Dissolved oxygen and metabolic parameters improve species distribution models for a marine predator

Dissolved oxygen and metabolic parameters improve species distribution models for a marine predator | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 March 2025 V1 Latest version Share on Dissolved oxygen and metabolic parameters improve species distribution models for a marine predator Authors : Emily Nazario 0000-0003-2372-8742 [email protected] , Nerea Lezama-Ochoa , Max Czapanskiy , Heidi Dewar , Antonella Preti , Alexa Fredston , Malin Pinsky 0000-0002-8523-8952 , Mercedes Pozo Buil , and Elliott Hazen Authors Info & Affiliations https://doi.org/10.22541/au.174100231.19056955/v1 611 views 390 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Species distribute themselves in the environment to maximize fitness, within their physiological and ecological constraints. The influence of dissolved oxygen and temperature on habitat use in marine systems, as well as their interactive effects on metabolic activity, all considerably impact habitat availability. Yet, despite their importance, a species’ physiology is rarely directly considered in species distribution models for marine species. We used species distribution models following boosted regression tree frameworks to evaluate the inclusion of dissolved oxygen and the Aerobic Growth Index (AGI; a metric for metabolic demands) for predicting habitat suitability of immature shortfin mako sharks ( Isurus oxyrinchus ) in the California Current System and adjacent waters using tracking data from 2003-2015. Model performance was assessed using the True Skill Statistic (TSS), Area Under the receiver operating Curve (AUC), and Percent Deviance Explained. Relative to distribution models solely considering traditional environmental predictor variables, we found that dissolved oxygen and the AGI considerably improved immature mako shark species distribution model predictive performance (ΔTSS dissolved oxygen = 0.099; ΔTSS AGI = 0.09; ΔAUC dissolved oxygen = 0.053 ; ΔAUC AGI = 0.050) and explanatory power of the distribution of shortfin mako sharks (Δ% Deviance Explained dissolved oxygen = 10.8; Δ% Deviance Explained AGI = 10.2). While the AGI had similar performance to models considering dissolved oxygen, species habitat predictions including the AGI uniquely predicted low habitat suitability in regions known to be metabolically stressful for the species, the Pacific North Equatorial Current. Ocean warming and deoxygenation are inextricably linked, which will have direct impacts on metabolic habitat viability, thus appropriately accounting for these changes together will result in improved understanding of current habitat availability, climate-ready management tools, and robust conservation planning. Title Dissolved oxygen and metabolic parameters improve species distribution models for a marine predator Abstract Species distribute themselves in the environment to maximize fitness, within their physiological and ecological constraints. The influence of dissolved oxygen and temperature on habitat use in marine systems, as well as their interactive effects on metabolic activity, all considerably impact habitat availability. Yet, despite their importance, a species’ physiology is rarely directly considered in species distribution models for marine species. We used species distribution models following boosted regression tree frameworks to evaluate the inclusion of dissolved oxygen and the Aerobic Growth Index (AGI; a metric for metabolic demands) for predicting habitat suitability of immature shortfin mako sharks ( Isurus oxyrinchus ) in the California Current System and adjacent waters using tracking data from 2003-2015. Model performance was assessed using the True Skill Statistic (TSS), Area Under the receiver operating Curve (AUC), and Percent Deviance Explained. Relative to distribution models solely considering traditional environmental predictor variables, we found that dissolved oxygen and the AGI considerably improved immature mako shark species distribution model predictive performance (ΔTSS dissolved oxygen = 0.099; ΔTSS AGI = 0.09; ΔAUC dissolved oxygen = 0.053; ΔAUC AGI = 0.050) and explanatory power of the distribution of shortfin mako sharks (Δ% Deviance Explained dissolved oxygen = 10.8; Δ% Deviance Explained AGI = 10.2). While the AGI had similar performance to models considering dissolved oxygen, species habitat predictions including the AGI uniquely predicted low habitat suitability in regions known to be metabolically stressful for the species, the Pacific North Equatorial Current. Ocean warming and deoxygenation are inextricably linked, which will have direct impacts on metabolic habitat viability, thus appropriately accounting for these changes together will result in improved understanding of current habitat availability, climate-ready management tools, and robust conservation planning. Keywords hybrid-species distribution model, dissolved oxygen, aerobic growth index, habitat suitability, physiology, mako sharks Introduction Marine predators are critical to ecosystem function, and their population declines can increase community vulnerability, disrupt nutrient cycling, and alter trophic dynamics (Bornatowski et al., 2014; Britten et al., 2014; Dedman et al., 2024; Estes et al., 2011; Heithaus et al., 2008; Myers et al., 2007; Sherman et al., 2023). These species can encounter multiple threats, including long-term