Convective rainfall drives behavioral shifts across multiple foraging scales in breeding seabirds

Convective rainfall drives behavioral shifts across multiple foraging scales in breeding seabirds | 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 Convective rainfall drives behavioral shifts across multiple foraging scales in breeding seabirds Wataru Takeda, Shiho Koyama, Yusuke Goto, Ken Yoda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6970356/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Movement Ecology → Version 1 posted 11 You are reading this latest preprint version Abstract Background Seabird foraging behaviors may be influenced not only by the direct adverse effects of rainfall (e.g., limitations on flight) but also by indirect effects—either adverse or beneficial—such as changes in prey distribution. To understand the multifaceted impacts of rainfall and their ecological significance, it is essential to examine foraging behavior across multiple spatiotemporal scales. In particular, assessing the effects of altered convective rainfall patterns—localized and shorter-lived events that have become increasingly frequent due to recent rapid temperature increases—is crucial for predicting future behavioral shifts in seabirds. Methods We investigated the relationships between multiple climatic factors—including two types of rainfall (convective and large-scale rainfall), cloud cover, and wind speed—and three spatiotemporally distinct aspects of foraging behavior, as well as chick growth, in streaked shearwaters Calonectris leucomelas rearing chicks on Awashima Island, Japan, from 2011 to 2024. Specifically, we focused on fine-scale behavioral states—traveling, foraging, and resting—estimated using a Hidden Markov Model; meso-scale trip parameters, including maximum distance and duration of each foraging trip; broad-scale behavioral specialization, quantified as Individual Foraging Site Fidelity (IFSF); and chick growth rate as a proxy for foraging outcomes. Results Increased convective rainfall was associated with increased transition probabilities from traveling to foraging states, higher stationary probabilities of foraging states, longer foraging trip durations, and higher IFSF. Additionally, cloud cover and wind speed were associated with fine-scale behavioral states and meso-scale trip parameters. While increased large-scale rainfall was associated with higher IFSF, no clear associations were found between climatic factors and chick growth rate. Conclusions Seabirds engaged in more active prey searching during convective rain events, which prolonged foraging trips, and successful foraging experiences during these periods increased IFSF. We propose that fine-scale behavioral changes induced by convective rain, likely driven by increased prey availability, can cascade into broader-scale behavioral patterns. Our findings contributes to a comprehensive understanding of climate-induced changes in seabird foraging behavior by focusing on behavior across multiple spatiotemporal scales. foraging behavior climate impacts seabird rainfall individual foraging site fidelity hidden Markov model biologging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background In recent years, rapid temperature increases driven by anthropogenic activities have been observed globally [ 1 ], significantly altering marine climatic conditions, particularly in patterns of cloud cover, rainfall, and wind speed [ 2 – 4 ]. As top predators in marine ecosystems, seabirds are especially sensitive to climatic variability [ 5 – 7 ]. Specifically, climatic factors such as cloud cover [ 12 ], rainfall [ 11 ], and wind speed [ 8 – 10 ] can directly constrain seabird foraging behavior. In addition to these direct effects, rainfall [ 10 , 14 ] and wind speed [ 9 , 10 , 13 ] may also indirectly influence seabird foraging by altering the distribution and availability of prey resources. Understanding these complex interactions between climate and seabird foraging behavior is important for future conservation and population management strategies. Rain may directly constrain seabird flight and foraging activity by increasing flight costs [ 15 ] and reducing prey visibility [ 16 ]. For instance, in magnificent frigatebirds Fregata magnificens , foraging time decreased, while time spent at roosts increased with higher rainfall [ 11 ]. Similarly, in Cape gannets Morus capensis , moderate rainfall increased the time spent foraging regardless of breeding period, likely due to impaired foraging conditions resulting from water turbidity caused by rain [ 14 ]. Conversely, rainfall may indirectly enhance seabird foraging efficiency by influencing the distribution and abundance of fish, which constitute the primary prey of seabirds. For example, mulloway Argyrosomus japonicus , which inhabit estuarine environments, tend to move into shallower waters following rain events [ 17 ]. Moreover, annual catches of mullet Mugil cephalus and barramundi Lates calcarifer have been reported to increase with greater rainfall [ 18 ]. Given these contrasting effects, a comprehensive understanding of how rainfall influences seabird foraging behavior requires consideration of both its direct and indirect pathways. While various studies have explored the relationship between rainfall and animal behavior, less attention has been paid to how rainfall influences spatial consistency in foraging. Individual Foraging Site Fidelity (IFSF) refers to the behavioral specialization in which animals repeatedly utilize the same foraging sites. In breeding seabirds, the degree of IFSF can vary depending on environmental conditions [ 19 , 20 ]. This phenomenon can be explained by the win-stay lose-switch strategy [ 21 – 23 ]. Specifically, in environments with a high probability of prey acquisition, individuals that successfully obtain prey are likely to revisit the same foraging sites (win-stay). In contrast, in environments with a low probability of prey acquisition, individuals are more likely to abandon previously visited foraging sites in favor of new ones (lose-switch). Consequently, rain-induced changes in prey distribution and foraging efficiency may influence IFSF, which reflects foraging patterns over the course of the breeding season. Furthermore, rainfall at breeding colonies has been reported to affect chick condition, resulting in reduced chick growth rates [ 24 , 25 ]. On the other hand, because foraging time and IFSF have been linked to breeding success [ 26 – 29 ], rainfall-induced changes in parental foraging behavior may indirectly influence chick growth. Indeed, in blue-footed boobies Sula nebouxii , years with heavier rainfall during the egg-laying period are associated with higher chick fledging rates, likely due to improved foraging conditions caused by rainfall [ 30 ]. Therefore, to comprehensively understand the impacts of rainfall on seabirds, it is necessary to examine its effects on behavior across multiple spatiotemporal scales—from fine-scale behavioral states, through meso-scale foraging time, to broad-scale behavioral specialization such as IFSF, as well as on outcomes such as chick growth. In addition to spatial and temporal scales, an often overlooked aspect in studies of rainfall and animal behavior is that rainfall can be categorized into two distinct types: convective and large-scale rainfall (Supplementary Fig. 1). Large-scale rain typically originates from nimbostratus clouds, while convective rain is generated by cumulonimbus clouds [ 31 ]. Convective rain is generally more localized and shorter-lived than large-scale rain [ 32 , 33 ], suggesting that these two types of rain may exert distinct influences on foraging behavior across different spatiotemporal scales. Furthermore, temperature increases driven by global warming are expected to increase the frequency and intensity of convective rainfall in the future [ 34 – 36 ]. Therefore, to anticipate the potential impacts of future shifts in rainfall patterns on seabird behavior, it is necessary to focus on these two rain types and compare their respective effects on foraging behavior. Breeding streaked shearwaters Calonectris leucomelas commute between their breeding colonies and the sea to forage for prey, returning to the colony for incubation and chick provisioning. These round-trip movements are referred to as foraging trips, during which they remain at sea without landing or seeking shelter from rain. In addition, because streaked shearwaters are burrow-nesting seabirds, the direct effects of rain on chicks are likely to be negligible, while indirect effects mediated through parental foraging behavior are considered more substantial. In this study, we investigated how three aspects of foraging behavior at different spatial and temporal scales, as well as the resulting chick growth rates, were related to rainfall. Our analysis focused on 311 individuals during the chick-rearing period over a 14-year span. Given that the foraging behaviors of streaked shearwaters may also be influenced by other climatic factors—and that these factors may not be independent—we included additional climatic factors such as cloud cover and wind speed in the analysis. Specifically, we examined the relationships between climatic factors and: (1) fine-scale behavioral states at 5-minute intervals during foraging trips, (2) meso-scale trip parameters such as maximum distance and trip duration, (3) broad-scale behavioral specialization quantified as IFSF across the breeding period, and (4) chick growth rate reflecting the cumulative outcomes of foraging behavior. We first assessed the effects of climate on fine-scale behavioral states, followed by analyses of meso-scale trip parameters, broad-scale behavioral specialization, and chick growth. Methods GPS tracking During the chick-rearing period, we attached GPS loggers to streaked shearwaters breeding on Awashima Island (38°27′N, 139°14′E), Niigata, Japan, over a 14-year span from 2011 to 2024 (Supplementary Table 1). The GPS loggers used were GiPSy-2 (37 × 16 × 4 mm), GiPSy-4 (37 × 19 × 6 mm), and Axy-Trek (55 × 25 × 11 mm; 25 g), manufactured by TechnoSmart (Rome, Italy), as well as PinPoint VHF (body size excluding antenna: 38 × 32 × 14 mm; 20 g) and PinPoint VHF with solar panels (body size excluding antenna: 82 × 25 × 27 mm; 18 g), manufactured by Lotek Wireless (Ontario, Canada). These loggers were attached to the birds’ backs using waterproof tape (Tesa tape; Beiersdorf AG, Hamburg, Germany) and adhesive (Henkel Loctite Adhesives Ltd., Hatfield, U.K.), or by harnesses using Teflon ribbon (TH-25; width: 6 mm; Bally Ribbon Mills, Bally, PA, U.S.A.). GiPSy-2, GiPSy-4, and some Axy-Trek loggers were waterproofed with heat-shrink tubing. The weight of each attached logger was less than 5% of the average body mass of the individuals (536.21 ± 90.3 g), remaining within the threshold considered to have negligible effects on behavior and breeding of streaked shearwaters [ 37 , 38 ]. GPS recording periods varied by year and logger type but generally spanned from mid-August to early November. Climate Data In this study, we focused on cloud cover, rainfall, and wind speed, which have previously been identified as key factors influencing seabird foraging behavior. Climate data were obtained from the ECMWF ERA5 dataset [ 39 ]. Four types of variables were used in the analysis. Cloud cover—defined as the proportion of clouds in the upper troposphere within a single grid cell—was used as an indicator of cloud cover. Rainfall was represented by two components: convective precipitation, the accumulated precipitation generated by convective scheme, and large-scale precipitation, the accumulation generated by cloud scheme. Wind speed was represented by the magnitude of the vector sum of the eastward and northward wind components (m/s), measured at 10 m above sea level. Statistical Analysis Statistical analyses were conducted using R software version 4.2.0 [ 40 ]. All models examining the relationships between variables were fitted using the brms package [ 41 ] or the rstan package [ 42 ]. We used uninformative priors and ran four independent chains. Each chain consisted of 4,000 iterations, with 2,000 warm-up samples. We considered the fixed effects and their interactions as statistically relevant if the 95% credible interval of the posterior distribution did not include 0. Model convergence was confirmed, as R-hat was less than 1.1 for all models. To assess multicollinearity among the explanatory variables, we computed a correlation matrix for all models (Supplementary Table 2). All pairs of variables showed Pearson correlation coefficients less than 0.7, therefore, multicollinearity was considered negligible [ 43 ]. Relationship between climate and fine-scale behavioral states Because GPS recording intervals varied depending on logger type, all GPS data were resampled at 5-minute intervals. The resampled data were divided into foraging trips based on the following criteria: a foraging trip was defined as a continuous movement in which an individual traveled more than 3 km away from the colony for a duration of at least 6 hours before returning to the breeding site [ 37 ]. Trips in which recording was interrupted due to battery depletion or other issues after the individual had moved more than 3 km from the colony were excluded from the analysis. In addition, because chick-feeding frequency declines after early October (Supplementary Fig. 2), foraging trips occurring in October or later were also excluded. Behavioral states and transition probabilities during foraging trips were estimated using Hidden Markov Models (HMMs) (Supplementary Fig. 3), implemented with the fitHMM function from the momentuHMM package [ 44 ]. The behavioral states of streaked shearwaters were classified into three