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Phenological shifts have been observed among marine species due to climate change. Modeling changes in fish spawning aggregations (FSAs) under climate change can be useful for adaptive management, because it can allow managers to adjust conservation strategies in the context of specific life history and phenological responses. We modeled effects of climate change on the distribution and phenology of Caribbean FSAs, examining 4 snapper and 4 grouper species. An ecological niche model was used to link FSAs with environmental conditions from remote sensing and project FSA distribution and seasonality under RCP8.5. We found significant differences between groupers and snappers in response to warming. While there was variation among species, groupers experienced slight delays in spawning season, a greater loss of suitable ocean habitat (average loss: 72.75%), and poleward shifts in FSA distribution. Snappers had larger shifts towards earlier phenology, with a smaller loss of suitable ocean habitat (average loss: 24.25%), excluding gray snapper, which gained habitat. Snappers exhibited interspecific variability in latitudinal distribution shifts. Snapper FSAs appeared more resilient to climate change and occupy wider and warmer spawning temperature ranges, while groupers prefer cooler spawning seasons. Consequently, groupers may lose more suitable ocean spawning habitat sooner due to climate change. When comparing species, there were trade-offs among climate change responses in terms of distribution shifts, phenology changes, and declines in habitat suitability. Understanding such trade-offs can help managers prioritize marine protected area locations and determine the optimal timing of seasonal fishing restrictions to protect FSAs vulnerable to fishing pressure in a changing climate. 

Species distribution models (SDMs) are a commonly used tool, which when combined with earth system models (ESMs), can project changes in organismal occurrence, abundance, and phenology under climate change. An often untested assumption of SDMs is that relationships between organisms and the environment are stationary. To evaluate this assumption, we examined whether patterns of distribution among larvae of four small pelagic fishes (Pacific sardine Sardinops sagax , northern anchovy Engraulis mordax , jack mackerel Trachurus symmetricus , chub mackerel Scomber japonicus ) in the California Current remained steady across time periods defined by climate regimes, changes in secondary productivity, and breakpoints in time series of spawning stock biomass (SSB). Generalized additive models (GAMs) were constructed separately for each period using temperature, salinity, dissolved oxygen (DO), and mesozooplankton volume as predictors of larval occurrence. We assessed non-stationarity based on changes in six metrics: 1) variables included in SDMs; 2) whether a variable exhibited a linear or non-linear form; 3) rank order of deviance explained by variables; 4) response curve shape; 5) degree of responsiveness of fishes to a variable; 6) range of environmental variables associated with maximum larval occurrence. Across all species and time periods, non-stationarity was ubiquitous, affecting at least one of the six indicators. Rank order of environmental variables, response curve shape, and oceanic conditions associated with peak larval occurrence were the indicators most subject to change. Non-stationarity was most common among regimes defined by changes in fish SSB. The relationships between larvae and DO were somewhat more likely to change across periods, whereas the relationships between fishes and temperature were more stable. Respectively, S. sagax , T. symmetricus , S. japonicus , and E. mordax exhibited non-stationarity across 89%, 67%, 50%, and 50% of indicators. For all species except E. mordax , inter-model variability had a larger impact on projected habitat suitability for larval fishes than differences between two climate change scenarios (SSP1-2.6 and SSP5-8.5), implying that subtle differences in model formulation could have amplified future effects. These results suggest that the widespread non-stationarity in how fishes utilize their environment could hamper our ability to reliably project how species will respond to climatic change.