environmental change (i.e., ocean deoxygenation, warming) and more acute concerns, like overfishing and bycatch (Crain et al., 2009; He & Silliman, 2019; O’Hara & Halpern, 2022; Pollom et al., 2024). As such, many species are in global decline, such as select populations of the sooty shearwater ( Ardenna grisea ) and scalloped hammerhead ( Sphyrna lewini ), which have experienced declines ranging from 70-98% (Britten et al., 2014; Dulvy et al., 2021; Dulvy & Kindsvater, 2017; Estes et al., 2011; Hayes et al., 2009; Myers et al., 2007; Pacoureau et al., 2021; Veit et al., 1997). Protecting these species is challenging due to multi-jurisdictional distributions (Dunn et al., 2019; Roberson et al., 2021), cumulative threats (Block et al., 2011; Maxwell et al., 2013), and anticipated spatial redistributions in response to climate variability (Braun, Lezama-Ochoa, et al., 2023; Erauskin‐Extramiana et al., 2019; Pinsky et al., 2020; Welch et al., 2023). Already, changing environmental conditions have altered the timing and location of ecologically significant areas, eading to nvel predator-prey dynamics and increased human-wildlife conflict (Hindell et al., 2020; Samhouri et al., 2021; Santora et al., 2020; Smith et al., 2023; Sunday et al., 2015). Sustainable management in the age of climate change requires a better understanding of the drivers of species occurrence and habitat suitability. Habitat distribution in both terrestrial and marine environments has typically been studied by correlating species presence or abundance data with environmental variables through the use of species distribution models (SDMs) (Elith & Leathwick, 2009). For marine predators, habitat suitability has been associated with a range of static and dynamic environmental variables, including bathymetry and sea surface temperature (Braun, Arostegui, et al., 2023; Brodie et al., 2018; Crear et al., 2021; Maxwell et al., 2019). Model outputs have been applied to address a range of concerns, including bycatch, ship strikes, and spatial conservation planning (Blondin et al., 2020; Hazen et al., 2017, 2018; Marshall et al., 2014). Despite their widespread use, SDMs often omit process-based drivers of habitat selection, such as a physiology, life history, and species interactions, raising concerns that existing correlations between species presence and environmental variables do not account for adaptation to ongoing and anticipated climate variability (Evans et al., 2015; Hazen et al., 2013; Kearney & Porter, 2009; Muhling et al., 2020; Singer et al., 2016). Integrating ecological and/or physiological data into SDM development could provide more robust projections under future environmental conditions while clarifying how mechanistic processes shape present-day distributions (Evans et al., 2015). Ecophysiology can provide insight into the complex relationship between marine predators and their environment (Evans et al., 2015; Kearney & Porter, 2009; Singer et al., 2016). Recent work combines environmental, ecological, physiological, and/or spatial data to produce models that better account for the underlying mechanisms contributing to habitat selection. For example, oxygen requirements and thermal constraints of South African reef fish were identified as key distribution drivers (Duncan et al., 2020). In another study, a mechanistic oxygen balance model for Atlantic bluefin tuna ( Thunnus thynnus ) provided refined habitat suitability relative to a correlative SDM, and suggested metabolic stress could drive a poleward range shift (Muhling et al., 2017). Unfortunately, metabolic data is rarely available for highly mobile fish due to the difficulty of conducting controlled studies (Evans et al., 2015). This poses a challenge for SDMs as metabolic demands are known to drive species distributions (Deutsch et al., 2020). Recently developed approaches to modeling resting and maintenance metabolic rates (the Metabolic Index and Aerobic Growth Index, respectively) offer an exciting opportunity to study how physiological traits influence habitat suitability predictions with minimal empirical data requirements (Clarke et al., 2021; Deutsch et al., 2020; Essington et al., 2022; Kearney & Porter, 2009). Including these metrics in combination with traditional environmental predictors to develop hybrid SDMs (hSDM) offers a valuable framework for studying the intersection between environmental conditions, physiological requirements, and habitat suitability (Tourinho & Vale, 2023). Such mechanistic additions are especially relevant for species with elevated metabolic demands (e.g., tunas, lamnid sharks). For these species, explicitly incorporating physiological constraints may address biases in the predictions of traditional, correlative SDMs, and improve model performance and ecologically relevant suitability predictions and projections (Buckley et al., 2010; Muhling et al., 2017; Tourinho & Vale, 2023). Among elasmobranchs, shortfin mako sharks ( Isurus oxyrinchus ; mako sharks hereafter) have some of the highest resting and maximum metabolic requirements among shark species, likely resulting from the suite of adaptations needed to support their high energy foraging behaviors and regional endothermy (Sepulveda et al., 2007; Sims et al., 2021; Waller et al., 2023). Mako sharks are globally distributed and taken by national and international fleets as well as being a popular recreational catch (Camhi et al., 2008). As a result of