categories based on step length and turning angle: points with large step lengths and small turning angles were classified as traveling; those with short step lengths and large turning angles as foraging, and those with short step length and small turning angles as resting. To examine the effects of climate on fine-scale behavioral states during foraging trips, we fitted HMMs with climatic variables as covariates. For all climate data, we used values from the point with the shortest linear distance and the closest timestamp to each GPS point as covariates. The analysis was restricted to foraging trips recorded between 2020 and 2024 (N = 808 trips). If climatic variables influence either behavioral states or transition probabilities, models including these covariates are expected to show improved fit [ 12 , 45 , 46 ]. Therefore, we compared the Akaike Information Criterion (AIC) across all possible combinations of climatic covariates and selected the model with the lowest AIC as the best-fitting model. A covariate was considered to have a statistically significant effect on transition probabilities when the 95% confidence intervals of its beta-scale parameters did not include zero [ 47 ]. Finally, we visualized the covariate effect on the relative use of behavioral states by plotting stationary state probability distributions (i.e. the equilibrium probability of occupying each state) as a function of each covariate [ 47 ]. Relationship between climate and meso-scale trip parameters Because ERA5 climate data are provided as hourly values on a 0.25° × 0.25° grid, it was necessary to rescale both the spatial and temporal dimensions of the data to match the scale of the foraging trips. For spatial scaling, we first extracted climate data within the 50% kernel utilization distributions (UDs) of all foraging trips over the ocean (Supplementary Fig. 4) and averaged the values across this area to obtain a single value per hour. For temporal scaling, we extracted climate data from the time of departure for each foraging trip until 24 hours later and averaged them over that period. The climate data processed in this manner were used as climatic parameters and candidate explanatory variables in the subsequent analysis. For all foraging trips, we calculated maximum distance and trip duration as indicators of foraging trip characteristics (N = 2,645 trips; Supplementary Fig. 5). Note that maximum distance represents the straight-line distance between the breeding site and the farthest point from the breeding site in each foraging trip. The geosphere package [ 48 ] was used to calculate distances between GPS points. When examining the relationships between climatic and trip parameters, it is important to note that a positive correlation is expected between maximum distance and trip duration. This is because trip duration includes, at a minimum, the time required for a round-trip between the furthest point reached and the breeding site (direct round-trip travel time), which is proportional to the maximum distances, assuming a constant flight speed. The actual trip duration also includes additional time components, including deviations from the direct path and time allocated to foraging activities and landing on water. Therefore, trip duration can be regarded as the sum of a distance-dependent component and other time expenditures. Consequently, the influence of climatic factors on trip duration can be conceptualized as having two distinct pathways: (1) an indirect effect mediated by maximum distance, which determines the direct round-trip travel time, (2) a direct effect on the remaining time components that are independent of travel distance. To account for this structure, we constructed Bayesian models with maximum distance and trip duration as response variables, assuming that climatic parameters influence both variables, and that maximum distance also affects trip duration. The specific model equations are presented below. For the n th trip, let \(\:{md}_{n}\) denote the observed maximum distance and \(\:{td}_{n}\) denote the observed trip duration. The model assumes: The linear predictors are defined as: $$\:{\mu\:}_{{md},n}={a}_{md}+\sum\:_{i}{b}_{md,i}{X}_{i,n}+{u}_{md,ID\left[n\right]}+{u}_{md,year\left[n\right]}$$ $$\:{\mu\:}_{{td},n}={a}_{td}+\sum\:_{j}{b}_{td,i}{X}_{j,n}+{b}_{td,j}\text{log}\left({md}_{n}\right)+{u}_{td,ID\left[n\right]}+{u}_{td,year\left[n\right]}$$ The random intercepts are modeled as: The log-normal distribution \(\:LogNormal(\mu\:,\sigma\:)\) is defined as: $$\:LogNormal\left(x|\mu\:,\sigma\:\right)=\frac{1}{\sqrt{2\pi\:\sigma\:x}}\text{e}\text{x}\text{p}\left(-\frac{{\left(\text{ln}x-\mu\:\right)}^{2}}{2{\sigma\:}^{2}}\right)$$ In this model, \(\:{u}_{md,ID\left[n\right]}\) and \(\:{u}_{td,ID\left[n\right]}\) represent individual-specific random intercepts for maximum distance and trip duration, respectively, corresponding to the individual ID associated with observation n , Similarly, \(\:{u}_{md,year\left[n\right]}\) and \(\:{u}_{td,year\left[n\right]}\) represent year-specific random intercepts for maximum distance and trip duration, respectively, corresponding to the year in which observation n was recorded. The predictors \(\:{X}_{i,n}\) and \(\:{X}_{j,n}\) denote the values of climatic variables for observation n , where: $$\:i,j\in\:\left\{Cloud,Convective\:Rainfall,\:Large\:Scale\:Rainfall,Wind\:Speed\right\}$$ The coefficients \(\:{b}_{md,i}\) represent the effects of climatic variable i on maximum distance, while \(\:{b}_{td,j}\) capture the effects of climatic variable j on trip duration. All climatic variables were standardized using variable-specific scaling procedures. Cloud cover and wind speed were standardized using z-score normalization, while convective and large-scale rainfall were log(x + 1)-transformed and subsequently standardized using z-score normalization. We then constructed models for all combinations of explanatory variables and evaluated model fit using the Leave-One-Out Information Criterion (LOOIC), calculated with the loo function. The model with the lowest LOOIC was considered the best-fitting model with the highest predictive performance [ 49 ]. Based on the explanatory variables included in the models, we examined the climatic factors associated with foraging trip characteristics and the pathways through which they exert their effects. Relationship between climate and IFSF In this study, IFSF was calculated based on the degree of spatial overlap in foraging sites between foraging trips. First, to identify foraging sites, we performed state estimation by running a HMM without covariates for all foraging trips (Supplementary Fig. 6). Foraging sites were defined as the centroid coordinates of consecutive GPS points, beginning with the transition from the traveling state to the foraging state, continuing through successive foraging or resting states, and ending with the transition back to the traveling state. We then calculated the 50% kernel UDs of foraging sites for each foraging trip using fixed kernel density estimation implemented in the adehabitatHR package. We used Bhattacharyya’s Affinity (BA), which represents the similarity of UDs, as an indicator of IFSF [ 50 ]. BA ranges from 0 to 1, where values closer to 0 indicate lower similarity while values closer to 1 indicate higher similarity. For each individual, we calculated BA values for all pairs of foraging trips (e.g., an individual with four trips yielded six BA values) and IFSF was defined as the mean of these values. To ensure reliability, individuals with fewer than three recorded foraging trips per year were excluded from the analysis. Finally, to examine the effects of climate on IFSF, we constructed a Bayesian model with IFSF as the response variable, climatic parameters as explanatory variables, and ID and year (corresponding to when IFSF was calculated) included as random effects (N = 241 individuals). Climatic parameters were represented by the mean values calculated over the period between logger deployment and retrieval for each individual. Because IFSF is a continuous variable ranging from 0 to 1, we assumed a beta distribution and applied a logit link function in the model. Following the same model selection procedure used for trip parameters, we constructed models for all combinations of explanatory variables and identified the best-fitting model. Based on the results of the best-fitting model, we identified the climatic parameters that influence IFSF. Since the accuracy of IFSF values may depend on the number of foraging trips used in their calculation, the number of trips was included as an explanatory variable in all models. Relationship between climate and chick growth rate During the chick-rearing period from 2011 to 2024, chick body mass was measured every five days using a spring scale (Pesola AG, Baar, Switzerland) (Supplementary Fig. 2). Measurements began in mid-to-late August each year (between August 19 and 26), while the final measurement dates varied annually (ranging from September 29 to November 16). Chick growth rate was calculated as the slope of a linear regression of body mass over time for each chick [ 51 ]. This approach was appropriate because the peak body mass of streaked shearwater chicks occurs in October (Supplementary Fig. 2), allowing fluctuations in mass during the study period to be approximated linearly. To examine the relationship between climatic factors that affected foraging behavior and chick growth rate, we constructed a Bayesian model, assuming a normal distribution. In this model, the growth rate of each chick during August and September served as the response variable. The mean values of cloud cover, convective rainfall, large-scale rainfall, and wind speed within the 50% kernel UDs of foraging sites (Supplementary Fig. 4) during the same period were included as explanatory variables. Year was included as a random effect. Results Effects of climate on fine-scale behavioral states Based on model selection using AIC, the model including cloud cover, convective rainfall, and wind speed as covariates had the lowest AIC (Table 1 ). According to the beta-scale parameter estimates and their 95% confidence intervals for the transition probabilities between behavioral states in this model (Supplementary Table 3), increased cloud cover was associated with decreased transition probabilities from traveling to foraging (Est. = -0.083; 95% CI = -0.132 to -0.034), from foraging to resting (Est. = -0.061; 95% CI = -0.113 to -0.009), and from resting to foraging (Est. = -0.052; 95% CI = -0.102 to -0.002) (Fig. 1 a–c). Additionally, with increasing convective rainfall, the transition probabilities from traveling to foraging increased (Est. = 0.071; 95% CI = 0.021 to 0.121), whereas that from foraging to resting decreased (Est. = -0.07; 95% CI = -0.129 to -0.01) (Fig. 1 d, e). Finally, with increasing wind speed, the transition probabilities from foraging to traveling increased (Est. = 0.017; 95% CI = 0.01 to 0.024), while that from resting to foraging decreased (Est. = -0.026; 95% CI = -0.033 to -0.018) (Fig. 1 f, g). According to the stationary probability distributions, an increase in convective rainfall led to a higher proportion in the foraging state and a lower proportion in the resting state (Fig. 2 b). In addition, increasing wind speed was associated with a higher proportion in the traveling state and a lower proportion in the foraging state (Fig. 2 d). Little or no clear changes in state proportions were observed in response to cloud cover (Fig. 2 a). Transition probabilities from traveling to resting or from resting to traveling were extremely low (< 0.0001), so we did not focus on these in this analysis (Supplementary Table 4). Table 1 AIC comparison of a set of candidate Hidden Markov Models to evaluate the effects of climatic factors on behavioral states. AIC was calculated for each model, and their differences (ΔAIC) from the best-fitting model are shown. Variables AIC ΔAIC C + CR + WS 7828729 0 C + CR + LSR + WS 7828730 1 C + WS 7828733 4 CR + LSR + WS 7828736 7 CR + WS 7828737 8 WS 7828740 11 C + LSR + WS 7828740 11 LSR + WS 7828746 17 C + CR + LSR 7828790 61 C + CR 7828793 64 CR + LSR 7828795 66 C 7828798 69 CR 7828800 70 C + LSR 7828801 71 Null 7828805 75 LSR 7828806 76 C: Cloud, CR: Convective Rainfall, LSR: Large-Scale Rainfall, WS: Wind Speed Effects of climate and meso-scale trip parameters Model selection based on the LOOIC identified the best-fitting model for maximum distance as the one including cloud cover and wind speed, and for trip duration as the one including convective rainfall and wind speed as explanatory variables (Table 2 ). When the combination of explanatory variables was applied to the models, we found that maximum distance increased with greater cloud cover and stronger wind speed, and trip duration increased with stronger convective rainfall (Table 3 ; Figs. 3 and 4 ). Table 2 Comparison of the top five models with the lowest LOOIC values for evaluating the effects of climatic factors on maximum distance, trip duration, and IFSF, based on different combinations of candidate climate variables as explanatory variables. LOOIC values were calculated for each model, and their differences (ΔLOOIC) from the best-fitting model are shown. Model Variables LOOIC ΔLOOIC Maximum Distance C + WS 31542.46 0 LSR + WS 31542.79 0.33 C + LSR + WS 31543.39 0.94 CR + LSR + WS 31543.65 1.19 WS 31544.83 2.37 Trip Duration CR + WS 21476.11 0 LSR + WS 21476.47 0.36 CR + LSR + WS 21478.36 2.25 C + LSR + WS 21478.64 2.53 WS 21478.99 2.88 IFSF CR + LSR -457.54 0 C + CR + LSR -456.22 1.32 CR + LSR + WS -455.80 1.74 C + CR + LSR + WS -454.56 2.98 LSR -450.22 7.32 C: Cloud, CR: Convective Rainfall, LSR: Large-Scale Rainfall, WS: Wind Speed Table 3 Estimation results from