As ecosystems undergo global changes, there is increasing interest in understanding how organisms respond to changing environments. Recent evidence drawn from available vertebrate studies suggests that most of the phenotypic responses to climate change would be due to plasticity. We hypothesize that organisms that have evolved in unpredictable environments inform us about the mechanisms of phenotypic plasticity which provide an adaptive response to climate instability. As climate changes increase climatic hazards, these resilience mechanisms are expected to spread within species, populations and communities. We review studies that have demonstrated the importance of phenotypic plasticity in different life-history traits in overcoming climate uncertainty. We focus on organisms from unstable, recurrently energetically restrictive environments which possess a variety of morphological, physiological and/or behavioural adaptations to climate-driven selective pressures. First, we treat plastic morphological changes in response to fluctuating food availability. Adjustment of morphometric traits and/or organ size to energy supply would be essential in harsh environments. Second, we review the role of flexible energy-saving mechanisms, such as daily torpor, hibernation and energy storage, in overcoming climate-driven energetic shortages. Lastly, we address the role of plastic modulation of reproduction in fine-tuning the energy allocation to offspring production according to environmental conditions, with an emphasis on opportunistic breeding. Overall, we predict that species (or genotypes) possessing these efficient physiological mechanisms of resilience to unpredictable water and food fluctuations will be selectively advantaged in the face of increasing climatic instability

Summary: 1. Numbers of individuals or species are often recorded to test for variations in abundance or richness between treatments, habitat types, ecosystem management types, experimental treatments, time periods, etc. However, a difference in mean detectability among treatments is likely to lead to the erroneous conclusion that mean abundance differs among treatments. No guidelines exist to determine the maximum acceptable difference in detectability.2. In this study, we simulated count data with imperfect detectability for two treatments with identical mean abundance (N) and number of plots (n_plots) but different mean detectability (p). We then estimated the risk of erroneously concluding that N differed between treatments because the difference in p was ignored. The magnitude of the risk depended on p, N and n_plots.3. Our simulations showed that even small differences in p can dramatically increase this risk. A detectability difference as small as 4–8% can lead to a 50–90% risk of erroneously concluding that a significant difference in N exists among treatments with identical N = 50 and n_plots = 50. Yet, differences in p of this magnitude among treatments or along gradients are commonplace in ecological studies.4. Fortunately, simple methods of accounting for imperfect detectability prove effective at removing detectability difference between treatments.5. Considering the high sensitivity of statistical tests to detectability differences among treatments, we conclude that accounting for detectability by setting up a replicated design, applied to at least part of the design scheme and analysing data with appropriate statistical tools, is always worthwhile when comparing count data (abundance, richness).