Linking demography with drivers: climate and competition

Weather and leaf age separately contribute to temporal shifts in phyllosphere community structure and composition

Microbial communities living on plant leaves can positively or negatively influence plant health and, by extension, can impact whole ecosystems. Most research into the leaf microbiome consists of snapshots, and little is known about how microbial communities change over time. Weather and host physiological characteristics change over time and are often collinear with other time-varying factors, such as substrate availability, making it difficult to separate the factors driving microbial community change. We leveraged repeated measures over the course of an entire year to isolate the relative importance of environmental, host physiological, and substrate age-related factors on the assembly, structure, and composition of leaf-associated fungal communities. We applied both culturing and sequencing approaches to investigate these communities, focusing on a foundational, widely-distributed plant of conservation concern: basin big sagebrush ( Artemisia tridentata subsp. tridentata ). We found that changes in alpha diversity were independently affected by the age of a community and the air temperature. Surprisingly, total fungal abundance and species richness were not positively correlated and responded differently, sometimes oppositely, to weather. With regard to beta diversity, communities were more similar to each other across similar leaf ages, air temperatures, leaf types, and δ 13 C stable isotope ratios. Nine different genera were differentially abundant with air temperature, δ 13 C, leaf type, and leaf age, and a set of 20 genera were continuously present across the year. Our findings highlight the necessity for longer-term, repeated sampling to parse drivers of temporal change in leaf microbial communities.

Open Research Statement

All ITS DNA amplicon sequence raw data are deposited in the NCBI Sequence Read Archive (SRA), BioProject number PRJNA1107252, data will be released upon publication. All community data, metadata, taxonomic data, and R code necessary to reproduce these results are deposited in the GitHub repository archived on Zenodo: 10.5281/zenodo.11106439.

Density-dependent species interactions modulate alpine treeline shifts

Species interactions such as facilitation and competition play a crucial role in driving species range shifts. However, density dependence as a key feature of these processes has received little attention in both empirical and modelling studies. Herein, we used a novel, individual-based treeline model informed by rich in situ observations to quantify the contribution of density-dependent species interactions to alpine treeline dynamics, an iconic biome boundary recognized as an indicator of global warming. We found that competition and facilitation dominate in dense versus sparse vegetation scenarios respectively. The optimal balance between these two effects was identified at an intermediate vegetation thickness where the treeline elevation was the highest. Furthermore, treeline shift rates decreased sharply with vegetation thickness and the associated transition from positive to negative species interactions. We thus postulate that vegetation density must be considered when modelling species range dynamics to avoid inadequate predictions of its responses to climate warming.

Inclusive Fitness May Explain Some but Not All Benefits Derived from Helping Behavior in a Cooperatively Breeding Bird

AbstractIn cooperative breeding systems, inclusive fitness theory predicts that nonbreeding helpers more closely related to the breeders should be more willing to provide costly alloparental care and thus have more impact on breeder fitness. In the red-cockaded woodpecker (Dryobates borealis), most helpers are the breeders' earlier offspring, but helpers do vary within groups in both relatedness to the breeders (some even being unrelated) and sex, and it can be difficult to parse their separate impacts on breeder fitness. Moreover, most support for inclusive fitness theory has been positive associations between relatedness and behavior rather than actual fitness consequences. We used functional linear models to evaluate the per capita effects of helpers of different relatedness on eight breeder fitness components measured for up to 41 years at three sites. In support of inclusive fitness theory, helpers more related to the breeding pair made greater contributions to six fitness components. However, male helpers made equal contributions to increasing prefledging survival regardless of relatedness. These findings suggest that both inclusive fitness benefits and other direct benefits may underlie helping behaviors in the red-cockaded woodpecker. Our results also demonstrate the application of an underused statistical approach to disentangle a complex ecological phenomenon.