the best-fitting models with maximum distance, trip duration, and IFSF as response variables, as well as the model with chick growth rate as the response variable. Model Variables Estimate ± SE Lower-95% CI Upper-95% CI Maximum Distance Intercept 4.62 ± 0.09 4.44 4.80 Cloud 0.04 ± 0.02 0.00 0.08 Wind Speed 0.05 ± 0.02 0.01 0.09 Trip Duration Intercept 0.03 ± 0.06 -0.09 0.15 Convective Rainfall 0.02 ± 0.01 0.00 0.04 Wind Speed -0.02 ± 0.01 -0.04 0.00 Maximum Distance 0.75 ± 0.01 0.73 0.76 IFSF Intercept 0.11 ± 0.04 0.02 0.19 Num. of Trips 0.04 ± 0.03 -0.02 0.09 Convective Rainfall 0.11 ± 0.04 0.04 0.18 Large-Scale Rainfall -0.13 ± 0.04 -0.20 -0.05 Chick Growth Rate Intercept 12.47 ± 0.47 11.50 13.36 Cloud 0.12 ± 0.8 -1.50 1.69 Convective Rainfall 0.49 ± 0.73 -0.93 1.97 Large-Scale Rainfall 0.05 ± 0.57 -1.09 1.18 Wind Speed -0.47 ± 0.6 -1.68 0.75 SE means standard errors and CI means 95% credible intervals. Bold indicates significant variables Effects of climate on broad-scale behavioral specialization Model selection based on the LOOIC, with IFSF as the response variable, identified the model including convective and large-scale rainfall as explanatory variables as the best-fitting model (Table 2 ). This model indicated that IFSF tended to increase with higher convective rainfall and decrease with higher large-scale rainfall (Table 3 , Fig. 5 ). Effects of climate on chick growth Results from the model including all candidate climatic parameters as explanatory variables showed that climatic conditions within the 50% kernel UDs of foraging sites were not associated with chick growth rate (Table 3 ). Discussion Foraging behavior and rainfall The increase in convective rainfall particularly led to higher transition probabilities from traveling to foraging states, as well as higher stationary probabilities of the foraging states. This pattern may be explained by the emergence of high-quality foraging sites following rain. For example, local decreases in salinity caused by rain have been reported to promote the abundance and reproductive activity of Japanese anchovy Engraulis japonicus [ 3 , 52 ], a major prey species of streaked shearwaters breeding on Awashima Island [ 53 ]. In addition, numerous previous studies have shown that freshwater input from rivers associated with rainfall enhances marine productivity [ 18 , 54 , 55 ], and streaked shearwaters have been observed foraging at river outflow areas at sea [ 56 ]. Therefore, foraging activity may have increased in areas and periods with elevated rainfall. It is important to note that foraging states estimated by the HMMs are based on GPS-derived movement patterns—specifically, slow and highly sinuous flight—and do not necessarily represent actual foraging behavior such as prey ingestion. Because rainfall increases flight costs in birds [ 15 ], this pattern may instead reflect restricted flight behavior during rain. Indeed, previous studies have shown that the proportion of slow flight increases during rain in magnificent frigatebirds [ 11 ], and that landing rates increase in albatrosses under rainy conditions [ 10 ]. These findings suggest that seabirds tend to avoid direct flight during rain. On the other hand, convective rainfall may prompt seabirds to actively adjust their foraging behavior in response to changing environmental conditions. Increased convective rainfall was associated with reduced transition probabilities from foraging to resting states and lower stationary probabilities of the resting states. This pattern can be interpreted through the lens of the trade-off between time and energy. The ability to regulate energy expenditure during foraging is considered to affect individual fitness [ 57 , 58 ], and animals are assumed to behave in ways that maximize the ratio of energy gained to energy expended during foraging [ 59 ]. Given that foraging efficiency may improve during convective rain, seabirds may actively reduce the amount of time spent resting and allocate more time to foraging. In support of this, convective rain increased trip duration regardless of the round-trip distance to the furthest point reached. This result likely reflects that seabirds spent more time actively foraging during convective rain, as suggested by changes in behavioral states. Consequently, climate-induced changes in fine-scale behavioral states may cascade upward to influence broader-scale behavior, such as foraging trip characteristics. IFSF decreased with increasing large-scale rainfall, suggesting that such rain may negatively affect seabird foraging behavior. Previous studies on the effects of rainfall on seabird foraging behavior have reported reduced flight speeds and foraging efficiency during rain [ 10 , 11 , 14 ], which may be attributed to increased flight costs and reduced underwater visibility. Large-scale rain is characterized by its persistence and broad spatial extent compared to convective rain [ 31 , 33 , 60 ], and is therefore likely to exert prolonged constraints on seabird foraging behavior. Accordingly, the observed decline in IFSF may reflect shifts in foraging sites due to foraging failures or altered flight costs under large-scale rain. In contrast, increased convective rainfall was associated with higher IFSF. According to the win-stay lose-switch strategy, individuals are expected to revisit previously successful foraging sites, thereby increasing foraging site fidelity [ 23 ]. This result may indicate that the negative effects of convective rainfall on foraging and flight are relatively short-term, and that the benefits associated with the appearance of high-quality foraging sites are more strongly reflected in foraging behavior. Thus, the contrasting effects of large-scale and convective rainfall on IFSF are likely due to differences in their temporal and spatial characteristics. Foraging behavior and cloud cover Increased cloud cover was associated with reduced transition probabilities from traveling to foraging, from resting to foraging, and from foraging to resting states. This pattern may reflect a decline in foraging efficiency under overcast conditions, leading to a decrease in behavioral transitions associated with prey search and capture. In Manx shearwaters Puffinus puffinus , dive depth was greatest during clear weather and shallower under dense cloud cover [ 12 ]. Similarly, in Eastern kingbirds Tyrannus tyranus , a terrestrial insectivorous bird, predation rates declined under overcast conditions [ 61 ]. Furthermore, fish school cohesion has been shown to decrease under shade [ 62 ], and such irregular fish movements during overcast conditions may make fish capture more difficult. These findings suggest that reduced prey visibility under cloud cover may impair prey detection and capture efficiency. In addition, the observed increase in maximum distance associated with greater cloud cover may indicate a behavioral tendency among seabirds to avoid cloudy areas, potentially as a response to decreased foraging efficiency under overcast skies. Collectively, these results suggest that fine-scale climatic effects on seabirds can influence their decision-making at a broader behavioral scale. Foraging behavior and wind speed This study revealed that transition probabilities from foraging to traveling states, as well as stationary probabilities of the traveling state, also increased with rising wind speed. This finding may indicate that improved flight efficiency under strong wind conditions promotes both the transition to and maintenance of traveling behavior. In both Manx shearwaters and wandering albatrosses Diomedea exulans , individuals have been shown to shift from flapping to soaring flight under strong winds [ 46 , 63 , 64 ], and increased flight speeds with higher wind speeds have been reported in albatrosses [ 65 , 66 ]. These findings suggest that increasing wind speed facilitates more energy-efficient flight in seabirds. Moreover, maximum distance tended to increase with higher wind speeds. This pattern may also reflect the facilitation of longer-distance travel resulting from enhanced flight efficiency. In Procellariiforme seabirds during the chick-rearing period, foraging in more distant oceanic areas has been associated with improvements in parental body mass [ 67 , 68 ]. Therefore, parents may choose to undertake longer trips under strong wind conditions to improve their own body condition, taking advantage of enhanced flight efficiency. Consequently, it is suggested that changes in flight efficiency driven by wind speed may affect foraging behaviors across multiple scales, from fine-scale behavioral states to meso-scale trip parameters. In contrast, increased wind speed was associated with decreased transition probabilities from foraging to resting states and lower stationary probabilities of the foraging states. This pattern may indicate that reduced foraging efficiency due to strong winds leads to decreases in the transition probabilities from slow flight for prey capture to landing on water, as well as in the probabilities of engaging in prey search behavior. For example, in wandering albatrosses, strong winds reduced feeding rates, likely due to decreased prey visibility caused by sea surface disturbance [ 10 ]. Similarly, in little penguins Eudyptula minor , strong winds deepen the mixed layer, thereby reducing prey encounter rates. Additionally, reduced water column stratification caused by strong winds lowers fish productivity, potentially reducing breeding success rates in kittiwakes [ 13 ]. Because streaked shearwaters forage at relatively shallow depths (approximately < 6 m) [ 69 , 70 ], sea surface disturbance and deepened mixed layers caused by strong winds may reduce prey visibility and availability, resulting in decreased foraging efficiency. Therefore, while increased wind speed may enhance flight efficiency, it may simultaneously impair foraging efficiency. These findings highlight that the effects of climate on seabird behavior are not unidirectional and underscore the need for a multifaceted perspective to fully understand their ecological implications. Chick growth and climate Although climate had various effects on foraging behavior across different spatiotemporal scales, no significant association was observed between climatic conditions and chick growth rate—an outcome reflecting overall foraging success. This finding suggests that the effects of climate on foraging behavior may be sufficiently minor to be compensated for by parental effort. In other words, when climatic conditions have negative effects, parent birds may strive to feed their chick sufficiently, even at the expense of their own condition, whereas under favorable conditions, they may prioritize improving their own condition rather than investing in chick growth. Therefore, climate-induced changes in foraging behavior may not have translated into measurable effects on chick growth. However, global warming is expected to increase the frequency and intensity of extreme weather events, such as heavy rainfall and storms [ 71 , 72 ]. It is therefore necessary to consider the possibility that future climate change may exceed the compensatory capacity of parent birds, potentially affecting breeding performance and individual fitness. Conclusion In this study, we examined the effects of climate on the foraging behavior of streaked shearwaters across multiple spatiotemporal scales—from fine-scale behavioral states, through meso-scale trip parameters, to broad-scale behavioral specialization. The results revealed that fine-scale behavioral states during foraging trips and meso-scale trip parameters were influenced by cloud cover, convective rainfall, and wind speed, while broad-scale behavioral specialization was affected by convective and large-scale rainfall. These findings demonstrate that climatic factors influence the foraging behavior of streaked shearwaters differently across spatial and temporal scales, with convective rainfall emerging as particularly important, as it influenced all aspects of foraging behavior: behavioral states, trip parameters, and IFSF. This suggests that climate-induced changes in foraging behavior at smaller spatiotemporal scales may cascade into changes at broader scales. This study contributes to a comprehensive understanding of climate-induced changes in seabird foraging behavior by focusing on behavior across multiple spatiotemporal scales, and underscores the need to account for scale-dependent behavioral responses when predicting the ecological impacts of future climate change. Abbreviations AIC Akaike’s Information Criterion BA Bhattacharyya’s Affinity HMM Hidden Markov Model IFSF Individual Foraging Site Fidelity LOOIC Leave-One-Out Information Criterion UD Utilization Distribution Declarations Ethics approval and consent to participate All experiments were approved by the Animal Experimental Committee of Nagoya University (GSES 2011–2024) and the Awashimaura Village Office, Niigata Prefecture, Japan. Consent for publication Not applicable. Competing interests The authors declare no conflict of interest. Funding This study was funded by the Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (24681006, 16H01769, 16H06541, 21H05294, 22H00569 to K.Y.), and by Core Research for Evolutional Science and Technology and Support for Pioneering Research Initiated by the Next Generation from the Japan Science and Technology Agency (JPMJCR23P2 to K.Y. and JPMJSP2125 to W.T). Author Contribution W.T. and K.Y. conceived the ideas and designed methodology; W.T. and S.K. collected the data; W.T. and Y.G. analyzed the data; All authors contributed to writing of the manuscript. All authors gave final approval for publication and agreed to be held accountable for the work performed therein. Acknowledgement The author (W.T.) would like to take this opportunity to thank the “Interdisciplinary Frontier Next-Generation Researcher Program of the Tokai Higher Education and Research System.” We would also like to thank our colleagues who contributed to the fieldwork. Data Availability The data from this study are available in Movebank (https://www.movebank.org/cms/movebank-main) and Biologging intelligent Platform (BiP; https://www.bip-earth.com/ja). The Movebank study ID of our data is 3240100863. References Jenkins S, Povey A, Gettelman A, Grainger R, Stier P, Allen M. Is anthropogenic global warming accelerating? J Climate 2022;35:7873–7890. Long S-M, Xie S-P, Zheng X-T, Liu Q. Fast and slow responses to global warming: Sea surface temperature and precipitation patterns. J Climate 2014;27:285–299. Lee S-J, Go Y-B. Study on the distribution patterns of anchovy eggs and larvae and environmental characteristics in the eastern part of Jeju strait, Korea. Korean J Ichthyol 2006;18:36–44. Salarieh B, Ugwu IA, Salman AM. Impact of changes in sea surface temperature due to climate change on hurricane wind and storm surge hazards across US Atlantic and Gulf coast regions. SN Appl Sci 2023;5:205. Schreiber EA. Climate and weather effects on seabirds. 