overfishing and bycatch, mako sharks are listed as endangered by the International Union for Conservation of Nature (IUCN) due to concerns about the Atlantic population (Lohe et al., 2022; Rigby et al., 2018). While the Eastern North Pacific (ENP) sub-population is considered healthy (ISC 2023), it has been projected to experience large-scale habitat loss and a poleward shifts due to climate change, necessitating novel conservation strategies and management approaches (Hazen et al., 2013; Santos et al., 2024). Given their high metabolic demands, availability of tracking and metabolic data, and conservation concerns, mako sharks are an excellent species for applying physiologically informed SDMs (i.e., hSDMs) to assess the influence of dissolved oxygen (DO) and/or metabolic constraints on habitat suitability. In this study, we tested whether a physiologically-informed environmental covariate, the Aerobic Growth Index (AGI), would improve model performance and ecological realism of SDMs for immature mako sharks ( Figure 1 ; Clarke et al., 2021). We compared the performance of three models that (1) included static and dynamic variables at the surface at a daily temporal resolution (base model, hereafter); (2) built on the base model and included DO at the surface and at depth at daily, seasonal, and annual resolutions (DO model, hereafter; and (3) built on base models and included a mechanistic variable, the AGI, at the surface and at depth at daily, seasonal, and annual resolutions (AGI model, hereafter). We expected that all model types would perform comparably in regions (the CCS) and time periods (neutral ENSO phases) within DO concentrations and water temperatures within their physiological capacities. We also predicted that the base model would overpredict areas of high habitat suitability in metabolically stressful regions (i.e., relatively low DO concentrations and/or high water temperatures), such as the Pacific North Equatorial Current, and time periods, such as La Niña years. Additionally, under these metabolically stressful conditions, we predicted that the AGI model would outperform the DO and base model. Identifying how DO and metabolic demands may influence habitat suitability will provide insight into the relationship between physiological constraints and habitat use, to help us understand ongoing and anticipated climate variability and associated changes in species distributions. Materials and methods This study combines tracking data, modeled environmental data, and the AGI to develop and compare three SDMs for immature mako sharks in the CCS. Tracking data Location data used in this study were collated from tagging efforts that occurred between 2002 and 2013 in the Southern California Bight and off the Pacific Coast of Baja California, Mexico (Supplemental Table 1; Nasby-Lucas et al., 2019). Mako sharks were tagged using a combination of pop-up satellite archival tags (PSAT) and satellite-linked radio-transmitting tags (SPOT; location accuracy as high as 250m). PSAT tags provided information on shark depth and temperature distributions, while location data was exclusively provided by the SPOT tags. A detailed description of the protocols used for tag deployment, specifications, and data processing can be found in Nasby-Lucas et al., 2019. Only immature individuals were included in this study and maturity was determined by fork length (FL). Females in this study had FLs between 100 cm - 249 cm, and males had FLs between 100 cm - 180 cm (Joung & Hsu, 2005; Urbisci et al., 2013); (Supplemental Figure 1). Tracks and segments lasting fewer than 30 days ( n = 4) were excluded to reduce tagging location bias. All analysis was completed using R (version 4.3.0, R Core Team, 2023). A continuous-time correlated random walk (CRW) state-space model from the ‘aniMotum’ package (version 1.2, Jonsen et al., 2020) was used to interpolate daily positions. A uniform time step of 29 hours between modeled locations was selected as determined by calculating the average median time step between successive locations across the entire dataset. Tracks with gaps longer than 30 days ( n = 9) between successive locations were split into separate segments prior to CRW state-space modeling (Maxwell et al., 2019). The final sample size for this analysis included 73 tracks from 70 mako sharks with an average of 213 positions per track (range: 42-587) over an average of 256 days across tags (range: 49.5-708; Supplemental Figure 2 ). Locations spanned from -151.08 and -105.41°W longitude and 2.99 to 47.37°N latitude from June 2003 to March 2015. The satellite tags only provide species presence, and thus pseudo-absences were generated to sample areas that were available but not selected (Barbet-Massin et al., 2012; Hazen et al., 2021). Pseudo-absences were generated using the ‘sim_fit()’ function from the ‘aniMotum’ package, which generated track simulations using CRW models that preserved the same start location and number of positions as the observed track, while the movements were random but correlated in direction and magnitude (version 1.2, Jonsen et al., 2020; see Supplemental Document 1 for more information on pseudo-absence generation). Lastly, prior to fitting the hSDM, one track of pseudo-absences was randomly selected for each state-space modeled track to maintain a 1:1 ratio between presence and pseudo-absence positions. Environmental data Environmental variable selection for this study was