Estimating survival in continuous space from mark-dead-recovery data - Towards a continuous version of the multinomial dead recovery model

Understanding spatially varying survival is crucial for understanding the ecology and evolution of migratory animals, which may ultimately help to conserve such species. We develop an approach to estimate an annual survival probability function varying continuously in geographic space, if the recovery probability is constant over space. This estimate is based on a density function over continuous geographic space and the discrete age at death obtained from dead recovery data. From the same density function, we obtain an estimate for animal distribution in space corrected for survival, i.e., migratory connectivity. This is possible, when migratory connectivity can be separated from recovery probability. In this article, we present the method how spatially and continuously varying survival and the migratory connectivity corrected for survival can be obtained, if a constant recovery probability can be assumed reasonably. The model is a stepping stone in developing a model allowing for disentangling spatially heterogeneous survival and migratory connectivity corrected for survival from a spatially heterogeneous recovery probability. We implement the method using kernel density estimates in the R-package CONSURE. Any other density estimation technique can be used as an alternative. In a simulation study, the estimators are unbiased but show edge effects in survival and migratory connectivity. Applying the method to a real-world data set of European robins Erithacus rubecula results in biologically reasonable continuous heat-maps for survival and migratory connectivity.

Delayed effects of climate on vital rates lead to demographic divergence in Amazonian forest fragments

Deforestation often results in landscapes where remaining forest habitat is highly fragmented, with remnants of different sizes embedded in an often highly contrasting matrix. Local extinction of species from individual fragments is common, but the demographic mechanisms underlying these extinctions are poorly understood. It is often hypothesized that altered environmental conditions in fragments drive declines in reproduction, recruitment, or survivorship. The Amazon basin, in addition to experiencing continuing fragmentation, is experiencing climate change-related increases in the frequency and intensity of droughts and unusually wet periods. Whether plant populations in tropical forest fragments are particularly susceptible to extremes in precipitation remains unclear. Most studies of plants in fragments are relatively short (1-6 years), focus on a single life-history stage, and often do not compare to populations in continuous forest. Even fewer studies consider delayed effects of climate on demographic vital rates despite the importance of delayed effects in studies that consider them. Using a decade of demographic and climate data from an experimentally fragmented landscape in the Central Amazon, we assess the effects of climate on populations of an understory herb (Heliconia acuminata, Heliconiaceae). We used distributed lag nonlinear models to understand the delayed effects of climate (measured as standardized precipitation evapotranspiration index, SPEI) on survival, growth, and flowering. We detected delayed effects of climate up to 36 months. Extremes in SPEI in the previous year reduced survival, drought in the wet season 8-11 months prior to the February census increased growth, and drought two dry seasons prior increased flowering probability. Effects of extremes in precipitation on survival and growth were more pronounced in forest fragments compared to continuous forest. The complex delayed effects of climate and habitat fragmentation in our study point to the importance of long-term demography experiments in understanding the effects of anthropogenic change on plant populations.

A practical guide to selecting models for exploration, inference, and prediction in ecology

Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.

Lagged and dormant season climate better predict plant vital rates than climate during the growing season

Understanding the effects of climate on the vital rates (e.g., survival, development, reproduction) and dynamics of natural populations is a long-standing quest in ecology, with ever-increasing relevance in the face of climate change. However, linking climate drivers to demographic processes requires identifying the appropriate time windows during which climate influences vital rates. Researchers often do not have access to the long-term data required to test a large number of windows, and are thus forced to make a priori choices. In this study, we first synthesize the literature to assess current a priori choices employed in studies performed on 104 plant species that link climate drivers with demographic responses. Second, we use a sliding-window approach to investigate which combination of climate drivers and temporal window have the best predictive ability for vital rates of four perennial plant species that each have over a decade of demographic data (Helianthella quinquenervis, Frasera speciosa, Cylindriopuntia imbricata, and Cryptantha flava). Our literature review shows that most studies consider time windows in only the year preceding the measurement of the vital rate(s) of interest, and focus on annual or growing season temporal scales. In contrast, our sliding-window analysis shows that in only four out of 13 vital rates the selected climate drivers have time windows that align with, or are similar to, the growing season. For many vital rates, the best window lagged more than 1 year and up to 4 years before the measurement of the vital rate. Our results demonstrate that for the vital rates of these four species, climate drivers that are lagged or outside of the growing season are the norm. Our study suggests that considering climatic predictors that fall outside of the most recent growing season will improve our understanding of how climate affects population dynamics.