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Supplementary Files climatesupplementaryME.docx Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Movement Ecology → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 21 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviews received at journal 17 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 26 Jun, 2025 Submission checks completed at journal 26 Jun, 2025 First submitted to journal 25 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6970356","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483967016,"identity":"6ccd9de3-2fcc-49b1-bd5b-5c1d1db2aab7","order_by":0,"name":"Wataru Takeda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACAxiDjZmx/cMHEIOdSC0SfOzMxxhngPUSq0WOny2NmQfEJKTFnL332GfeNoY6NmYes8c2v7bJ8zEzMH74mINbi2XPueTZQC0SQC3mxrl9tw3bmBmYJWduw+OwGznGzLzbwFoMpHN7bjMCtbCBRHBruf8GSYtlz217wlpu8MC0sKVJM/y4nUhYy5kcY8a5/yQk25iZDxv2NtxObmNmbMbvl+NnjBnenLHhl+8/2Pjgx5/btvPbmw9++IhHCwgw8TBIQFiMbWCyAb96kJIfcOYfgopHwSgYBaNgBAIALotE2/LFb7UAAAAASUVORK5CYII=","orcid":"","institution":"Nagoya University","correspondingAuthor":true,"prefix":"","firstName":"Wataru","middleName":"","lastName":"Takeda","suffix":""},{"id":483967017,"identity":"b7058a54-7ebc-416d-a53a-fb7dea035fbd","order_by":1,"name":"Shiho Koyama","email":"","orcid":"","institution":"Nagoya University","correspondingAuthor":false,"prefix":"","firstName":"Shiho","middleName":"","lastName":"Koyama","suffix":""},{"id":483967018,"identity":"7218f8ff-6949-4d8c-bb74-6bde353ce8f1","order_by":2,"name":"Yusuke Goto","email":"","orcid":"","institution":"Nagoya University","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Goto","suffix":""},{"id":483967019,"identity":"319d81b7-c765-4d70-b4ff-de45ee0bab84","order_by":3,"name":"Ken Yoda","email":"","orcid":"","institution":"Nagoya University","correspondingAuthor":false,"prefix":"","firstName":"Ken","middleName":"","lastName":"Yoda","suffix":""}],"badges":[],"createdAt":"2025-06-25 04:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6970356/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6970356/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40462-026-00648-8","type":"published","date":"2026-04-14T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86632119,"identity":"177d2eef-8206-44f4-ab8d-883ffa6a2d7a","added_by":"auto","created_at":"2025-07-14 06:32:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329135,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between covariates and state transition probabilities estimated by Hidden Markov Models including cloud cover, convective rainfall, and wind speed as covariates. Estimated coefficients are shown as solid lines, and 95% confidence intervals are shown as shaded areas. Cloud cover was associated with transition probabilities from traveling to foraging (a), from foraging to resting (b), and from resting to foraging states (c). Convective rainfall was associated with transition probabilities from traveling to foraging (d) and from foraging to resting states (e). Wind speed was associated with transition probabilities from foraging to traveling (f) and from foraging to resting states (g).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6970356/v1/073074cb7c8b2bb2c931baf0.png"},{"id":86632862,"identity":"791af20e-e620-4a15-8809-a0f6d77ac1e5","added_by":"auto","created_at":"2025-07-14 06:40:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235304,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in stationary probabilities of the three states (blue = traveling; red = foraging; green = resting) in response to cloud cover, convective rainfall, and wind speed. Estimated coefficients are shown as solid lines, and 95% confidence intervals are shown as shaded areas. No clear relationship was observed between cloud cover and stationary probabilities (a). In contrast, convective rainfall was associated with increased stationary probabilities of the foraging states and decreased probabilities of the resting states (b), while wind speed was associated with increased probabilities of the traveling state and decreased probabilities of the foraging state (c).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6970356/v1/e01ba755f723171e9b3417e2.png"},{"id":86632118,"identity":"6e5ffc03-ad6f-4396-9f79-8ff0e42c27ea","added_by":"auto","created_at":"2025-07-14 06:32:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69374,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram of hypothesized relationships between climatic factors and trip parameters. Climatic factors are expected to independently affect both maximum distance and trip duration, while trip duration is also influenced by maximum distance. The best-fitting model for maximum distance included cloud cover and wind speed as explanatory variables, both of which showed positive effects. In contrast, the best-fitting model for trip duration included convective rainfall and wind speed; convective rainfall had a positive effect, whereas wind speed did not show a significant effect.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6970356/v1/64fcf5603abb1390b847ef65.png"},{"id":86632122,"identity":"a2d3213e-5fbd-487c-afba-4c498b897f68","added_by":"auto","created_at":"2025-07-14 06:32:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":318894,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between log-transformed maximum distance and both mean wind speed and cloud cover, as well as between log-transformed trip duration and mean convective rainfall. Estimated coefficients are shown as solid lines, and 95% confidence intervals are shown as shaded areas. Points are color-coded by year. Maximum distance tended to increase with greater cloud cover (a) and stronger wind speed (b), while trip duration tended to increase with higher convective rainfall (c).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6970356/v1/98407c591f569d9d6cb86316.png"},{"id":86632865,"identity":"1c137109-2ad1-4289-b22b-dcce48cc175d","added_by":"auto","created_at":"2025-07-14 06:40:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":166186,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between IFSF and mean convective and mean large-scale rainfall. Estimated coefficients are shown as solid lines, and 95% credible intervals are shown as shaded areas. Points are color-coded by year. IFSF tended to increase with higher convective rainfall (a) and decrease with higher large-scale rainfall (b).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6970356/v1/6196b6d68d9c27a8cee34dda.png"},{"id":107350717,"identity":"cbf8b36f-a40b-480f-9189-073a97d0d16d","added_by":"auto","created_at":"2026-04-20 16:01:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1684952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6970356/v1/01310a23-7155-4a37-8096-7df8cc04391e.pdf"},{"id":86632121,"identity":"06806933-996b-4f2e-8bee-9adeea1d52c3","added_by":"auto","created_at":"2025-07-14 06:32:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1298108,"visible":true,"origin":"","legend":"","description":"","filename":"climatesupplementaryME.docx","url":"https://assets-eu.researchsquare.com/files/rs-6970356/v1/38d038bc1dc7598686742eea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Convective rainfall drives behavioral shifts across multiple foraging scales in breeding seabirds","fulltext":[{"header":"Background","content":"\u003cp\u003eIn recent years, rapid temperature increases driven by anthropogenic activities have been observed globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], significantly altering marine climatic conditions, particularly in patterns of cloud cover, rainfall, and wind speed [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As top predators in marine ecosystems, seabirds are especially sensitive to climatic variability [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Specifically, climatic factors such as cloud cover [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], rainfall [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and wind speed [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] can directly constrain seabird foraging behavior. In addition to these direct effects, rainfall [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and wind speed [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] may also indirectly influence seabird foraging by altering the distribution and availability of prey resources. Understanding these complex interactions between climate and seabird foraging behavior is important for future conservation and population management strategies.\u003c/p\u003e\u003cp\u003eRain may directly constrain seabird flight and foraging activity by increasing flight costs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and reducing prey visibility [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For instance, in magnificent frigatebirds \u003cem\u003eFregata magnificens\u003c/em\u003e, foraging time decreased, while time spent at roosts increased with higher rainfall [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, in Cape gannets \u003cem\u003eMorus capensis\u003c/em\u003e, moderate rainfall increased the time spent foraging regardless of breeding period, likely due to impaired foraging conditions resulting from water turbidity caused by rain [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Conversely, rainfall may indirectly enhance seabird foraging efficiency by influencing the distribution and abundance of fish, which constitute the primary prey of seabirds. For example, mulloway \u003cem\u003eArgyrosomus japonicus\u003c/em\u003e, which inhabit estuarine environments, tend to move into shallower waters following rain events [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, annual catches of mullet \u003cem\u003eMugil cephalus\u003c/em\u003e and barramundi \u003cem\u003eLates calcarifer\u003c/em\u003e have been reported to increase with greater rainfall [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given these contrasting effects, a comprehensive understanding of how rainfall influences seabird foraging behavior requires consideration of both its direct and indirect pathways.\u003c/p\u003e\u003cp\u003eWhile various studies have explored the relationship between rainfall and animal behavior, less attention has been paid to how rainfall influences spatial consistency in foraging. Individual Foraging Site Fidelity (IFSF) refers to the behavioral specialization in which animals repeatedly utilize the same foraging sites. In breeding seabirds, the degree of IFSF can vary depending on environmental conditions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This phenomenon can be explained by the win-stay lose-switch strategy [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Specifically, in environments with a high probability of prey acquisition, individuals that successfully obtain prey are likely to revisit the same foraging sites (win-stay). In contrast, in environments with a low probability of prey acquisition, individuals are more likely to abandon previously visited foraging sites in favor of new ones (lose-switch). Consequently, rain-induced changes in prey distribution and foraging efficiency may influence IFSF, which reflects foraging patterns over the course of the breeding season.\u003c/p\u003e\u003cp\u003eFurthermore, rainfall at breeding colonies has been reported to affect chick condition, resulting in reduced chick growth rates [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. On the other hand, because foraging time and IFSF have been linked to breeding success [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], rainfall-induced changes in parental foraging behavior may indirectly influence chick growth. Indeed, in blue-footed boobies \u003cem\u003eSula nebouxii\u003c/em\u003e, years with heavier rainfall during the egg-laying period are associated with higher chick fledging rates, likely due to improved foraging conditions caused by rainfall [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, to comprehensively understand the impacts of rainfall on seabirds, it is necessary to examine its effects on behavior across multiple spatiotemporal scales\u0026mdash;from fine-scale behavioral states, through meso-scale foraging time, to broad-scale behavioral specialization such as IFSF, as well as on outcomes such as chick growth.\u003c/p\u003e\u003cp\u003eIn addition to spatial and temporal scales, an often overlooked aspect in studies of rainfall and animal behavior is that rainfall can be categorized into two distinct types: convective and large-scale rainfall (Supplementary Fig.