothfish size. In these areas, sperm whales started depredating sooner than in other areas when vessels operated within the same patch for prolonged periods of time. For example, sperm whales were often already present before the arrival of fishing vessels in the western and northern parts of Kerguelen, which are areas characterised by high toothfish abundance, particularly of larger individuals. Studies on the spatial distribution of Patagonian toothfish showed that as they approach maturity, large fish move downslope to deep-sea habitats (from 1,200 m to \u0026gt;\u0026thinsp;2,300 m) and head towards the spawning grounds on the western side of the Kerguelen Plateau (P\u0026eacute;ron et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Welsford et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yates et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Altogether, these results support the assumption that toothfish is an important prey for sperm whales in the region, with individuals preferentially foraging on large toothfish and therefore co-occurring with fishers in areas where large individuals are predominant.\u003c/p\u003e\u003cp\u003eThe Crozet type killer whales were most likely to depredate in spring and least likely in winter. This finding contrasts with previous studies that reported a decrease in Crozet type killer whale depredation in December (Tixier et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These studies associated this decrease with prey-switching, such as to juvenile southern elephant seals (\u003cem\u003eMirounga leonina\u003c/em\u003e), which are abundant in inshore waters while breeding on the islands at this time of year (Guinet, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Guinet \u0026amp; Bouvier, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Tixier et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This difference may be explained by potential shifts in the feeding behaviour of the Crozet type killer whales, which are also known to feed on prey like recovering large whales further offshore (Guinet, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Guinet et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). A recent shift in fishing effort may also explain this difference, with more fishing vessels operating at Crozet and thus providing the whales with more opportunities to depredate in spring. The effect of year was not included in the models, as the study focused on spatial and seasonal patterns of depredation, but future work could explore interannual variability for all the types of predators. For Type D killer whales, depredation was most likely in autumn. Interestingly, the number of depredating individuals increased in winter, and the areas of high predicted numbers of depredating individuals changed consistently across seasons. These seasonal patterns may reflect aspects of the natural ecology of Type D killer whales, but given that the main prey of these killer whales is unknown, the underlying drivers of seasonal variation remain unclear.\u003c/p\u003e\u003cp\u003eThe two killer whale ecotypes differed in the spatial distribution of their depredation on toothfish catches, with the Crozet type killer whales being more likely to depredate in the south-eastern part of the Crozet archipelago and Type D killer whales being more likely to depredate in the southern part. For the Crozet type, the probability of depredation and the number of depredating individuals increased over low-slope grounds, and the probability of depredation increased with the distance from the coast. This suggests that the distribution of Crozet type killer whales is predominantly offshore, with inshore foraging grounds being used for limited periods of the year and driven by seasonal variation in prey abundance in the region (Guinet, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Guinet et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consistent with previous studies, hotspots of depredation for this ecotype were detected in shallower waters over the insular shelf (Tixier et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tixier, Vacquie Garcia, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Interestingly, the occurrence of depredation by both Crozet type and Type D killer whales was elevated near seamounts, and for Type D killer whales, many hotspots of depredation were located near seamounts in deep waters. In addition, Type D killer whale depredation correlated with salinity at 600-2,000m depth, suggesting an association with specific oceanic water masses that structure prey availability in pelagic ecosystems (McMahon et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is in line with the assumed pelagic feeding ecology of this ecotype in subantarctic waters (Pitman et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to sperm whales, the influence of toothfish CPUE on the occurrence of depredation by killer whales was limited for both Crozet type and Type D killer whales, with large toothfish not being specifically targeted. For the Crozet type killer whales, this can be explained by the fact that large toothfish was estimated contributing to only about 30% of their diet (Tixier, Gim\u0026eacute;nez, et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For Type D killer whales, this suggests that toothfish is not a primary prey item of their likely oceanic feeding ecology (Tixier et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), although the limited data and large uncertainty in the results for this ecotype preclude any strong conclusion. Spatial predictions revealed hotspots of killer whale depredation, with killer whales being rapidly present around fishing vessels when these started