based on previous SDM models in the study region (Brodie et al., 2018). A total of 13 environmental variables (2 static, 5 surface dynamic, 2 subsurface dynamic) relevant to mako shark movement and/or foraging ecology were included ( Table 1 ) . The static variables were bathymetry (z, m) and rugosity (z_sd, m; GEBCO Compilation Group, 2024; aggregated to a 0.25° resolution). For the dynamic variables, we used two global ocean models without data assimilation from Mercator Ocean International. The first simulates daily global ocean physics fields at a 0.25° horizontal resolution (GLORYS2V4). The second is a biogeochemical hindcast accessed via E.U. Copernicus Marine Service Information (CMEMS; GLOBAL_MULTIYEAR_BGC_001_029). The biogeochemical product used physical forcing from the GLORYS2V4 model and produced daily global ocean biogeochemical fields also at a 0.25° resolution. The selected dynamic variables included temperature (temp., °C), sea surface height (SSH, m), mixed layer depth (MLD, m), salinity (sal, psu), and chlorophyll-a (chl-a, mg/m 3 ). The remaining variables, dissolved oxygen (DO, mmol/m 3 ) and the Aerobic Growth Index (AGI), were dynamic and included at the three depth layers. The depths selected for this study included 0 m (surface), 60 m (the depth representing 90% of median dive depths as indicated by the PSAT data), and 250 m (the depth representing 90% of the maximal dive depth across all individuals as indicated by the PSAT data). We selected the three vertical levels from the models nearest to these depths, which were 0.51 m, 61.11 m, and 244.89 m. The modeled physics were on a curvilinear grid, requiring additional post-processing to ensure its format aligned with the modeled biogeochemical data ( Supplemental Document 2 ). The Mercator data was also validated across our study domain both in terms of long term averages and temporal variation ( Supplemental Document 2 ). To examine patterns across temporal scales, we downloaded the modeled environmental data at a daily resolution and then generated seasonally or annually averaged raster files for DO and the AGI from the daily rasters alone, thus seasonally and annually averaged raster files were not included in the base model. Aerobic Growth Index (AGI) The AGI captures the theoretical temperature-driven maintenance oxygen demands of a species using von Bertalanffy growth theory, metabolic theory, and biogeography (Clarke et al., 2021). The AGI is the ratio of DO supply to theoretical maintenance oxygen demand (Eq.1 & 2 adapted from Eq. 1 and Eq. 13 in Clarke et al., 2021; Morée et al., 2023). DO is included as a partial pressure (atm), which is more physiologically relevant than DO concentrations (Clarke et al., 2021). (Eq. 1) \(AGI\ =\ \frac{pO_{2}^{\text{supply}}}{pO_{2}^{\text{demand}}}\) (Eq. 2) \(pO_{2}^{\text{demand}}\) =\(\frac{W^{1-d}\ \bullet\ exp(\frac{-j_{2}}{T})\ \bullet\ pO_{2}^{\text{thresh}}\bullet\ exp(\frac{-j_{1}}{T^{\text{pref}}})}{W_{\infty}^{1-d}\bullet\ \ exp(\frac{-j_{1}}{T})\ \bullet\ \ exp(\frac{-j_{2}}{T^{\text{pref}}})}\) The DO available in the environment is represented as p O 2 supply (atm), and T is the temperature of the surrounding water (K). The anabolism activation energy ( j 1 = 4.5), the catabolism activation energy ( j 2 = 8), and the metabolic scaling coefficient d (0.7) are not species-specific, increasing the potential application of this approach (Cheung et al., 2011; Clarke et al., 2021; Morée et al., 2023). The mean species and asymptotic weights are included as W and W ∞ , respectively, and were identified for shortfin mako sharks from FishBase as 51807 g and 228421 g (Froese & Pauly, 2024). The preferred temperature ( T pref ) was 16.45°C (289.6 K), which we selected as it was the median temperature at the median dive depth (50 m) this study’s sharks occupied as reported by the PSAT tag data. Lastly, an oxygen threshold ( p O 2 thresh ), of 2 ml/L (0.0494 atm at 0 m) was selected based on previous studies (Vetter et al., 2008). The AGI was calculated at 0 m, 60 m and 250 m. For each depth, we used 16.45°C (289.6 K), but corrected the oxygen threshold value to account for changes in temperature, pressure, and salinity (Supplemental Code 2 Clarke et al., 2021). The same code was used to convert the environmental oxygen concentrations from mmol/m 3 to atm. For all observed and pseudo-absence positions, the AGI was calculated for each depth using the depth-specific DO (atm) and temperature (K) data. Lastly, we identified regions with an AGI less than 1, i.e. where the p O 2 supply is less than the p O 2 demand . Species distribution models hSDMs were developed for all immature mako sharks to investigate statistical associations between the presence data and the environmental predictors described in Table 1. We used a boosted regression tree (BRT) framework from the “dismo” R package to model occurrence probability as a function of a suite of environmental covariates (see ‘Environmental data’ section above; Elith et al., 2008). BRTs have been widely used to model shark habitat suitability in the CCS and are a common approach due to their ability to handle collinearity, fit complex non-linear relationships, and missing information in predictor datasets (Elith et al., 2008). BRT hyperparameters were tuned using the “caret” R package (Kuhn, 2008). Here, a grid of values were tested for the learning rate and tree complexity, and ultimately, values of 0.05 and 