\u0026nbsp;1). Large-scale rain typically originates from nimbostratus clouds, while convective rain is generated by cumulonimbus clouds [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Convective rain is generally more localized and shorter-lived than large-scale rain [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], suggesting that these two types of rain may exert distinct influences on foraging behavior across different spatiotemporal scales. Furthermore, temperature increases driven by global warming are expected to increase the frequency and intensity of convective rainfall in the future [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, to anticipate the potential impacts of future shifts in rainfall patterns on seabird behavior, it is necessary to focus on these two rain types and compare their respective effects on foraging behavior.\u003c/p\u003e\u003cp\u003eBreeding streaked shearwaters \u003cem\u003eCalonectris leucomelas\u003c/em\u003e commute between their breeding colonies and the sea to forage for prey, returning to the colony for incubation and chick provisioning. These round-trip movements are referred to as foraging trips, during which they remain at sea without landing or seeking shelter from rain. In addition, because streaked shearwaters are burrow-nesting seabirds, the direct effects of rain on chicks are likely to be negligible, while indirect effects mediated through parental foraging behavior are considered more substantial. In this study, we investigated how three aspects of foraging behavior at different spatial and temporal scales, as well as the resulting chick growth rates, were related to rainfall. Our analysis focused on 311 individuals during the chick-rearing period over a 14-year span. Given that the foraging behaviors of streaked shearwaters may also be influenced by other climatic factors\u0026mdash;and that these factors may not be independent\u0026mdash;we included additional climatic factors such as cloud cover and wind speed in the analysis. Specifically, we examined the relationships between climatic factors and: (1) fine-scale behavioral states at 5-minute intervals during foraging trips, (2) meso-scale trip parameters such as maximum distance and trip duration, (3) broad-scale behavioral specialization quantified as IFSF across the breeding period, and (4) chick growth rate reflecting the cumulative outcomes of foraging behavior. We first assessed the effects of climate on fine-scale behavioral states, followed by analyses of meso-scale trip parameters, broad-scale behavioral specialization, and chick growth.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eGPS tracking\u003c/p\u003e\u003cp\u003eDuring the chick-rearing period, we attached GPS loggers to streaked shearwaters breeding on Awashima Island (38\u0026deg;27\u0026prime;N, 139\u0026deg;14\u0026prime;E), Niigata, Japan, over a 14-year span from 2011 to 2024 (Supplementary Table\u0026nbsp;1). The GPS loggers used were GiPSy-2 (37 \u0026times; 16 \u0026times; 4 mm), GiPSy-4 (37 \u0026times; 19 \u0026times; 6 mm), and Axy-Trek (55 \u0026times; 25 \u0026times; 11 mm; 25 g), manufactured by TechnoSmart (Rome, Italy), as well as PinPoint VHF (body size excluding antenna: 38 \u0026times; 32 \u0026times; 14 mm; 20 g) and PinPoint VHF with solar panels (body size excluding antenna: 82 \u0026times; 25 \u0026times; 27 mm; 18 g), manufactured by Lotek Wireless (Ontario, Canada). These loggers were attached to the birds\u0026rsquo; backs using waterproof tape (Tesa tape; Beiersdorf AG, Hamburg, Germany) and adhesive (Henkel Loctite Adhesives Ltd., Hatfield, U.K.), or by harnesses using Teflon ribbon (TH-25; width: 6 mm; Bally Ribbon Mills, Bally, PA, U.S.A.). GiPSy-2, GiPSy-4, and some Axy-Trek loggers were waterproofed with heat-shrink tubing. The weight of each attached logger was less than 5% of the average body mass of the individuals (536.21\u0026thinsp;\u0026plusmn;\u0026thinsp;90.3 g), remaining within the threshold considered to have negligible effects on behavior and breeding of streaked shearwaters [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. GPS recording periods varied by year and logger type but generally spanned from mid-August to early November.\u003c/p\u003e\u003cp\u003eClimate Data\u003c/p\u003e\u003cp\u003eIn this study, we focused on cloud cover, rainfall, and wind speed, which have previously been identified as key factors influencing seabird foraging behavior. Climate data were obtained from the ECMWF ERA5 dataset [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Four types of variables were used in the analysis. Cloud cover\u0026mdash;defined as the proportion of clouds in the upper troposphere within a single grid cell\u0026mdash;was used as an indicator of cloud cover. Rainfall was represented by two components: convective precipitation, the accumulated precipitation generated by convective scheme, and large-scale precipitation, the accumulation generated by cloud scheme. Wind speed was represented by the magnitude of the vector sum of the eastward and northward wind components (m/s), measured at 10 m above sea level.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using R software version 4.2.0 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. All models examining the relationships between variables were fitted using the brms package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] or the rstan package [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We used uninformative priors and ran four independent chains. Each chain consisted of 4,000 iterations, with 2,000 warm-up samples. We considered the fixed effects and their interactions as statistically relevant if the 95% credible interval of the posterior distribution did not include 0. Model convergence was confirmed, as R-hat was less than 1.1 for all models. To assess multicollinearity among the explanatory variables, we computed a correlation matrix for all models (Supplementary Table\u0026nbsp;2). All pairs of variables showed Pearson correlation coefficients less than 0.7, therefore, multicollinearity was considered negligible [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRelationship between climate and fine-scale behavioral states\u003c/p\u003e\u003cp\u003eBecause GPS recording intervals varied depending on logger type, all GPS data were resampled at 5-minute intervals. The resampled data were divided into foraging trips based on the following criteria: a foraging trip was defined as a continuous movement in which an individual traveled more than 3 km away from the colony for a duration of at least 6 hours before returning to the breeding site [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Trips in which recording was interrupted due to battery depletion or other issues after the individual had moved more than 3 km from the colony were excluded from the analysis. In addition, because chick-feeding frequency declines after early October (Supplementary Fig.\u0026nbsp;2), foraging trips occurring in October or later were also excluded. Behavioral states and transition probabilities during foraging trips were estimated using Hidden Markov Models (HMMs) (Supplementary Fig.\u0026nbsp;3), implemented with the fitHMM function from the momentuHMM package [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The behavioral states of streaked shearwaters were classified into three categories based on step length and turning angle: points with large step lengths and small turning angles were classified as traveling; those with short step lengths and large turning angles as foraging, and those with short step length and small turning angles as resting.\u003c/p\u003e\u003cp\u003eTo examine the effects of climate on fine-scale behavioral states during foraging trips, we fitted HMMs with climatic variables as covariates. For all climate data, we used values from the point with the shortest linear distance and the closest timestamp to each GPS point as covariates. The analysis was restricted to foraging trips recorded between 2020 and 2024 (N\u0026thinsp;=\u0026thinsp;808 trips). If climatic variables influence either behavioral states or transition probabilities, models including these covariates are expected to show improved fit [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Therefore, we compared the Akaike Information Criterion (AIC) across all possible combinations of climatic covariates and selected the model with the lowest AIC as the best-fitting model. A covariate was considered to have a statistically significant effect on transition probabilities when the 95% confidence intervals of its beta-scale parameters did not include zero [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Finally, we visualized the covariate effect on the relative use of behavioral states by plotting stationary state probability distributions (i.e. the equilibrium probability of occupying each state) as a function of each covariate [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRelationship between climate and meso-scale trip parameters\u003c/p\u003e\u003cp\u003eBecause ERA5 climate data are provided as hourly values on a 0.25\u0026deg; \u0026times; 0.25\u0026deg; grid, it was necessary to rescale both the spatial and temporal dimensions of the data to match the scale of the foraging trips. For spatial scaling, we first extracted climate data within the 50% kernel utilization distributions (UDs) of all foraging trips over the ocean (Supplementary Fig.\u0026nbsp;4) and averaged the values across this area to obtain a single value per hour. For temporal scaling, we extracted climate data from the time of departure for each foraging trip until 24 hours later and averaged them over that period. The climate data processed in this manner were used as climatic parameters and candidate explanatory variables in the subsequent analysis.\u003c/p\u003e\u003cp\u003eFor all foraging trips, we calculated maximum distance and trip duration as indicators of foraging trip characteristics (N\u0026thinsp;=\u0026thinsp;2,645 trips; Supplementary Fig.\u0026nbsp;5). Note that maximum distance represents the straight-line distance between the breeding site and the farthest point from the breeding site in each foraging trip. The geosphere package [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] was used to calculate distances between GPS points. When examining the relationships between climatic and trip parameters, it is important to note that a positive correlation is expected between maximum distance and trip duration. This is because trip duration includes, at a minimum, the time required for a round-trip between the furthest point reached and the breeding site (direct round-trip travel time), which is proportional to the maximum distances, assuming a constant flight speed. The actual trip duration also includes additional time components, including deviations from the direct path and time allocated to foraging activities and landing on water. Therefore, trip duration can be regarded as the sum of a distance-dependent component and other time expenditures. Consequently, the influence of climatic factors on trip duration can be conceptualized as having two distinct pathways: (1) an indirect effect mediated by maximum distance, which determines the direct round-trip travel time, (2) a direct effect on the remaining time components that are independent of travel distance. To account for this structure, we constructed Bayesian models with maximum distance and trip duration as response variables, assuming that climatic parameters influence both variables, and that maximum distance also affects trip duration. The specific model equations are presented below. For the \u003cem\u003en\u003c/em\u003eth trip, let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{md}_{n}\\)\u003c/span\u003e\u003c/span\u003e denote the observed maximum distance and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{td}_{n}\\)\u003c/span\u003e\u003c/span\u003e denote the observed trip duration. The model assumes:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"233\" height=\"93\"\u003e\u003c/p\u003e\n\u003cp\u003eThe linear predictors are defined as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{\\mu\\:}_{{md},n}={a}_{md}+\\sum\\:_{i}{b}_{md,i}{X}_{i,n}+{u}_{md,ID\\left[n\\right]}+{u}_{md,year\\left[n\\right]}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{\\mu\\:}_{{td},n}={a}_{td}+\\sum\\:_{j}{b}_{td,i}{X}_{j,n}+{b}_{td,j}\\text{log}\\left({md}_{n}\\right)+{u}_{td,ID\\left[n\\right]}+{u}_{td,year\\left[n\\right]}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe random intercepts are modeled as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"418\" height=\"96\"\u003e\u003c/p\u003e\n\u003cp\u003eThe log-normal distribution \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LogNormal(\\mu\\:,\\sigma\\:)\\)\u003c/span\u003e\u003c/span\u003e is defined as:\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:LogNormal\\left(x|\\mu\\:,\\sigma\\:\\right)=\\frac{1}{\\sqrt{2\\pi\\:\\sigma\\:x}}\\text{e}\\text{x}\\text{p}\\left(-\\frac{{\\left(\\text{ln}x-\\mu\\:\\right)}^{2}}{2{\\sigma\\:}^{2}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{md,ID\\left[n\\right]}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{td,ID\\left[n\\right]}\\)\u003c/span\u003e\u003c/span\u003e represent individual-specific random intercepts for maximum distance and trip duration, respectively, corresponding to the individual ID associated with observation \u003cem\u003en\u003c/em\u003e, Similarly, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{md,year\\left[n\\right]}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{td,year\\left[n\\right]}\\)\u003c/span\u003e\u003c/span\u003e represent year-specific random intercepts for maximum distance and trip duration, respectively, corresponding to the year in which observation \u003cem\u003en\u003c/em\u003e was recorded. The predictors \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i,n}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{j,n}\\)\u003c/span\u003e\u003c/span\u003e denote the values of climatic variables for observation \u003cem\u003en\u003c/em\u003e, where:\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:i,j\\in\\:\\left\\{Cloud,Convective\\:Rainfall,\\:Large\\:Scale\\:Rainfall,Wind\\:Speed\\right\\}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{b}_{md,i}\\)\u003c/span\u003e\u003c/span\u003e represent the effects of climatic variable \u003cem\u003ei\u003c/em\u003e on maximum distance, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{b}_{td,j}\\)\u003c/span\u003e\u003c/span\u003e capture the effects of climatic variable \u003cem\u003ej\u003c/em\u003e on trip duration. All climatic variables were standardized using variable-specific scaling procedures. Cloud cover and wind speed were standardized using z-score normalization, while convective and large-scale rainfall were log(x\u0026thinsp;+\u0026thinsp;1)-transformed and subsequently standardized using z-score normalization. We then constructed models for all combinations of explanatory variables and evaluated model fit using the Leave-One-Out Information Criterion (LOOIC), calculated with the loo function. The model with the lowest LOOIC was considered the best-fitting model with the highest predictive performance [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Based on the explanatory variables included in the models, we examined the climatic factors associated with foraging trip characteristics and the pathways through which they exert their effects.