operating. However, the low correlation between toothfish abundance and killer whale depredation suggests that the action of fisheries, by offering opportunities to feed on high-calorie prey with limited foraging effort, may alter to some extent the natural distribution, foraging activity and prey intake for the species in the region. Accurately determining this extent is critical for ecosystem-based management of toothfish fisheries (Clavareau et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and should be supported by further research on the diet and foraging ecology of the killer whale ecotypes involved in depredation. In addition, since both sperm and killer whales have been reported to selectively depredate Patagonian toothfish, interspecific competition for the same resource may also occur when both species depredate the same longline sets simultaneously, and could be further investigated by integrating predator types within a single model.\u003c/p\u003e\u003cp\u003e\u003cem\u003eInfluence of fishing behaviour on sperm and killer whale depredation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOur results show that occurrence of depredation by both sperm and killer whales is influenced by operational factors related to fishers\u0026rsquo; behaviour, and, more specifically, the extent to which fishers provided predators with opportunities to depredate. The probability of depredation by both Crozet type and Type D killer whales increased when soaking time exceeded 25 hours. For Crozet type killer whales, both the probability of depredation occurring and the number of depredating individuals increased when more than 5,000 hooks were set per line. These results suggest that giving sperm and killer whales more time to locate the fishing gear and access the catch increases the likelihood that depredation occurs (Tixier, Vacquie Garcia, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, the patterns observed in the model outputs may reflect both predator behaviour and fishers\u0026rsquo; reactive strategies to depredation, potentially biasing the interpretation of the results. Increasing number of vessels operating simultaneously significantly decreased the probability of depredation by sperm whales and killer whales on longline sets. This is likely due to the limited number of depredating whales in the fishing area, whereby an increased number of vessels leads to a dilution effect, reducing the probability of any single vessel interacting with whales (Tixier, Vacquie Garcia, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, the cumulated depredation effects on all vessels operating simultaneously in a given area remain to be properly assessed.\u003c/p\u003e\u003cp\u003eThe time spent by vessels within a patch did not lead to an increased occurrence of depredation by either sperm whales or killer whales, suggesting that fishing vessels may not act as strong attractors, but rather that the whales were already present in fishing areas. This result supports the assumption of co-occurrence between whales and fishing activities in productive areas as discussed above. Depredation hotspots often reflect areas of high fish density, where both predators and fishers co-occur. As a result, CPUE may paradoxically be higher in these zones, despite depredation, than in areas with lower predator presence but also lower fish abundance. In line with previous studies showing that sperm and killer whales are able to actively follow vessels when they travel from one fishing ground to another (Janc et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tixier, Vacquie Garcia, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Towers et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), in this study, vessels were significantly less likely to experience depredation on subsequent longline sets if they travelled distances greater than 70 km. While further analyses using photo-identification would be required to examine the drivers of whales deciding to follow vessels at a finer scale, these results suggest that sperm and killer whales do not typically follow vessels over large distances, supporting the use of spatial displacement as a mitigation measure. Implementing a \u0026ldquo;move-on\u0026rdquo; strategy has been shown as effective in reducing odontocete depredation in other regions (Forney et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Peterson \u0026amp; Carothers, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), but this effectiveness may depend on local factors such as the size of fishing grounds, the density of sperm and killer whales, and variation in the motivation to depredate across individuals within populations (Auguin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tixier et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Towers et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eImplications for conflict mitigation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe results of this study could be used to develop operational fishing strategies that minimise sperm and killer whale depredation on longline fisheries in the southern Indian Ocean. First, improved understanding of the ecology and distribution of the whales can help fishers better anticipate depredation risks and adjust their operations to avoid areas naturally used by the whales. Second, from an operational perspective, increasing distance travelled between fishing grounds, increasing the density of vessels operating simultaneously, reducing soaking times, and shortening longline sets length, which do not impact CPUE (Tixier et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tixier, Vacquie Garcia, et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), may work as easy-to-implement mitigation measures in anticipation of, or in response to, depredation.