3, respectively, were selected as these resulted in the highest model accuracy as reported by cross-validation. All models had a bag fraction of 0.75 and used a Bernoulli family in accordance with the structure of the response variable (presence: 1 and absence: 0). A number of hSDMs were compared ( Supplemental Figure 3; Supplemental Table 2). Predictor variables were included if they had a relative importance score that was consistently higher than values randomly drawn from a normal distribution. Models were evaluated and ultimately selected using percent explained deviance, Area Under the receiver operating Curve (AUC), and true skill statistic (TSS; Elith et al., 2008). All exploratory models were initially trained using 75% of the data (selected at random across the full dataset). The remaining 25% was used to evaluate overall model performance. To account for BRT variations due to input data, 20 iterations were developed for different 75% subsampled tracking and pseudo-absence data (Welch et al., 2023). Model exploration regarding depth layers and temporal resolution was done using a step-wise approach to balance the trade-offs of added model complexity. Risk of overfitting, relative importance of predictor variables, model performance, and ecological realism of habitat suitability predictions were all considered when selecting the best models. This selection resulted in a final list of three models: a base model, a DO model, and an AGI model ( Supplemental Figure 4 ). We evaluated model performance across the three final models using the test dataset described above (25% of the full dataset). Each of the 20 model iterations were used to generate spatial predictions indicating a habitat suitability index (HSI) ranging from values of 0 to 1, with 0 being the least and 1 being the most suitable. Final habitat suitability prediction maps were averaged over the entire study period and across iterations for each of the final models: base, DO, and AGI. To test variations in model performance associated with different spatial regions and ENSO phases, stratified sampling was completed to keep 20% of nine spatiotemporal combinations for model evaluation, and 20 iterations of BRTs using the same approach described above were generated with the remaining data. The nine configurations represent every combination of three spatial regions: the CCS (between -134° W to shore and 30-48° N), NEC (all area below 12° N), and remaining area of the NEP not included in the CCS or NEC, and the three ENSO phases: neutral, La Niña, and El Niño. ENSO phases were determined using the Southern Oscillation Index (SOI) hosted by NOAA (https://www.cpc.ncep.noaa.gov/data/indices/soi). When a given date’s SOI was > 0.5 we determined that month to have El Niño conditions, and if < -0.5, that month was assigned to have La Niña conditions. We used the held-out 20% of data from each of these nine regions to calculate region- and ENSO phase-specific SDM skill scores, using the same model evaluation statistics as above. Habitat suitability maps were developed for the neutral ENSO year across the study domain. Difference maps were created to compare the habitat suitability variations between ENSO phases for each best-performing model for a strong El Niño and La Niña year, 2014 and 2010, respectively. Aerobic Growth Index The AGI varied over the study area, with values equal to or less than 1 occurring predominately existing nearshore and extending further offshore below 25° latitude ( Figure 2 ). Near the equator the low AGI values overlaps with areas of relatively low DO and high temperature (Supplemental Figure 5; Supplemental Figure 6; Supplemental Figure 7; Supplemental Figure 8; Supplemental Figure 9; Supplemental Figure 10 ). Regions where metabolic demands were not met (AGI < 1) comprised ~30% of the total study domain. Areas where AGI < 1 varied slightly with ENSO phases; 2.48% more habitat during the strong La Niña vs 0.59% less habitat during a strong El Niño. Species distribution models Models considering DO and/or the AGI had significantly better explanatory power and predictive performance relative to the base model ( Figure 3a ) with higher TSS, AUC and deviance explained. While the DO model had slightly higher performance metrics than the AGI model, the differences were not significant. Including either the AGI or DO in the NEP and CCS improved model performance across all ENSO phases ( Figure 3b ). The largest model improvements were observed in the NEC under neutral ENSO conditions, while all models performed similarly within the NEC during El Niño and La Niña phases. A model-driven approach was taken when selecting the final list of predictor variables ( Supplemental Figure 11 ). DO and AGI models containing predictors across all three depth layers (0m, 60m, 250m) and temporal resolutions (daily, seasonal, annual) performed just as well relative to comparable models that omitted data at 60m (Supplemental Figure 3; Supplemental Table 2). All temporal resolutions appeared in the top five most important predictor variables for both the DO and AGI models, and were consequently included in the final models ( Supplemental Figure 4; Supplemental Figure 12; Supplemental Figure 13; Supplemental Figure 14 ). Habitat suitability maps Predicted habitat suitability maps averaged over the entire study period had distinct variations for each model type ( Figure 4 ). The DO model contained the largest area (8.22%) of