\u003c/p\u003e\u003cp\u003eRelationship between climate and IFSF\u003c/p\u003e\u003cp\u003eIn this study, IFSF was calculated based on the degree of spatial overlap in foraging sites between foraging trips. First, to identify foraging sites, we performed state estimation by running a HMM without covariates for all foraging trips (Supplementary Fig.\u0026nbsp;6). Foraging sites were defined as the centroid coordinates of consecutive GPS points, beginning with the transition from the traveling state to the foraging state, continuing through successive foraging or resting states, and ending with the transition back to the traveling state. We then calculated the 50% kernel UDs of foraging sites for each foraging trip using fixed kernel density estimation implemented in the adehabitatHR package. We used Bhattacharyya\u0026rsquo;s Affinity (BA), which represents the similarity of UDs, as an indicator of IFSF [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. BA ranges from 0 to 1, where values closer to 0 indicate lower similarity while values closer to 1 indicate higher similarity. For each individual, we calculated BA values for all pairs of foraging trips (e.g., an individual with four trips yielded six BA values) and IFSF was defined as the mean of these values. To ensure reliability, individuals with fewer than three recorded foraging trips per year were excluded from the analysis.\u003c/p\u003e\u003cp\u003eFinally, to examine the effects of climate on IFSF, we constructed a Bayesian model with IFSF as the response variable, climatic parameters as explanatory variables, and ID and year (corresponding to when IFSF was calculated) included as random effects (N\u0026thinsp;=\u0026thinsp;241 individuals). Climatic parameters were represented by the mean values calculated over the period between logger deployment and retrieval for each individual. Because IFSF is a continuous variable ranging from 0 to 1, we assumed a beta distribution and applied a logit link function in the model. Following the same model selection procedure used for trip parameters, we constructed models for all combinations of explanatory variables and identified the best-fitting model. Based on the results of the best-fitting model, we identified the climatic parameters that influence IFSF. Since the accuracy of IFSF values may depend on the number of foraging trips used in their calculation, the number of trips was included as an explanatory variable in all models.\u003c/p\u003e\u003cp\u003eRelationship between climate and chick growth rate\u003c/p\u003e\u003cp\u003eDuring the chick-rearing period from 2011 to 2024, chick body mass was measured every five days using a spring scale (Pesola AG, Baar, Switzerland) (Supplementary Fig.\u0026nbsp;2). Measurements began in mid-to-late August each year (between August 19 and 26), while the final measurement dates varied annually (ranging from September 29 to November 16). Chick growth rate was calculated as the slope of a linear regression of body mass over time for each chick [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This approach was appropriate because the peak body mass of streaked shearwater chicks occurs in October (Supplementary Fig.\u0026nbsp;2), allowing fluctuations in mass during the study period to be approximated linearly. To examine the relationship between climatic factors that affected foraging behavior and chick growth rate, we constructed a Bayesian model, assuming a normal distribution. In this model, the growth rate of each chick during August and September served as the response variable. The mean values of cloud cover, convective rainfall, large-scale rainfall, and wind speed within the 50% kernel UDs of foraging sites (Supplementary Fig.\u0026nbsp;4) during the same period were included as explanatory variables. Year was included as a random effect.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eEffects of climate on fine-scale behavioral states\u003c/p\u003e\u003cp\u003eBased on model selection using AIC, the model including cloud cover, convective rainfall, and wind speed as covariates had the lowest AIC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the beta-scale parameter estimates and their 95% confidence intervals for the transition probabilities between behavioral states in this model (Supplementary Table\u0026nbsp;3), increased cloud cover was associated with decreased transition probabilities from traveling to foraging (Est. = -0.083; 95% CI = -0.132 to -0.034), from foraging to resting (Est. = -0.061; 95% CI = -0.113 to -0.009), and from resting to foraging (Est. = -0.052; 95% CI = -0.102 to -0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;c). Additionally, with increasing convective rainfall, the transition probabilities from traveling to foraging increased (Est. = 0.071; 95% CI\u0026thinsp;=\u0026thinsp;0.021 to 0.121), whereas that from foraging to resting decreased (Est. = -0.07; 95% CI = -0.129 to -0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, e). Finally, with increasing wind speed, the transition probabilities from foraging to traveling increased (Est. = 0.017; 95% CI\u0026thinsp;=\u0026thinsp;0.01 to 0.024), while that from resting to foraging decreased (Est. = -0.026; 95% CI = -0.033 to -0.018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, g). According to the stationary probability distributions, an increase in convective rainfall led to a higher proportion in the foraging state and a lower proportion in the resting state (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In addition, increasing wind speed was associated with a higher proportion in the traveling state and a lower proportion in the foraging state (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Little or no clear changes in state proportions were observed in response to cloud cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Transition probabilities from traveling to resting or from resting to traveling were extremely low (\u0026lt;\u0026thinsp;0.0001), so we did not focus on these in this analysis (Supplementary Table\u0026nbsp;4).\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\u003eAIC comparison of a set of candidate Hidden Markov Models to evaluate the effects of climatic factors on behavioral states. AIC was calculated for each model, and their differences (ΔAIC) from the best-fitting model are shown.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔAIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;CR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;CR\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;CR\u0026thinsp;+\u0026thinsp;LSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;CR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u0026thinsp;+\u0026thinsp;LSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;LSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNull\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7828806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eC: Cloud, CR: Convective Rainfall, LSR: Large-Scale Rainfall, WS: Wind Speed\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eEffects of climate and meso-scale trip parameters\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eModel selection based on the LOOIC identified the best-fitting model for maximum distance as the one including cloud cover and wind speed, and for trip duration as the one including convective rainfall and wind speed as explanatory variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When the combination of explanatory variables was applied to the models, we found that maximum distance increased with greater cloud cover and stronger wind speed, and trip duration increased with stronger convective rainfall (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eComparison of the top five models with the lowest LOOIC values for evaluating the effects of climatic factors on maximum distance, trip duration, and IFSF, based on different combinations of candidate climate variables as explanatory variables. LOOIC values were calculated for each model, and their differences (ΔLOOIC) from the best-fitting model are shown.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLOOIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔLOOIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31542.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31542.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31543.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCR\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31543.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31544.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrip Duration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21476.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21476.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCR\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21478.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21478.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21478.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFSF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCR\u0026thinsp;+\u0026thinsp;LSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-457.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;CR\u0026thinsp;+\u0026thinsp;LSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-456.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCR\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-455.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u0026thinsp;+\u0026thinsp;CR\u0026thinsp;+\u0026thinsp;LSR\u0026thinsp;+\u0026thinsp;WS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-454.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-450.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eC: Cloud, CR: Convective Rainfall, LSR: Large-Scale Rainfall, WS: Wind Speed\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimation results from the best-fitting models with maximum distance, trip duration, and IFSF as response variables, as well as the model with chick growth rate as the response variable.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower-95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper-95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCloud\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eWind Speed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrip Duration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eConvective Rainfall\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWind Speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMaximum Distance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFSF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNum. of Trips\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eConvective Rainfall\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLarge-Scale Rainfall\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChick Growth Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e12.