\u003c/p\u003e\u003cp\u003eThe models developed in this study helped identify hotspots of sperm and killer whale depredation, with high probability of occurrence located mainly in the south-west and the north of Kerguelen for sperm whales, and in the south-east of Crozet for killer whales. These areas could be avoided by fishers without severely reducing their fishing success or having to travel longer distances and spend more time at sea, the latter factors being often cited as indirect costs of depredation avoidance strategies (Gilman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Peterson et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tixier, Lea, et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, alternative areas of high toothfish CPUE, though associated with smaller fish, were identified in the south-east of Kerguelen, offering fishers opportunities to maintain high catch rates while reducing the risk of depredation.\u003c/p\u003e\u003cp\u003eWhile implementing avoidance strategies may impose additional constraints on fishers (Janc et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maccarrone et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Peterson et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), comprehensive bio-socio-economic assessments of the costs and benefits associated with changes in fishing practices are necessary. For instance, the \u0026ldquo;move on\u0026rdquo; technique may result in increased non-fishing time and fuel consumption, potentially making this strategy less economically attractive or sustainable for the fishery (Richard et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To ensure profitability, these additional costs should not outweigh the benefits gained from reducing depredation (Trijoulet, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Trijoulet et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, the closure of the Kerguelen zone to reduce seabird mortality during February and March likely reduces depredation in this area, but also results in increased fishing effort, and therefore increased opportunities for the whales to depredate, around Crozet during this period. Further assessments are also needed to ensure that such operational adaptive measures align with fisheries regulations and resource management strategies. This alignment of regulations and outcomes remains one of the major challenges for various stakeholders (Doyen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gourguet et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nielsen et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eDepredation of Patagonian toothfish by sperm whales and killer whales in subantarctic waters of the Southern Indian Ocean is a major concern for the actors of the fishery, including fishers, managers and researchers working on the ecology and the conservation of whale populations and fish stocks. This concern is shared by the actors of many other fisheries, as documented depredation of catches by large marine predators has increased globally. Our study highlights the importance of understanding the factors driving the spatio-temporal distribution of depredation in developing effective mitigation strategies. Based on long-term data collected in a fishery with a 100% coverage by fishery observers, our results identified a clear spatial overlap between predator-preferred habitats and productive fishing grounds, but also highlighted operational factors that can either exacerbate or reduce depredation risk. Integrating spatio-temporal risk maps into fisheries management rules could help limit economic losses from the conflict associated with depredation, while also reducing its ecological impacts on vulnerable fish stocks and marine predator populations. The distribution models developed in this study provide valuable insights into the mechanisms that may influence and help mitigate interactions of sperm and killer whales with this fishery, with potential applicability to other fisheries facing similar challenges. Future research should explore predator behaviour at fine spatial scales, assess the long-term effectiveness of avoidance strategies, and integrate socio-economic considerations to co-develop mitigation tools that are both practical for fishers and beneficial for conservation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the PPR Oc\u0026eacute;an et Climat.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.M.: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization (including species illustrations), Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing. C.G.: Conceptualization, Investigation, Writing \u0026ndash; review and editing. C.P.: Investigation, Resources. F.MG.: Investigation, Resources, Writing \u0026ndash; review and editing. N.G.: Data Curation, Investigation, Resources, Writing \u0026ndash; review and editing. C.C.: Data Curation, Investigation, Resources, Writing \u0026ndash; review and editing. S.D.: Methodology, Validation, Writing \u0026ndash; review and editing. E.W.: Validation, Writing \u0026ndash; review and editing. S.C.: Validation, Writing \u0026ndash; review and editing. V.R.: Methodology, Validation. C.M.: Funding acquisition, Supervision, Validation, Writing \u0026ndash; review and editing. P.T.: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing \u0026ndash; review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the PPR Oc\u0026eacute;an et Climat. We are grateful to the Mus\u0026eacute;um National d'Histoire Naturelle of Paris and especially P. Pruvost, A. Martin and C. Chazeau, for providing the data from the \u0026ldquo;PECHEKER\u0026rdquo; database. This