habitat suitability (HSI > 0.75), with large offshore patches overlapping with the Pacific North Equatorial Current. The AGI model predicted reduced high habitat suitability area (7.1%), while the base model predicted the least amount of high habitat suitability area (6.27%). Relative to a neutral year (2013), the DO and AGI models predicted habitat suitability loss during a strong La Niña year primarily observed through loss in offshore habitat (2010; Figure 5 ). The AGI model predicted the largest contraction (1.2%), while the base model predicted habitat gain (0.12%). All models predicted habitat loss during a strong El Niño year (2014), with the DO model predicting the largest contraction (0.92%) and the AGI model predicting the least amount of habitat loss (0.53%). Discussion Characterizing the environmental and mechanistic factors driving a species suitable habitat can improve distribution models that support management and conservation planning. While selected environmental predictors can serve as proxies to physiological processes (e.g., temperature, DO concentrations), they may not capture the interactive and non-linear impacts on species’ distributions (Carlisle et al., 2017). Thus, recent advances that merge physiology and biogeography (i.e., Metabolic Index, AGI) with relatively low data requirements mark an important next step in species habitat modeling. We identified that the inclusion of the AGI and/or DO at multiple depths and temporal resolutions improved model performance and explanatory power for immature mako sharks in an important nursery area (hypothesis 1). The AGI model performed better in the NEP and CCS during La Niña oceanographic conditions (hypothesis 2 & 3). Further, the AGI model indicated lower habitat suitability scores relative to the other model predictions in low latitude regions suggesting high water temperatures and shallow oxygen minimum zones (OMZ) may limit habitat (Cepeda-Morales et al., 2013; Stramma et al., 2012). Accounting for a species’ physiological constraints and mechanistic drivers in SDM approaches is a key tool especially as we move into novel ocean conditions associated with climate change. DO and AGI habitat suitability insights All models predicted high near-shore habitat suitability throughout the CCS, which agrees with previous immature mako shark habitat designation ( Figure 4 ; Carreón-Zapiain et al., 2018; Nasby-Lucas et al., 2019; Nosal et al., 2019). The traditional correlative model (referred to as the ‘base model’) and the DO model also predicted high offshore habitat suitability between 10-20° latitude. This is distinct from the AGI model’s predictions, which predicted moderate to low suitability in this region. This area surrounds the Pacific North Equatorial Current (NEC) and the western warm pool of the Pacific, waters characterized by a sharp thermocline, sea surface temperatures exceeding 28°C, and low DO (OMZs as shallow as 60m depth; Cepeda-Morales et al., 2013; Griffiths et al., 2019; O’Brien et al., 2014). High temperature combined with low oxygen makes the region metabolically stressful, resulting in foraging hotspots for some predators because prey species are concentrated toward the oxygenated surface waters (Byrne et al., 2024; Logan et al., 2023; Polovina et al., 2004; Ryan et al., 2017; Stewart et al., 2019). The competing factors in this region, profitable foraging vs. metabolic constraints, may result in the observed patterns of brief habitat use while simultaneously limiting long-term residence and overall suitability. The equatorial waters of the Northeast Pacific have been described as a potential latitudinal boundary for mako sharks, and it’s been hypothesized that this pattern was likely due to unsustainable metabolic conditions (Byrne et al., 2024). This is confirmed by the present study using models inking physiology and environmental conditions. In this region, the metabolic demands exceeded oxygen supply (AGI < 1) at 250 m ( Figure 2 ). Thus, the relatively low habitat suitability of this area predicted by the AGI model captured a more ecologically realistic depiction of Northeastern Pacific immature mako shark habitat suitability. Results for the Eastern North Pacific can provide insight into habitat suitability in other regions. For example, in the western North Atlantic, mako sharks may experience similar environmental conditions and constraints on habitat, impacting this overfished population’s distribution (NOAA 2017 stock assessment; Byrne et al., 2024; Sims et al., 2021; Sims et al., 2018). Thus, quantifying how these interacting effects may redistribute species and ecological important areas (e.g., low latitude and/or vertical habitat compression) will be increasingly important for climate adaptation and species conservation objectives (Bijman et al., 2013; Comte et al., 2024; Gilly et al., 2013; Jorda et al., 2019; Stramma et al., 2012; Waller et al., 2024; Whitney et al., 2023). The occurrence of El Niño and La Niña during this study provides the opportunity to examine model performance and shifts in habitat suitability under naturally occurring environmental variability ( Figure 5 ). The reduced habitat suitability during the La Niña year were only captured by models including DO and/or the AGI. La Niña conditions in the CCS result in anomalous cool, deoxygenated, and productive waters at the surface (Bjorkstedt et al., 2012; Ohman et al., 2017). During the La Niña year, the DO model