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCloud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConvective Rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLarge-Scale Rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWind Speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e-0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSE means standard errors and CI means 95% credible intervals. Bold indicates significant variables\u003c/p\u003e\u003cp\u003eEffects of climate on broad-scale behavioral specialization\u003c/p\u003e\u003cp\u003eModel selection based on the LOOIC, with IFSF as the response variable, identified the model including convective and large-scale rainfall as explanatory variables as the best-fitting model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This model indicated that IFSF tended to increase with higher convective rainfall and decrease with higher large-scale rainfall (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEffects of climate on chick growth\u003c/p\u003e\u003cp\u003eResults from the model including all candidate climatic parameters as explanatory variables showed that climatic conditions within the 50% kernel UDs of foraging sites were not associated with chick growth rate (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eForaging behavior and rainfall\u003c/p\u003e\u003cp\u003eThe increase in convective rainfall particularly led to higher transition probabilities from traveling to foraging states, as well as higher stationary probabilities of the foraging states. This pattern may be explained by the emergence of high-quality foraging sites following rain. For example, local decreases in salinity caused by rain have been reported to promote the abundance and reproductive activity of Japanese anchovy \u003cem\u003eEngraulis japonicus\u003c/em\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], a major prey species of streaked shearwaters breeding on Awashima Island [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In addition, numerous previous studies have shown that freshwater input from rivers associated with rainfall enhances marine productivity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and streaked shearwaters have been observed foraging at river outflow areas at sea [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Therefore, foraging activity may have increased in areas and periods with elevated rainfall. It is important to note that foraging states estimated by the HMMs are based on GPS-derived movement patterns\u0026mdash;specifically, slow and highly sinuous flight\u0026mdash;and do not necessarily represent actual foraging behavior such as prey ingestion. Because rainfall increases flight costs in birds [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], this pattern may instead reflect restricted flight behavior during rain. Indeed, previous studies have shown that the proportion of slow flight increases during rain in magnificent frigatebirds [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and that landing rates increase in albatrosses under rainy conditions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These findings suggest that seabirds tend to avoid direct flight during rain.\u003c/p\u003e\u003cp\u003eOn the other hand, convective rainfall may prompt seabirds to actively adjust their foraging behavior in response to changing environmental conditions. Increased convective rainfall was associated with reduced transition probabilities from foraging to resting states and lower stationary probabilities of the resting states. This pattern can be interpreted through the lens of the trade-off between time and energy. The ability to regulate energy expenditure during foraging is considered to affect individual fitness [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and animals are assumed to behave in ways that maximize the ratio of energy gained to energy expended during foraging [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Given that foraging efficiency may improve during convective rain, seabirds may actively reduce the amount of time spent resting and allocate more time to foraging. In support of this, convective rain increased trip duration regardless of the round-trip distance to the furthest point reached. This result likely reflects that seabirds spent more time actively foraging during convective rain, as suggested by changes in behavioral states. Consequently, climate-induced changes in fine-scale behavioral states may cascade upward to influence broader-scale behavior, such as foraging trip characteristics.\u003c/p\u003e\u003cp\u003eIFSF decreased with increasing large-scale rainfall, suggesting that such rain may negatively affect seabird foraging behavior. Previous studies on the effects of rainfall on seabird foraging behavior have reported reduced flight speeds and foraging efficiency during rain [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which may be attributed to increased flight costs and reduced underwater visibility. Large-scale rain is characterized by its persistence and broad spatial extent compared to convective rain [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], and is therefore likely to exert prolonged constraints on seabird foraging behavior. Accordingly, the observed decline in IFSF may reflect shifts in foraging sites due to foraging failures or altered flight costs under large-scale rain. In contrast, increased convective rainfall was associated with higher IFSF. According to the win-stay lose-switch strategy, individuals are expected to revisit previously successful foraging sites, thereby increasing foraging site fidelity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This result may indicate that the negative effects of convective rainfall on foraging and flight are relatively short-term, and that the benefits associated with the appearance of high-quality foraging sites are more strongly reflected in foraging behavior. Thus, the contrasting effects of large-scale and convective rainfall on IFSF are likely due to differences in their temporal and spatial characteristics.\u003c/p\u003e\u003cp\u003eForaging behavior and cloud cover\u003c/p\u003e\u003cp\u003eIncreased cloud cover was associated with reduced transition probabilities from traveling to foraging, from resting to foraging, and from foraging to resting states. This pattern may reflect a decline in foraging efficiency under overcast conditions, leading to a decrease in behavioral transitions associated with prey search and capture. In Manx shearwaters \u003cem\u003ePuffinus puffinus\u003c/em\u003e, dive depth was greatest during clear weather and shallower under dense cloud cover [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, in Eastern kingbirds \u003cem\u003eTyrannus tyranus\u003c/em\u003e, a terrestrial insectivorous bird, predation rates declined under overcast conditions [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Furthermore, fish school cohesion has been shown to decrease under shade [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], and such irregular fish movements during overcast conditions may make fish capture more difficult. These findings suggest that reduced prey visibility under cloud cover may impair prey detection and capture efficiency. In addition, the observed increase in maximum distance associated with greater cloud cover may indicate a behavioral tendency among seabirds to avoid cloudy areas, potentially as a response to decreased foraging efficiency under overcast skies. Collectively, these results suggest that fine-scale climatic effects on seabirds can influence their decision-making at a broader behavioral scale.\u003c/p\u003e\u003cp\u003eForaging behavior and wind speed\u003c/p\u003e\u003cp\u003eThis study revealed that transition probabilities from foraging to traveling states, as well as stationary probabilities of the traveling state, also increased with rising wind speed. This finding may indicate that improved flight efficiency under strong wind conditions promotes both the transition to and maintenance of traveling behavior. In both Manx shearwaters and wandering albatrosses \u003cem\u003eDiomedea exulans\u003c/em\u003e, individuals have been shown to shift from flapping to soaring flight under strong winds [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], and increased flight speeds with higher wind speeds have been reported in albatrosses [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These findings suggest that increasing wind speed facilitates more energy-efficient flight in seabirds. Moreover, maximum distance tended to increase with higher wind speeds. This pattern may also reflect the facilitation of longer-distance travel resulting from enhanced flight efficiency. In Procellariiforme seabirds during the chick-rearing period, foraging in more distant oceanic areas has been associated with improvements in parental body mass [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Therefore, parents may choose to undertake longer trips under strong wind conditions to improve their own body condition, taking advantage of enhanced flight efficiency. Consequently, it is suggested that changes in flight efficiency driven by wind speed may affect foraging behaviors across multiple scales, from fine-scale behavioral states to meso-scale trip parameters.\u003c/p\u003e\u003cp\u003eIn contrast, increased wind speed was associated with decreased transition probabilities from foraging to resting states and lower stationary probabilities of the foraging states. This pattern may indicate that reduced foraging efficiency due to strong winds leads to decreases in the transition probabilities from slow flight for prey capture to landing on water, as well as in the probabilities of engaging in prey search behavior. For example, in wandering albatrosses, strong winds reduced feeding rates, likely due to decreased prey visibility caused by sea surface disturbance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, in little penguins \u003cem\u003eEudyptula minor\u003c/em\u003e, strong winds deepen the mixed layer, thereby reducing prey encounter rates. Additionally, reduced water column stratification caused by strong winds lowers fish productivity, potentially reducing breeding success rates in kittiwakes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Because streaked shearwaters forage at relatively shallow depths (approximately\u0026thinsp;\u0026lt;\u0026thinsp;6 m) [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], sea surface disturbance and deepened mixed layers caused by strong winds may reduce prey visibility and availability, resulting in decreased foraging efficiency. Therefore, while increased wind speed may enhance flight efficiency, it may simultaneously impair foraging efficiency. These findings highlight that the effects of climate on seabird behavior are not unidirectional and underscore the need for a multifaceted perspective to fully understand their ecological implications.\u003c/p\u003e\u003cp\u003eChick growth and climate\u003c/p\u003e\u003cp\u003eAlthough climate had various effects on foraging behavior across different spatiotemporal scales, no significant association was observed between climatic conditions and chick growth rate\u0026mdash;an outcome reflecting overall foraging success. This finding suggests that the effects of climate on foraging behavior may be sufficiently minor to be compensated for by parental effort. In other words, when climatic conditions have negative effects, parent birds may strive to feed their chick sufficiently, even at the expense of their own condition, whereas under favorable conditions, they may prioritize improving their own condition rather than investing in chick growth. Therefore, climate-induced changes in foraging behavior may not have translated into measurable effects on chick growth. However, global warming is expected to increase the frequency and intensity of extreme weather events, such as heavy rainfall and storms [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. It is therefore necessary to consider the possibility that future climate change may exceed the compensatory capacity of parent birds, potentially affecting breeding performance and individual fitness.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we examined the effects of climate on the foraging behavior of streaked shearwaters across multiple spatiotemporal scales\u0026mdash;from fine-scale behavioral states, through meso-scale trip parameters, to broad-scale behavioral specialization. The results revealed that fine-scale behavioral states during foraging trips and meso-scale trip parameters were influenced by cloud cover, convective rainfall, and wind speed, while broad-scale behavioral specialization was affected by convective and large-scale rainfall. These findings demonstrate that climatic factors influence the foraging behavior of streaked shearwaters differently across spatial and temporal scales, with convective rainfall emerging as particularly important, as it influenced all aspects of foraging behavior: behavioral states, trip parameters, and IFSF. This suggests that climate-induced changes in foraging behavior at smaller spatiotemporal scales may cascade into changes at broader scales. This study contributes to a comprehensive understanding of climate-induced changes in seabird foraging behavior by focusing on behavior across multiple spatiotemporal scales, and underscores the need to account for scale-dependent behavioral responses when predicting the ecological impacts of future climate change.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAkaike\u0026rsquo;s Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBhattacharyya\u0026rsquo;s Affinity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHMM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHidden Markov Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIFSF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIndividual Foraging Site Fidelity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLOOIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeave-One-Out Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUtilization Distribution\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eAll experiments were approved by the Animal Experimental Committee of Nagoya University (GSES 2011\u0026ndash;2024) and the Awashimaura Village Office, Niigata Prefecture, Japan.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by the Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (24681006, 16H01769, 16H06541, 21H05294, 22H00569 to K.Y.), and by Core Research for Evolutional Science and Technology and Support for Pioneering Research Initiated by the Next Generation from the Japan Science and Technology Agency (JPMJCR23P2 to K.Y. and JPMJSP2125 to W.T).