Thu, Aug 18, 2022: 9:00 AM-9:20 AM
520F
Background/Question/MethodSpecies living in interannually fluctuating environments have to alter the timing of their energetically demanding life history events each year. They achieve this by using information from their environment to predict the timing of peak resources. When their resource is another species, the challenge of optimal timing is further complicated. Climate change induces shifts in the environmental cues; however, individual species do not shift their timing uniformly, potentially disrupting interspecific interactions. To begin to tease apart the impacts of climate change on these phenological relationships, it is first necessary to identify what information is being used by species as cues for their life history events. Cue identification is achieved with statistical testing of candidate cues through mechanistic or regression-based approaches. We test the predictive ability of commonly applied statistical methods for cue identification. We explore how the timing and aggregate statistic of the identified cue changed based on the statistical method used and the timespan of the data. We then use an evolutionarily explicit formulation of an integral projection model to project future phenology of a predator (great tit, Parus major) and its prey (winter moth, Operophtera brumata) under three climate change scenarios.
Results/ConclusionsWe show that the identified critical window of cue sensitivity is consistent across most methods, but the accuracy of predictions varies. Predictions precede observations for the first few decades of our study but lag observed timings in the most recent years, potentially indicating a shifting cue-phenology or cue-proxy relationship or a missing causal variable. While sufficient for predictive purposes, the cues identified from these statistical techniques often lack biological realism and therefore further work on developing methods to identify causal environment-phenology links is encouraged. Our population modelling work shows that plasticity, which has evolved from exposure to an interannually fluctuating environment, helps great tits to retain matching with their prey species under moderate climate change but slows evolution. Under pessimistic climate change projections, differences in the response of the predator and prey species lead to a divergence in timing (mismatch). We identify thresholds of temporal mismatch beyond which the predator population rapidly goes extinct. This work highlights the importance of including environmental variability in assessments of the impact of climate change on biological populations.
Results/ConclusionsWe show that the identified critical window of cue sensitivity is consistent across most methods, but the accuracy of predictions varies. Predictions precede observations for the first few decades of our study but lag observed timings in the most recent years, potentially indicating a shifting cue-phenology or cue-proxy relationship or a missing causal variable. While sufficient for predictive purposes, the cues identified from these statistical techniques often lack biological realism and therefore further work on developing methods to identify causal environment-phenology links is encouraged. Our population modelling work shows that plasticity, which has evolved from exposure to an interannually fluctuating environment, helps great tits to retain matching with their prey species under moderate climate change but slows evolution. Under pessimistic climate change projections, differences in the response of the predator and prey species lead to a divergence in timing (mismatch). We identify thresholds of temporal mismatch beyond which the predator population rapidly goes extinct. This work highlights the importance of including environmental variability in assessments of the impact of climate change on biological populations.