predicted large areas of habitat contractions along the NEC, and that habitat expansion took place coastally. While in contrast, the AGI model predicted contractions offshore at high latitudes. Environmental shifts associated with La Niña can increase metabolically stressful habitat in the upper water column for species sensitive to hypoxic conditions and. For example, similar conditions in Costa Rica reduce the occurrence of other lamnid sharks with high energetic requirements (Osgood et al., 2021). This provides support for the patterns observed in this study and for the use of mechanistic SDMs. Both the DO and AGI model predicted reductions in area of high habitat suitability during the 2014 El Niño, primarily nearshore at low latitudes. This pattern is consistent with the poleward shifts that have been observed for other marine species following previous El Niño events. El Niño is characterized by current intensity, reduced upwelling, concentrated regions of productivity, increased sea surface temperatures (Lynn & Bograd, 2002; Ohman et al., 2017). Habitat expansions occurred near-shore at mid to high latitudes, and as offshore upwelling declines and becomes concentrated along the coast during strong El Niño events, bands of primary productivity (i.e., Chl- a ) become compressed near shore possibly increasing the suitability in these regions (Ohman et al., 2017). The percent change in high habitat suitability predicted by the DO and AGI models was smaller than anticipated, but may still capture the general ecological patterns taking place more accurately than the base model, and greater impacts under short extreme events (e.g., marine heat waves) and climate change scenarios. Inclusion of DO and the AGI into habitat models The comparison among models, including physiological and environmental parameters, in three dimensions provides insight into factors influencing habitat suitability and model performance. Across models, oxygen concentrations or AGI values at an annual resolution at 250m and at a daily resolution at 0m had the highest influence relative to the other variables ( Supplemental Figure 11 ). While DO concentrations at the surface are typically considered saturated, lower latitudes had relatively lower surface DO concentrations (Supplemental Figure 5). The surface layer is an important metabolic refuge for many marine species, and thus regions with low surface DO may not be as suitable (Carlisle et al., 2017; Prince & Goodyear, 2006). At depth, oxygen concentrations in the Northeast Pacific vary considerably, with OMZs being as shallow as 60m at low latitudes (16-20°N) along the coast ( Supplemental Figure 6 ; Cepeda-Morales et al., 2013). These low oxygen areas are known to restrict vertical habitat use of hypoxia-intolerant marine prey and predators (Byrne et al., 2024; Carlisle et al., 2017; Humphries et al., 2024; Sepulveda et al., 2010). Including oxygen concentrations or demands at longer temporal scales (i.e., annual resolution) in habitat models may help account for persistent mechanistic constraints possibly driving overall distributions (Mannocci et al., 2017). The addition of DO and AGI variables improved model explanatory power and predictive performance. Oxygen is critical for physiological functions, and thus considering its role in habitat use and suitability is essential (Bandara et al., 2024; Carlisle et al., 2017; Essington et al., 2022; Koslow et al., 2011; Muhling et al., 2017). DO and the AGI provided similar improvements to model performance relative to the base model when compared across the full study domain and time period ( Figure 3a ). The DO model typically performed the best, although in the NEP and CCS during La Niña phases, the AGI model may have better explained the resulting habitat suitability consequences ( Figure 3b ). Model performance within the NEC during El Niño and La Niña phases presented mixed results, possibly linked to small sample sizes for these scenarios. While the results from this work suggest that DO and the AGI provided similar contributions to model performance, future work should also be done to understand if this pattern extends to additional species with varying metabolic demands, in other boundary zones, and across different spatiotemporal resolutions, and scales (Bandara et al., 2024; Essington et al., 2022). However, the distinct ecological insights captured by the AGI habitat suitability map predictions show improved biological realism not captured by the model performance scores (Zurell et al., 2020). The AGI offers a novel approach to accounting for a metabolic activity in species distribution modeling, and given its minimal empirical data requirements, is available to hundreds of marine fish and invertebrate species (Clarke et al., 2021). The AGI, however, represents the most basal metabolic requirements for a species, and is limited to representing the energetic demands needed to support survival, feeding, and movement, but not growth, reproduction or recovery from anaerobic activity. Thus, field metabolic demands for a species will be much higher, and this must be considered when interpreting the AGI model habitat suitability results. Additionally, metabolic rates and horizontal and vertical habitat use will directly vary with mako shark size (Nasby-Lucas et al., 2019). The models in this study pooled individuals according to a singular age class (i.e., immature animals according to sex-specific fork