Larger workers outperform smaller workers across resource environments: An evaluation of demographic data using functional linear models

Behavior and organization of social groups is thought to be vital to the functioning of societies, yet the contributions of various roles within social groups toward population growth and dynamics have been difficult to quantify. A common approach to quantifying these role-based contributions is evaluating the number of individuals conducting certain roles, which ignores how behavior might scale up to effects at the population-level. Manipulative experiments are another common approach to determine population-level effects, but they often ignore potential feedbacks associated with these various roles.Here, we evaluate the effects of worker size distribution in bumblebee colonies on worker production in 24 observational colonies across three environments, using functional linear models. Functional linear models are an underused correlative technique that has been used to assess lag effects of environmental drivers on plant performance. We demonstrate potential applications of this technique for exploring high-dimensional ecological systems, such as the contributions of individuals with different traits to colony dynamics.We found that more larger workers had mostly positive effects and more smaller workers had negative effects on worker production. Most of these effects were only detected under low or fluctuating resource environments suggesting that the advantage of colonies with larger-bodied workers becomes more apparent under stressful conditions.We also demonstrate the wider ecological application of functional linear models. We highlight the advantages and limitations when considering these models, and how they are a valuable complement to many of these performance-based and manipulative experiments.

Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale

Life history strategies are fundamental to the ecology and evolution of organisms and are important for understanding extinction risk and responses to global change. Using global datasets and a multiple response modelling framework we show that trait-climate interactions are associated with life history strategies for a diverse range of plant species at the global scale. Our modelling framework informs our understanding of trade-offs and positive correlations between elements of life history after accounting for environmental context and evolutionary and trait-based constraints. Interactions between plant traits and climatic context were needed to explain variation in age at maturity, distribution of mortality across the lifespan and generation times of species. Mean age at maturity and the distribution of mortality across plants' lifespan were under evolutionary constraints. These findings provide empirical support for the theoretical expectation that climatic context is key to understanding trait to life history relationships globally.

Strengthening the evidence base for temperature-mediated phenological asynchrony and its impacts

Climate warming has caused the seasonal timing of many components of ecological food chains to advance. In the context of trophic interactions, the match-mismatch hypothesis postulates that differential shifts can lead to phenological asynchrony with negative impacts for consumers. However, at present there has been no consistent analysis of the links between temperature change, phenological asynchrony and individual-to-population-level impacts across taxa, trophic levels and biomes at a global scale. Here, we propose five criteria that all need to be met to demonstrate that temperature-mediated trophic asynchrony poses a growing risk to consumers. We conduct a literature review of 109 papers studying 129 taxa, and find that all five criteria are assessed for only two taxa, with the majority of taxa only having one or two criteria assessed. Crucially, nearly every study was conducted in Europe or North America, and most studies were on terrestrial secondary consumers. We thus lack a robust evidence base from which to draw general conclusions about the risk that climate-mediated trophic asynchrony may pose to populations worldwide.

Using Climate to Explain and Predict West Nile Virus Risk in Nebraska

We used monthly precipitation and temperature data to give early warning of years with higher West Nile Virus (WNV) risk in Nebraska. We used generalized additive models with a negative binomial distribution and smoothing curves to identify combinations of extremes and timing that had the most influence, experimenting with all combinations of temperature and drought data, lagged by 12, 18, 24, 30, and 36 months. We fit models on data from 2002 through 2011, used Akaike's Information Criterion (AIC) to select the best-fitting model, and used 2012 as out-of-sample data for prediction, and repeated this process for each successive year, ending with fitting models on 2002-2017 data and using 2018 for out-of-sample prediction. We found that warm temperatures and a dry year preceded by a wet year were the strongest predictors of cases of WNV. Our models did significantly better than random chance and better than an annual persistence naïve model at predicting which counties would have cases. Exploring different scenarios, the model predicted that without drought, there would have been 26% fewer cases of WNV in Nebraska through 2018; without warm temperatures, 29% fewer; and with neither drought nor warmth, 45% fewer. This method for assessing the influence of different combinations of extremes at different time intervals is likely applicable to diseases other than West Nile, and to other annual outcome variables such as crop yield.

