Species across a wide range of taxa and habitats are shifting phenological events in response to climate change. While advances are common, shifts vary in magnitude and direction within and among species, and the basis for this variation is relatively unknown. We examine previously suggested patterns of variation in phenological shifts in order to understand the cue–response mechanisms that underlie phenological change. Here, we review what is known about the mechanistic basis for nine factors proposed to predict phenological change (latitude, elevation, habitat type, trophic level, migratory strategy, ecological specialization, species’ seasonality, thermoregulatory mode, and generation time). We find that many studies either do not identify a specific underlying mechanism or do not evaluate alternative mechanistic hypotheses, limiting the ability of scientists to predict future responses to global change with accuracy. We present a conceptual framework that emphasizes a critical distinction between environmental (cue‐driven) and organismal (response‐driven) mechanisms causing variation in phenological shifts and discuss how this distinction can reduce confusion in the field and improve predictions of future phenological change.

Chemical contaminants (e.g. metals, pesticides, pharmaceuticals) are changing ecosystems via effects on wildlife. Indeed, recent work explicitly performed under environmentally realistic conditions reveals that chemical contaminants can have both direct and indirect effects at multiple levels of organization by influencing animal behaviour. Altered behaviour reflects multiple physiological changes and links individual- to population-level processes, thereby representing a sensitive tool for holistically assessing impacts of environmentally relevant contaminant concentrations. Here, we show that even if direct effects of contaminants on behavioural responses are reasonably well documented, there are significant knowledge gaps in understanding both the plasticity (i.e. individual variation) and evolution of contaminant-induced behavioural changes. We explore implications of multi-level processes by developing a conceptual framework that integrates direct and indirect effects on behaviour under environmentally realistic contexts. Our framework illustrates how sublethal behavioural effects of contaminants can be both negative and positive, varying dynamically within the same individuals and populations. This is because linkages within communities will act indirectly to alter and even magnify contaminant-induced effects. Given the increasing pressure on wildlife and ecosystems from chemical pollution, we argue there is a need to incorporate existing knowledge in ecology and evolution to improve ecological hazard and risk assessments.

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Human activity alters natural habitats for many species. Understanding variation in animals' behavioural responses to these changing environments is critical. We show how signal detection theory can be used within a wider framework of state-dependent modelling to predict behavioural responses to a major environmental change: novel, exotic species. We allow thresholds for action to be a function of reserves, and demonstrate how optimal thresholds can be calculated. We term this framework 'state-dependent detection theory' (SDDT). We focus on behavioural and fitness outcomes when animals continue to use formerly adaptive thresholds following environmental change. In a simple example, we show that exposure to novel animals which appear dangerous-but are actually safe-(e.g. ecotourists) can have catastrophic consequences for 'prey' (organisms that respond as if the new organisms are predators), significantly increasing mortality even when the novel species is not predatory. SDDT also reveals that the effect on reproduction can be greater than the effect on lifespan. We investigate factors that influence the effect of novel organisms, and address the potential for behavioural adjustments (via evolution or learning) to recover otherwise reduced fitness. Although effects of environmental change are often difficult to predict, we suggest that SDDT provides a useful route ahead.

Signal detection theory has influenced the behavioural sciences for over 50 years. The theory provides a simple equation that indicates numerous 'intuitive' results; e.g. prey should be more prone to take evasive action (in response to an ambiguous cue) if predators are more common. Here, we use analytical and computational models to show that, in numerous biological scenarios, the standard results of signal detection theory do not apply; more predators can result in prey being less responsive to such cues. The standard results need not apply when the probability of danger pertains not just to the present, but also to future decisions. We identify how responses to risk should depend on background mortality and autocorrelation, and that predictions in relation to animal welfare can also be reversed from the standard theory.

Although animals vary substantially in their behavioral responses to human-induced rapid environmental change (HIREC), we are only beginning to develop theory to explain this variation. Signal detection theory predicts variation in responses to novel dangerous organisms (exotic predators or toxic prey) or exotic organisms that are safe but might appear dangerous (e.g., ecotourists). Models of dispersal and habitat use explain variation in ability to cope with habitat change (loss, fragmentation). Many models assume that organisms use one main cue axis to evaluate options. New models are needed to account for the use of multiple cues. A general framework that treats genes as cues that set a 'prior' that can be updated by experiences predicts genetic versus plastic responses to HIREC.

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