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.T. and K.Y. conceived the ideas and designed methodology; W.T. and S.K. collected the data; W.T. and Y.G. analyzed the data; All authors contributed to writing of the manuscript. All authors gave final approval for publication and agreed to be held accountable for the work performed therein.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author (W.T.) would like to take this opportunity to thank the \u0026ldquo;Interdisciplinary Frontier Next-Generation Researcher Program of the Tokai Higher Education and Research System.\u0026rdquo; We would also like to thank our colleagues who contributed to the fieldwork.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data from this study are available in Movebank (https://www.movebank.org/cms/movebank-main) and Biologging intelligent Platform (BiP; https://www.bip-earth.com/ja). The Movebank study ID of our data is 3240100863.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJenkins S, Povey A, Gettelman A, Grainger R, Stier P, Allen M. Is anthropogenic global warming accelerating? 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Dual foraging strategy and chick growth of streaked shearwater \u003cem\u003eCalonectris leucomelas\u003c/em\u003e at two colonies in different oceanographic environments. Ornithol Sci 2016;15:213\u0026ndash;225.\u003c/li\u003e\n\u003cli\u003ePatrick SC, Weimerskirch H. Reproductive success is driven by local site fidelity despite stronger specialisation by individuals for large-scale habitat preference. J Anim Ecol 2017;86:674\u0026ndash;682.\u003c/li\u003e\n\u003cli\u003eRebstock GA, Abrahms B, Boersma PD. Site fidelity increases reproductive success by increasing foraging efficiency in a marine predator. Behav Ecol 2022;33:868\u0026ndash;875.\u003c/li\u003e\n\u003cli\u003eOrtega S, Rodr\u0026iacute;guez C, Drummond, H. Seasonal weather effects on offspring survival differ between reproductive stages in a long-lived neotropical seabird. Oecologia 2022;199:611\u0026ndash;623.\u003c/li\u003e\n\u003cli\u003eHouze RA. 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(Vienna, Austria): R Foundation for Statistical Computing. 2024. https://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eB\u0026uuml;rkner P-C. brms: An R package for Bayesian multilevel models using Stan. J Stat Softw 2017;80:1\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eStan Development Team. Rstan: The R interface to Stan. R package version 2.32.6. 2017. https://mc-stan.org/\u003c/li\u003e\n\u003cli\u003eDormann CF, Elith J, Bacher S et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 2012;36:27-46.\u003c/li\u003e\n\u003cli\u003eMcClintock BT, Michelot T. momentuHMM: R package for generalized hidden Markov models of animal movement. Methods Ecol Evol 2018;9:1518\u0026ndash;1530.\u003c/li\u003e\n\u003cli\u003eKane A, Pirotta E, Wischnewski S, Critchley EJ, Bennison A, Jessopp M, Quinn JL. Spatio-temporal patterns of foraging behaviour in a wide-ranging seabird reveal the role of primary productivity in locating prey. Mar Ecol Prog Ser 2020;646:175-188.\u003c/li\u003e\n\u003cli\u003eLieber L, Langrock R, Nimmo-Smith WAM. A bird\u0026rsquo;s-eye view on turbulence: Seabird foraging associations with evolving surface flow features. Proc R Soc B 2021;288:20210592.\u003c/li\u003e\n\u003cli\u003eClay TA, Joo R, Weimerskirch H, Phillips RA, den Ouden O, Basille M, Clusella-Trullas S, Assink JD, Patrick SC. Sex-specific effects of wind on the flight decisions of a sexually dimorphic soaring bird. J Anim Ecol 2020;89:1811\u0026ndash;1823.\u003c/li\u003e\n\u003cli\u003eHijmans R. Geosphere: spherical trigonometry. R package version 1.5\u0026ndash;18. 2022. https://CRAN.R-project.org/package=geosphere\u003c/li\u003e\n\u003cli\u003eVehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. 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Inter-annual, seasonal, and sex differences in the diet of a surface feeding seabird, streaked shearwater \u003cem\u003eCalonectris leucomelas\u003c/em\u003e, breeding in the Sea of Japan. Ornithol Sci 2025;24:99\u0026ndash;116. \u003c/li\u003e\n\u003cli\u003eKim J-Y, Kang Y-S, Oh H-J, Suh Y-S, Hwang J-D. Spatial distribution of early life stages of anchovy (\u003cem\u003eEngraulis japonicus\u003c/em\u003e) and hairtail (\u003cem\u003eTrichiurus lepturus\u003c/em\u003e) and their relationship with oceanographic features of the East China Sea during the 1997\u0026ndash;1998 El Ni\u0026ntilde;o Event. Estuar Coast Shelf Sci 2005;63:13\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eThompson PA, O\u0026rsquo;Brien TD, Paerl HW, Peierls BL, Harrison PJ, Robb M. Precipitation as a driver of phytoplankton ecology in coastal waters: A climatic perspective. Estuar Coast Shelf Sci 2015;162:119\u0026ndash;129.\u003c/li\u003e\n\u003cli\u003eMatsumoto S, Yamamoto T, Kawabe R, Ohshimo S, Yoda K. The Changjiang River discharge affects the distribution of foraging seabirds. Mar Ecol Prog Ser 2016;555:273-277.\u003c/li\u003e\n\u003cli\u003eDrent RH, Daan S. The prudent parent: Energetic adjustments in avian breeding. Ardea 1980;55:225\u0026ndash;252.\u003c/li\u003e\n\u003cli\u003eStephens DW, Krebs JR. Foraging Theory. Princeton University Press; 1986.\u003c/li\u003e\n\u003cli\u003eYdenberg RC, Welham CVJ, Schmid-Hempel R, Schmid-Hempel P, Beauchamp G. Time and energy constraints and the relationships between currencies in foraging theory. Behav Ecol 1994;5:28\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eHoughton HG. On precipitation mechanisms and their artificial modification. J Appl Meteor Climatol 1968;7:851\u0026ndash;859.\u003c/li\u003e\n\u003cli\u003eMurphy MT. The impact of weather on kingbird foraging behavior. Condor 1987;89: 721\u0026ndash;730.\u003c/li\u003e\n\u003cli\u003eRibeiro HV, Acre MR, Faulkner JD, da Cunha LR, Lawson KM, Wamboldt JJ, Brey MK, Woodley CM, Calfee RD. Effects of shady environments on fish collective behavior. Sci Rep 2022;12:17873.\u003c/li\u003e\n\u003cli\u003eGibb R, Shoji A, Fayet AL, Perrins CM, Guilford T, Freeman R. Remotely sensed wind speed predicts soaring behaviour in a wide-ranging pelagic seabird. J R Soc Interface. 2017;14(132):20170262.\u003c/li\u003e\n\u003cli\u003eSchoombie S, Wilson RP, Ryan PG. Wind driven effects on the fine-scale flight behaviour of dynamic soaring wandering albatrosses. Mar Ecol Prog Ser 2023;723:119\u0026ndash;134.\u003c/li\u003e\n\u003cli\u003eWeimerskirch H, Louzao M, de Grissac S, Delord K. Changes in wind pattern alter albatross distribution and life-history traits. Science 2012;335:211\u0026ndash;214.\u003c/li\u003e\n\u003cli\u003eCornioley T, B\u0026ouml;rger L, Ozgul A, Weimerskirch H. Impact of changing wind conditions on foraging and incubation success in male and female wandering albatrosses. J Anim Ecol 2016;85:1318\u0026ndash;1327.\u003c/li\u003e\n\u003cli\u003eWeimerskirch H, Ancel A, Caloin M, Zahariev A, Spagiari J, Kersten M, Chastel O. Foraging efficiency and adjustment of energy expenditure in a pelagic seabird provisioning its chick. J Anim Ecol 2003;72:500\u0026ndash;508.\u003c/li\u003e\n\u003cli\u003eOchi D, Oka N, Watanuki Y. Foraging trip decisions by the streaked shearwater \u003cem\u003eCalonectris leucomelas\u003c/em\u003e depend on both parental and chick state. J Ethol 2010;28:313\u0026ndash;321.\u003c/li\u003e\n\u003cli\u003eMatsumoto K, Oka N, Ochi D, Muto F, Satoh TP, Watanuki Y. Foraging behavior and diet of Streaked Shearwaters \u003cem\u003eCalonectris leucomelas\u003c/em\u003e rearing chicks on Mikura Island. Ornithol Sci 2012;11:9\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eTanigaki K, Otsuka R, Li A, Hatano Y, Wei Y, Koyama S, Yoda K, Maekawa T. Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector. PNAS Nexus, 2024;3:447.\u003c/li\u003e\n\u003cli\u003eMyhre G, Alterskj\u0026aelig;r K, Stjern CW. Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci Rep 2019;9:16063.\u003c/li\u003e\n\u003cli\u003eBalaguru K, Xu W, Chang CC, Leung LR, Judi DR, Hagos SM, Wehner MF, Kossin JP, Ting M. Increased U.S. coastal hurricane risk under climate change. Sci Adv 2023;9:eadf0259.\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":"movement-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"move","sideBox":"Learn more about [Movement Ecology](http://movementecologyjournal.biomedcentral.com/)","snPcode":"40462","submissionUrl":"https://submission.nature.com/new-submission/40462/3","title":"Movement Ecology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"foraging behavior, climate impacts, seabird, rainfall, individual foraging site fidelity, hidden Markov model, biologging","lastPublishedDoi":"10.21203/rs.3.rs-6970356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6970356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSeabird foraging behaviors may be influenced not only by the direct adverse effects of rainfall (e.g., limitations on flight) but also by indirect effects\u0026mdash;either adverse or beneficial\u0026mdash;such as changes in prey distribution. To understand the multifaceted impacts of rainfall and their ecological significance, it is essential to examine foraging behavior across multiple spatiotemporal scales. In particular, assessing the effects of altered convective rainfall patterns\u0026mdash;localized and shorter-lived events that have become increasingly frequent due to recent rapid temperature increases\u0026mdash;is crucial for predicting future behavioral shifts in seabirds.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe investigated the relationships between multiple climatic factors\u0026mdash;including two types of rainfall (convective and large-scale rainfall), cloud cover, and wind speed\u0026mdash;and three spatiotemporally distinct aspects of foraging behavior, as well as chick growth, in streaked shearwaters \u003cem\u003eCalonectris leucomelas\u003c/em\u003e rearing chicks on Awashima Island, Japan, from 2011 to 2024. Specifically, we focused on fine-scale behavioral states\u0026mdash;traveling, foraging, and resting\u0026mdash;estimated using a Hidden Markov Model; meso-scale trip parameters, including maximum distance and duration of each foraging trip; broad-scale behavioral specialization, quantified as Individual Foraging Site Fidelity (IFSF); and chick growth rate as a proxy for foraging outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIncreased convective rainfall was associated with increased transition probabilities from traveling to foraging states, higher stationary probabilities of foraging states, longer foraging trip durations, and higher IFSF. Additionally, cloud cover and wind speed were associated with fine-scale behavioral states and meso-scale trip parameters. While increased large-scale rainfall was associated with higher IFSF, no clear associations were found between climatic factors and chick growth rate.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eSeabirds engaged in more active prey searching during convective rain events, which prolonged foraging trips, and successful foraging experiences during these periods increased IFSF. We propose that fine-scale behavioral changes induced by convective rain, likely driven by increased prey availability, can cascade into broader-scale behavioral patterns. Our findings contributes to a comprehensive understanding of climate-induced changes in seabird foraging behavior by focusing on behavior across multiple spatiotemporal scales.\u003c/p\u003e","manuscriptTitle":"Convective rainfall drives behavioral shifts across multiple foraging scales in breeding seabirds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 06:32:23","doi":"10.21203/rs.3.rs-6970356/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T20:23:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-21T14:02:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27149200953183413468762770185003342401","date":"2025-11-06T10:09:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80826945272808241008215363288969534318","date":"2025-11-03T14:45:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191431321276950526091012814556576711036","date":"2025-08-04T16:15:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T16:19:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3643992851004168424520660252207407837","date":"2025-07-14T07:55:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T20:44:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-26T11:27:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-26T11:26:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Movement Ecology","date":"2025-06-25T04:06:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"movement-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"move","sideBox":"Learn more about [Movement Ecology](http://movementecologyjournal.biomedcentral.com/)","snPcode":"40462","submissionUrl":"https://submission.nature.com/new-submission/40462/3","title":"Movement Ecology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb226c31-949e-4a45-aab9-738575d842f5","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:00:23+00:00","versionOfRecord":{"articleIdentity":"rs-6970356","link":"https://doi.org/10.1186/s40462-026-00648-8","journal":{"identity":"movement-ecology","isVorOnly":false,"title":"Movement Ecology"},"publishedOn":"2026-04-14 15:57:16","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2025-07-14 06:32:23","video":"","vorDoi":"10.1186/s40462-026-00648-8","vorDoiUrl":"https://doi.org/10.1186/s40462-026-00648-8","workflowStages":[]},"version":"v1","identity":"rs-6970356","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6970356","identity":"rs-6970356","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}