work could not have been possible without the extensive and rigorous contribution of all the fishery observers and scientific fieldworkers for collecting the data on-board the fishing vessels of the French Patagonian toothfish fishery. We thank the Terres Australes et Antarctiques Fran\u0026ccedil;aises (TAAF), with both the DPQM and the DE, for supporting the work of the fishery observers and scientific fieldworkers. We thank the French Polar Institute (IPEV Program 109, coordinator: Christophe Barbraud at CEBC-CNRS) with the help of Karine Delord and Dominique Besson (CEBC-CNRS) for support in the long-term monitoring programs of whale populations by photo-identification. We also thank the crews of fishing vessels, the toothfish fishing companies (SARPC \u0026amp; Fondation d\u0026rsquo;Entreprise des Mers Australes), and the Direction des P\u0026ecirc;ches Maritimes et de l\u0026rsquo;Aquaculture (DPMA) for their contribution to data collection.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe authors do not have permission to share data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbade, L., Macdonald, D. W., Dickman, A. J. (2014). Assessing the relative importance of landscape and husbandry factors in determining large carnivore depredation risk in Tanzania\u0026rsquo;s Ruaha landscape. \u003cem\u003eBiological Conservation, 180\u003c/em\u003e, 241\u0026ndash;248. https://doi.org/10.1016/j.biocon.2014.10.005.\u003c/li\u003e\n\u003cli\u003eAkaike, H. (1974). 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A protocol for data exploration to avoid common statistical problems. \u003cem\u003eMethods in Ecology and Evolution, 1,\u003c/em\u003e 3\u0026ndash;14. https://doi.org/10.1111/j.2041-2\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Human-wildlife interaction, depredation mitigation, large marine predators, predictive modelling, conservation, Southern Ocean","lastPublishedDoi":"10.21203/rs.3.rs-7009043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7009043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eToothed whale depredation of fish caught on fishing gear raises socioeconomic and conservation concerns. It can lead to substantial losses for fishers and impacts on depredating species, but effective solutions remain limited. In this study, we implemented a spatiotemporal modelling approach to predict depredation occurrence and intensity, based on natural distribution of predators involved and fishing practices, to support mitigation strategies. Using 11 years of data from the Patagonian toothfish (\u003cem\u003eDissostichus eleginoides\u003c/em\u003e) longline fisheries operating around Crozet and Kerguelen islands, and generalized additive models (GAMs), we assessed the environmental and operational factors influencing depredation by sperm whales (\u003cem\u003ePhyseter macrocephalus\u003c/em\u003e) and two killer whale (\u003cem\u003eOrcinus orca\u003c/em\u003e) ecotypes: Crozet and Type D. All models indicated strong seasonal patterns in depredation, particularly for sperm whales, whose presence decreased in winter and was primarily driven by high abundance of large toothfish. Crozet type killer whales were associated with shallow, low-slope areas near the continental shelf, whereas Type D killer whales were more frequent in deeper waters and near seamounts, suggesting a more offshore distribution. Longer soak times and line lengths increased killer whale depredation, likely by increasing gear detectability. Crucially, vessels that moved more than 70 km after a depredation event significantly reduced the likelihood of further interactions with both predator types. The results suggest spatial overlap between fishing grounds and whale-preferred habitats, but highlight clear depredation hotspots within that overlap. Avoiding these areas provides fishers and managers with easy-to-implement, cost-effective options for mitigating depredation while maintaining the socio-economic viability of the activity.\u003c/p\u003e","manuscriptTitle":"Predicting interactions of sperm and killer whales with industrial fisheries in the Southern Ocean: a spatiotemporal modelling approach for conflict mitigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 11:37:25","doi":"10.21203/rs.3.rs-7009043/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-19T12:05:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T21:07:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"340041920210520407557647539265798678176","date":"2025-12-11T18:00:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-22T00:53:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84817277914849288999089054781477635374","date":"2025-09-07T22:19:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T20:14:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T11:15:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T12:34:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2025-06-30T09:39:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7748607a-b99f-4512-8d6e-225b769fa444","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:04:33+00:00","versionOfRecord":{"articleIdentity":"rs-7009043","link":"https://doi.org/10.1007/s10531-026-03312-0","journal":{"identity":"biodiversity-and-conservation","isVorOnly":false,"title":"Biodiversity and Conservation"},"publishedOn":"2026-03-30 15:59:23","publishedOnDateReadable":"March 30th, 2026"},"versionCreatedAt":"2025-09-11 11:37:25","video":"","vorDoi":"10.1007/s10531-026-03312-0","vorDoiUrl":"https://doi.org/10.1007/s10531-026-03312-0","workflowStages":[]},"version":"v1","identity":"rs-7009043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7009043","identity":"rs-7009043","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}