Assessing seasonal demographic covariation to understand environmental-change impacts on a hibernating mammal

Natural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development and reproduction). The resulting covariation in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor-analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual-based demography from yellow-bellied marmots (Marmota flaviventer) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneously, negative responses of winter survival and reproductive-status change to declining environmental quality result in a higher risk of population quasi-extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments.

Cumulative weather effects can impact across the whole life cycle

Predicting how species will be affected by future climatic change requires the underlying environmental drivers to be identified. As vital rates vary over the lifecycle, structured population models derived from statistical environment-demography relationships are often used to inform such predictions. Environmental drivers are typically identified independently for different vital rates and demographic classes. However, these rates often exhibit positive temporal covariance, suggesting that vital rates respond to common environmental drivers. Additionally, models often only incorporate average weather conditions during a single, a priori chosen time window (e.g. monthly means). Mismatches between these windows and the period when the vital rates are sensitive to variation in climate decrease the predictive performance of such approaches. We used a demographic structural equation model (SEM) to demonstrate that a single axis of environmental variation drives the majority of the (co)variation in survival, reproduction, and twinning across six age-sex classes in a Soay sheep population. This axis provides a simple target for the complex task of identifying the drivers of vital rate variation. We used functional linear models (FLMs) to determine the critical windows of three local climatic drivers, allowing the magnitude and direction of the climate effects to differ over time. Previously unidentified lagged climatic effects were detected in this well-studied population. The FLMs had a better predictive performance than selecting a critical window a priori, but not than a large-scale climate index. Positive covariance amongst vital rates and temporal variation in the effects of environmental drivers are common, suggesting our SEM-FLM approach is a widely applicable tool for exploring the joint responses of vital rates to environmental change.

Filling in the gaps in survival analysis: using field data to infer plant responses to environmental stressors

Elucidating how organismal survival depends on the environment is a core component of ecological and evolutionary research. To reconcile high-frequency covariates with lower-frequency demographic censuses, many statistical tools involve aggregating environmental conditions over long periods, potentially obscuring the importance of fluctuating conditions in driving mortality. Here, we introduce a flexible model designed to infer how survival probabilities depend on changing environmental covariates. Specifically, the model (1) quantifies effects of environmental covariates at a higher frequency than the census intervals, and (2) allows partitioning of environmental drivers of individual survival into acute (short-term) and chronic (accumulated) effects. By applying our method to a long-term observational data set of eight annual plant species, we show we can accurately infer daily survival probabilities as temperature and moisture levels change. Next, we show that a species' water use efficiency, known to mediate annual plant population dynamics, is positively correlated with the importance of "chronic stress" inferred by the model. This suggests that model parameters can reflect underlying physiological mechanisms. This method is also applicable to other binary responses (hatching, phenology) or systems (insects, nestlings). Once known, environmental sensitivities can be used for ecological forecasting even when the frequency or variability of environments are changing.

Shifting Vital Rate Correlations Alter Predicted Population Responses to Increasingly Variable Environments

Time series of vital rates are often used to construct "environment-blind" stochastic population projections and calculate the elasticity of population growth to increased temporal variance in vital rates. Here, we show that the utility of this widely used demographic tool is greatly limited by shifts in vital rate correlations that occur as environmental drivers become increasingly variable. The direction and magnitude of these shifts are unpredictable without environmentally explicit models. Shifting vital rate correlations had the largest fitness effects on life histories with short to medium generation times, potentially hampering comparative analyses based on elasticities to vital rate variance for a wide range of species. Shifts in vital rate correlations are likely ubiquitous in increasingly variable environments, and further research should empirically evaluate the life histories for which detailed mechanistic relationships between vital rates and environmental drivers are required for making reliable predictions versus those for which summarized demographic data are sufficient.

