Species' responses to environmental changes such as global warming are affected not only by trends in mean conditions, but also by natural and human-induced environmental fluctuations. Methods are needed to predict how such environmental variation affects ecological and evolutionary processes, in order to design effective strategies to conserve biodiversity under global change. Here, we review recent theoretical and empirical studies to assess: (1) how populations respond to changes in environmental variance, and (2) how environmental variance affects population responses to changes in mean conditions. Contrary to frequent claims, empirical studies show that increases in environmental variance can increase as well as decrease long-term population growth rates. Moreover, environmental variance can alter and even reverse the effects of changes in the mean environment, such that even if environmental variance remains constant, omitting it from population models compromises their ability to predict species' responses to changes in mean conditions. Drawing on theory relating these effects of environmental variance to the curvatures of population growth responses to the environment, we outline how species' traits such as phylogenetic history and body mass could be used to predict their responses to global change under future environmental variability.
Summary1. Ecologists and many evolutionary biologists relate the variation in physiological, behavioural, life-history, demographic, population and community traits to the variation in weather, a key environmental driver. However, identifying which weather variables (e.g. rain, temperature, El Niño index), over which time period (e.g. recent weather, spring or year-round weather) and in what ways (e.g. mean, threshold of temperature) they affect biological responses is by no means trivial, particularly when traits are expressed at different times among individuals.2. A literature review shows that a systematic approach for identifying weather signals is lacking and that the majority of studies select weather variables from a small number of competing hypotheses that are founded on unverified a priori assumptions. This is worrying because studies that investigate the nature of weather signals in detail suggest that signals can be complex. Using suboptimal or wrongly identified weather signals may lead to unreliable projections and management decisions. 3. We propose a four-step approach that allows for more rigorous identification and quantification of weather signals (or any other predictor variable for which data are available at high temporal resolution), easily implementable with our new R package 'climwin'. We compare our approach with conventional approaches and provide worked examples. 4. Although our more exploratory approach also has some drawbacks, such as the risk of overfitting and bias that our simulations show can occur at low sample and effect sizes, these issues can be addressed with the right knowledge and tools. 5. By developing both the methods to fit critical weather windows to a wide range of biological responses and the tools to validate them and determine sample size requirements, our approach facilitates the exploration and quantification of the biological effects of weather in a rigorous, replicable and comparable way, while also providing a benchmark performance to compare other approaches to.
Summary1. The use of species distribution models to understand and predict species' distributions necessitates tests of fit to empirical data. Numerous performance metrics have been proposed, many of which require continuous occurrence probabilities to be converted to binary 'present or absent' predictions using threshold transformations. It is widely accepted that both continuous and binary performance metrics should be independent of prevalence (the proportion of locations that are occupied). However, because these metrics have been mostly assessed on a casespecific basis, there are few general guidelines for measuring performance. 2. Here, we develop a conceptual framework for classifying performance metrics, based on whether they are sensitive to prevalence, and whether they require binary predictions. We use this framework to investigate how these performance metric properties influence the predictions made by the models they select. 3. A literature survey reveals that binary metrics are widely employed and that prevalence-independent metrics are used more frequently than prevalence-dependent metrics. However, we show that prevalence-dependent metrics are essential to assess the numerical accuracy of model predictions and are more useful in applications that require occupancy estimates. Furthermore, we demonstrate that in comparison with continuous metrics, binary metrics often select models that have reduced ability to separate presences from absences, make predictions which over-or underestimate occupancy and give misleading estimates of uncertainty. Importantly, models selected using binary metrics will often be of reduced practical use even when applied to ecological problems that require binary decision-making. 4. We suggest that SDM performance should be assessed using prevalence-dependent performance metrics whenever the absolute values of occurrence predictions are important and that continuous metrics should be used instead of binary metrics whenever possible. We thus recommend the wider application of prevalencedependent continuous metrics, particularly likelihood-based metrics such as Akaike's Information Criterion (AIC), to assess the performance of presence-absence models.
Ecological responses to climate change may depend on complex patterns of variability in weather and local microclimate that overlay global increases in mean temperature. Here, we show that high-resolution temporal and spatial variability in temperature drives the dynamics of range expansion for an exemplar species, the butterfly Hesperia comma. Using fine-resolution (5 m) models of vegetation surface microclimate, we estimate the thermal suitability of 906 habitat patches at the species' range margin for 27 years. Population and metapopulation models that incorporate this dynamic microclimate surface improve predictions of observed annual changes to population density and patch occupancy dynamics during the species' range expansion from 1982 to 2009. Our findings reveal how fine-scale, short-term environmental variability drives rates and patterns of range expansion through spatially localised, intermittent episodes of expansion and contraction. Incorporating dynamic microclimates can thus improve models of species range shifts at spatial and temporal scales relevant to conservation interventions.
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