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Modeling how climate change may affect the potential distribution of species and communities typically utilizes bioclimatic variables. Distribution predictions rely on the values of the bioclimatic variable (e.g., precipitation of the wettest quarter). However, the ecological meaning of most of these variables depends strongly on the within‐year position of a specific climate period (SCP), for example, the wettest quarter of the year, which is often overlooked. Our aim was to determine how the within‐year position of the SCPs would shift (SCP shift) in reaction to climate change in a global context. We calculated the deviations of the future within‐year position of the SCPs relative to the reference period. We used four future time periods, four scenarios, and four CMIP6 global climate models (GCMs) to provide an ensemble of expectations regarding SCP shifts and locate the spatial hotspots of the shifts. Also, the size and frequency of the SCP shifts were subjected to linear models to evaluate the importance of the impact modeler's decision on time period, scenario, and GCM. We found ample examples of SCP shifts exceeding 2 months, with 6‐month shifts being predicted as well. Many areas in the tropics are expected to experience both temperature and precipitation‐related shifts, but precipitation‐related shifts are abundantly predicted for the temperate and arctic zones as well. The combined shifts at the Equator reinforce the likelihood of the emergence of no‐analogue climates there. The shifts become more pronounced as time and scenario progress, while GCMs could not be ranked in a clear order in this respect. For most SCPs, the modeler's decision on the GCM was the least important, while the choice of time period was typically more important than the choice of scenario. Future predictive distribution models should account for SCP shifts and incorporate the phenomenon in the modeling efforts. [ABSTRACT FROM AUTHOR]

Aim: Climate change is affecting the geographic distributions of many species and researchers are increasingly relying on species distribution models (SDMs) to forecast species' redistributions under climate change. Such modelling studies, however, often ignore biotic interactions that shape species' geographic ranges. This is especially problematic for coral reefs, which host a high diversity of species and interactions. We tested how biotic interactions affect the distribution patterns of obligate coral‐dwelling Trapezia crabs. Location: Global coastal ocean. Time Period: 2000–2014, 2040–2050, 2090–2100. Major Taxa Studied: Corals and coral‐dwelling Trapezia crabs. Methods: We determined the symbiotic relationships between 22 crab species in the genus Trapezia and corals via field survey and extensive literature review. We first developed SDMs for coral and crab species using exclusively abiotic variables (abiotic‐only models for corals and crabs). Then we constructed a second set of models where we accounted for coral distributions into crab predictions by combining model predictions for the two taxa a posteriori (abiotic‐plus‐biotic models for crabs only). Results: We obtained 30 commonly accepted coral‐crab symbiotic relationships from nine Trapezia crab and six stony coral species. The abiotic‐only model predictions showed that six corals may lose approximately one‐sixth of suitable ranges under RCP 8.5 in 2040–2050. The two types of models for crabs yielded largely different habitat suitability predictions and accounting for biotic interactions into SDM predictions exacerbates the predicted impacts of climate change on coral‐dwelling crabs. Main Conclusions: Our results show large discrepancies in crab spatial distribution patterns with and without accounting for symbiotic interactions. Our findings highlight the important role of modeller's decision on accounting for biotic interactions when predicting the geographical ranges of coral‐dwelling species, with important implications for designing future conservation and management strategies for marine species. [ABSTRACT FROM AUTHOR]

Worldwide populations of shorebirds are declining, associated with a complex interplay of climate change, predation, human disturbance and habitat degradation. Comprehensive information on the distribution and breeding ecology of shorebird populations is crucial to understand and mitigate these threats. Kazakhstan, the largest country in Central Asia, comprises multiple flyways and breeding habitats for shorebird species, including the Kentish Plover Charadrius alexandrinus, but information on the population size and breeding distribution of shorebird species in the region is highly limited. We conducted a wide‐scale survey of Kentish Plover across Kazakhstan during the breeding season and utilize species distribution modelling to outline key anthropogenic and environmental variables that determine Kentish Plover presence. Our results reveal widespread distribution of Kentish Plovers across Kazakhstan but indicate that breeding densities are generally low. Our distribution modelling stresses the primary importance of proximity to water bodies and climate as the main predictors of Kentish Plover presence, but reveals a weak association with indicators of human disturbance. We utilize our distribution modelling to provide the first quantitative estimate of the breeding population size of Kentish Plover in Kazakhstan, which indicates a modest number of individuals given the size of the country (between 12 000 and 32 000 individuals). Our results indicate the key routes via which climate change may impact on population‐level distributions of Kentish Plover and provide a platform for future studies investigating species distributions across similarly vast and inaccessible regions. [ABSTRACT FROM AUTHOR]

In an era of ongoing biodiversity, it is critical to map biodiversity patterns in space and time for better‐informing conservation and management. Species distribution models (SDMs) are widely applied in various types of such biodiversity assessments. Cross‐validation represents a prevalent approach to assess the discrimination capacity of a target SDM algorithm and determine its optimal parameters. Several alternative cross‐validation methods exist; however, the influence of choosing a specific cross‐validation method on SDM performance and predictions remains unresolved. Here, we tested the performance of random versus spatial cross‐validation methods for SDM using goatfishes (Actinopteri: Syngnathiformes: Mullidae) as a case study, which are recognized as indicator species for coastal waters. Our results showed that the random versus spatial cross‐validation methods resulted in different optimal model parameterizations in 57 out of 60 modeled species. Significant difference existed in predictive performance between the random and spatial cross‐validation methods, and the two cross‐validation methods yielded different projected present‐day spatial distribution and future projection patterns of goatfishes under climate change exposure. Despite the disparity in species distributions, both approaches consistently suggested the Indo‐Australian Archipelago as the hotspot of goatfish species richness and also as the most vulnerable area to climate change. Our findings highlight that the choice of cross‐validation method is an overlooked source of uncertainty in SDM studies. Meanwhile, the consistency in richness predictions highlights the usefulness of SDMs in marine conservation. These findings emphasize that we should pay special attention to the selection of cross‐validation methods in SDM studies. [ABSTRACT FROM AUTHOR]