Phenological sequences: how early-season events define those that follow

PREMISE OF THE STUDY

Plant phenology is a critical trait, as the timings of phenophases such as budburst, leafout, flowering, and fruiting, are important to plant fitness. Despite much study about when individual phenophases occur and how they may shift with climate change, little is known about how multiple phenophases relate to one another across an entire growing season. We test the extent to which early phenological stages constrain later ones, throughout a growing season, across 25 angiosperm tree species.

METHODS

We observed phenology (budburst, leafout, flowering, fruiting, and senescence) of 118 individual trees across 25 species, from April through December 2015.

KEY RESULTS

We found that early phenological events weakly constrain most later events, with the strongest constraints seen between consecutive stages. In contrast, interphase duration was a much stronger predictor of phenology, especially for reproductive events, suggesting that the development time of flowers and fruits may constrain the phenology of these events.

CONCLUSIONS

Much of the variation in later phenological events can be explained by the timing of earlier events and by interphase durations. This highlights that a shift in one phenophase may often have cascading effects on later phases. Accurate forecasts of climate change impacts should therefore include multiple phenophases within and across years.

Delivering the promises of trait-based approaches to the needs of demographic approaches, and vice versa

Few facets of biology vary more than functional traits and life-history traits. To explore this vast variation, functional ecologists and population ecologists have developed independent approaches that identify the mechanisms behind and consequences of trait variation.Collaborative research between researchers using trait-based and demographic approaches remains scarce. We argue that this is a missed opportunity, as the strengths of both approaches could help boost the research agendas of functional ecology and population ecology.This special feature, which spans three journals of the British Ecological Society due to its interdisciplinary nature, showcases state-of-the-art research applying trait-based and demographic approaches to examine relationships between organismal function, life history strategies and population performance across multiple kingdoms. Examples include the exploration of how functional trait × environment interactions affect vital rates and thus explain population trends and species occurrence; the coordination of seed traits and dispersal ability with the pace of life in plants; the incorporation of functional traits in dynamic energy budget models; or the discovery of linkages between microbial functional traits and the fast-slow continuum.Despite their historical isolation, collaborative work between functional ecologists and population ecologists could unlock novel research pathways. We call for an integrative research agenda to evaluate which and when traits are functional, as well as their ability to describe and predict life history strategies and population dynamics. We highlight promising, complementary research avenues to overcome current limitations. These include a more explicit linkage of selection gradients in the context of functional trait-vital rate relationships, and the implementation of standardised protocols to track changes in traits and vital rates over time at the same location and individuals, thus allowing for the explicit incorporation of trade-offs in analyses of covariation of functional traits and life-history traits.

Time-lagged effects of weather on plant demography: drought and Astragalus scaphoides

Temperature and precipitation determine the conditions where plant species can occur. Despite their significance, to date, surprisingly few demographic field studies have considered the effects of abiotic drivers. This is problematic because anticipating the effect of global climate change on plant population viability requires understanding how weather variables affect population dynamics. One possible reason for omitting the effect of weather variables in demographic studies is the difficulty in detecting tight associations between vital rates and environmental drivers. In this paper, we applied Functional Linear Models (FLMs) to long-term demographic data of the perennial wildflower, Astragalus scaphoides, and explored sensitivity of the results to reduced amounts of data. We compared models of the effect of average temperature, total precipitation, or an integrated measure of drought intensity (standardized precipitation evapotranspiration index, SPEI), on plant vital rates. We found that transitions to flowering and recruitment in year t were highest if winter/spring of year t was wet (positive effect of SPEI). Counterintuitively, if the preceding spring of year t - 1 was wet, flowering probabilities were decreased (negative effect of SPEI). Survival of vegetative plants from t - 1 to t was also negatively affected by wet weather in the spring of year t - 1 and, for large plants, even wet weather in the spring of t - 2 had a negative effect. We assessed the integrated effect of all vital rates on life history performance by fitting FLMs to the asymptotic growth rate, log(λt). Log(λt) was highest if dry conditions in year t - 1 were followed by wet conditions in the year t. Overall, the positive effects of wet years exceeded their negative effects, suggesting that increasing frequency of drought conditions would reduce population viability of A. scaphoides. The drought signal weakened when reducing the number of monitoring years. Substituting space for time did not recover the weather signal, probably because the weather variables varied little between sites. We detected the SPEI signal when the analysis included data from two sites monitored over 20 yr (2 × 20 observations), but not when analyzing data from four sites monitored over 10 yr (4 × 10 observations).

Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.

Does climate variability influence the demography of wild primates? Evidence from long-term life-history data in seven species

Earth's rapidly changing climate creates a growing need to understand how demographic processes in natural populations are affected by climate variability, particularly among organisms threatened by extinction. Long-term, large-scale, and cross-taxon studies of vital rate variation in relation to climate variability can be particularly valuable because they can reveal environmental drivers that affect multiple species over extensive regions. Few such data exist for animals with slow life histories, particularly in the tropics, where climate variation over large-scale space is asynchronous. As our closest relatives, nonhuman primates are especially valuable as a resource to understand the roles of climate variability and climate change in human evolutionary history. Here, we provide the first comprehensive investigation of vital rate variation in relation to climate variability among wild primates. We ask whether primates are sensitive to global changes that are universal (e.g., higher temperature, large-scale climate oscillations) or whether they are more sensitive to global change effects that are local (e.g., more rain in some places), which would complicate predictions of how primates in general will respond to climate change. To address these questions, we use a database of long-term life-history data for natural populations of seven primate species that have been studied for 29-52 years to investigate associations between vital rate variation, local climate variability, and global climate oscillations. Associations between vital rates and climate variability varied among species and depended on the time windows considered, highlighting the importance of temporal scale in detection of such effects. We found strong climate signals in the fertility rates of three species. However, survival, which has a greater impact on population growth, was little affected by climate variability. Thus, we found evidence for demographic buffering of life histories, but also evidence of mechanisms by which climate change could affect the fates of wild primates.

Does climate variability influence the demography of wild primates? Evidence from long-term life-history data in seven species

Earth's rapidly changing climate creates a growing need to understand how demographic processes in natural populations are affected by climate variability, particularly among organisms threatened by extinction. Long-term, large-scale, and cross-taxon studies of vital rate variation in relation to climate variability can be particularly valuable because they can reveal environmental drivers that affect multiple species over extensive regions. Few such data exist for animals with slow life histories, particularly in the tropics, where climate variation over large-scale space is asynchronous. As our closest relatives, nonhuman primates are especially valuable as a resource to understand the roles of climate variability and climate change in human evolutionary history. Here, we provide the first comprehensive investigation of vital rate variation in relation to climate variability among wild primates. We ask whether primates are sensitive to global changes that are universal (e.g., higher temperature, large-scale climate oscillations) or whether they are more sensitive to global change effects that are local (e.g., more rain in some places), which would complicate predictions of how primates in general will respond to climate change. To address these questions, we use a database of long-term life-history data for natural populations of seven primate species that have been studied for 29-52 years to investigate associations between vital rate variation, local climate variability, and global climate oscillations. Associations between vital rates and climate variability varied among species and depended on the time windows considered, highlighting the importance of temporal scale in detection of such effects. We found strong climate signals in the fertility rates of three species. However, survival, which has a greater impact on population growth, was little affected by climate variability. Thus, we found evidence for demographic buffering of life histories, but also evidence of mechanisms by which climate change could affect the fates of wild primates.

Detecting population-environmental interactions with mismatched time series data

Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population-environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates.

climwin: An R Toolbox for Climate Window Analysis

When studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.

Confronting species distribution model predictions with species functional traits

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.