length) and only supplied one layer of environmental data at depth. Future work investigating 1) empirical metabolic measurements along with 2) specific responses to variations in DO through the water column and 3) the development habitat models in a 3D environment will considerably improve future SDM efforts and ecological relevance (Andrzejaczek et al., 2022; Valle et al., 2024). The improved performance metrics and suitability predictions support the consideration of DO and/or the AGI at various depths and temporal resolutions, though deciding which metric, depth, and temporal resolution to use will likely be species-specific. The AGI uniquely predicted low habitat suitability in metabolically stressful regions, which has not been previously accounted for. Including physiological drivers has conservation implications for anticipated climate variability, including ocean warming and deoxygenation (Brodie et al., 2018). 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A standard protocol for reporting species distribution models. Ecography , 43 (9), 1261–1277. https://doi.org/10.1111/ecog.04960 Tables Table 1. Physical and biological environmental variables and how they were accessed Variable name Unit Acronym Downloaded spatial resolution Downloaded temporal resolution Ecological relevance References Data source Model Temperature °C temp 0.25° Daily Physiological temperature effects on makos and prey. Nasby-Lucas et al., 2019; Adams et al., 2016; Brodie et al., 2018; Ramos et al., 2017; Yu et al., 2019; Paulino et al., 2016; Succa et al., 2022; Syah et al., 2016; Hsu et al., 2022 GLORYS2V4 reanalysis without data assimilation https://www.mercator-ocean.eu/solutions-expertises/acceder-aux-donnees-numeriques/produits-de-loffre/?offer=4217979b-2662-329a-907c-602fdc69c3a3 Base, DO, AGI Sea surface height m SSH 0.25° Daily Indicator of divergence and convergence. SSH anomoly used more frequently in prey species SDMs. Succa et al., 2022 Base, DO, AGI Mixed layer depth m MLD 0.25° Daily Association with MLD and mako vertical habitat use. MLD effects the vertical water column structure (25-200m depth) and distribution of nutrients. Nasby-Lucas et al., 2019; Brodie et al., 2018 Base, DO, AGI Salinity psu sal 0.25° Daily Observed to influence common prey item presence. Not as consistently included in squid lit as other vars (SST, Chl, NPPV). Ramos et al., 2017; Yu et al., 2019; Stewart et al., 2014; Nasby-Lucas et al., 2019; Hsu et al., 2021. Base, DO, AGI Chlorophyll-a mg/m 3 chl-a 0.25° Daily Proxy for productivity and prey aggregations (including SDMs for Jumbo/Humboldt squid and Pa. Saury). Observed seasonal movements within the CCS coinciding with peaks in chl-a. Brodie et al., 2018; Ramos et al., 2017; Paulino et al., 2016; Nasby-Lucas et al., 2019; Syah et al., 2016 CMEMS Global Ocean Biogeochemistry Hindast https://doi.org/10.48670/moi-00019 Base, DO, AGI Dissolved oxygen mmol/m 3 DO 0.25° Daily Physiological oxygen demands and suggested to constrain the vertical movement of Io. Shoaling OMZ may result in habitat expansion for prey species. Nasby-Lucas et al., 2019; Ramos et al., 2017; Stewart et al., 2014 DO Bathymetry* m z 0.004° NA Observed juv. mako associations with bathy contours, proxy for light, salinity, pressure, and temperature. Observed influence surrounding bathy features where prey may aggregate. Brodie et al., 2018; Sepulveda et al., 2004 GEBCO https://doi.org/10.5285/1c44ce99-0a0d-5f4f-e063-7086abc0ea0f Base, DO, AGI Rugosity (bathymetry sd)* m z_sd 0.004° NA Proxy for habitat complexity and may influence prey availability. Squid abundance associations with shifts in bathy such as sea mounts. Suca et al., 2022 Base, DO, AGI Aerobic Growth Index NA AGI NA NA Metabolic and thermal drivers that may influence habitat use. Clarke et al., 2021 Derived from GLORYS4_FREE and CMEMS Global Ocean Biogeochemistry Hindast product variables AGI aggregated data so resolution was 0.25 degrees for the models Figures legends and embedded figures Supplementary Material File (table_1.xlsx) Download 17.83 KB Information & Authors Information Version history V1 Version 1 03 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords aerobic growth index dissolved oxygen habitat suitability hybrid-species distribution model mako sharks physiology Authors Affiliations Emily Nazario 0000-0003-2372-8742 [email protected] University of California Santa Cruz View all articles by this author Nerea Lezama-Ochoa University of California Santa Cruz Institute of Marine Sciences View all articles by this author Max Czapanskiy University of California Santa Barbara View all articles by this author Heidi Dewar NOAA Fisheries Southwest Fisheries Science Center View all articles by this author Antonella Preti University of California Santa Cruz Institute of Marine Sciences View all articles by this author Alexa Fredston University of California Santa Cruz View all articles by this author Malin Pinsky 0000-0002-8523-8952 University of California Santa Cruz View all articles by this author Mercedes Pozo Buil University of California Santa Cruz Institute of Marine Sciences View all articles by this author Elliott Hazen University of California Santa Cruz View all articles by this author Metrics & Citations Metrics Article Usage 611 views 390 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Emily Nazario, Nerea Lezama-Ochoa, Max Czapanskiy, et al. 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