Natural history collections as windows on evolutionary processes

Natural history collections provide an immense record of biodiversity on Earth. These repositories have traditionally been used to address fundamental questions in biogeography, systematics and conservation. However, they also hold the potential for studying evolution directly. While some of the best direct observations of evolution have come from long-term field studies or from experimental studies in the laboratory, natural history collections are providing new insights into evolutionary change in natural populations. By comparing phenotypic and genotypic changes in populations through time, natural history collections provide a window into evolutionary processes. Recent studies utilizing this approach have revealed some dramatic instances of phenotypic change over short timescales in response to presumably strong selective pressures. In some instances, evolutionary change can be paired with environmental change, providing a context for potential selective forces. Moreover, in a few cases, the genetic basis of phenotypic change is well understood, allowing for insight into adaptive change at multiple levels. These kinds of studies open the door to a wide range of previously intractable questions by enabling the study of evolution through time, analogous to experimental studies in the laboratory, but amenable to a diversity of species over longer timescales in natural populations.

Characteristics of temporal changes in communities where dynamics differ between species

Communities with different phenotypic variation among species can have identical species abundance distributions, although their temporal dynamics may be very different. By using stochastic species abundance models, both the lognormal and beta prime abundance distributions can be obtained with either homogeneous or heterogeneous dynamics among species. Assuming that anthropogenic activity disturbs the communities such that species' carrying capacities are decreasing deterministically, the structure of the communities are studied using simulations. In order to construct homogeneous communities with reasonable variation in abundance, the parameter values describing the dynamics of the species can be unrealistic in terms of long return times to equilibrium. Species in heterogeneous communities can have stronger density regulation, while maintaining the same variation in abundance, by assuming heterogeneity in one of the dynamical parameters. The heterogeneity generates variation in carrying capacity among species, while reducing the temporal stochasticity. If carrying capacity decreases, changes in community structure occur at a much slower rate for the homogeneous compared to the heterogeneous communities. Even over short time periods, the difference in response to deterministic changes in carrying capacity between homogeneous and heterogeneous community models can be substantial, making the heterogeneous model a recommended starting point for community analysis.

Making spatial prioritizations robust to climate change uncertainties: a case study with North American birds

Spatial prioritizations are essential tools for conserving biodiversity in the face of accelerating climate change. Uncertainty about species' responses to changing climates can complicate prioritization efforts, however, and delay conservation investment. In an effort to facilitate decision-making, we identified three hypotheses about species' potential responses to climate change based on distinct biological assumptions related to niche flexibility and colonization ability. Using 314 species of North American birds as a test case, we tuned separate spatial prioritizations to each hypothesis and assessed the degree to which assumptions about biological responses affected the perceived conservation value of the landscape and prospects for individual taxa. We also developed a bet-hedging prioritization to minimize the chance that incorrect assumptions would lead to valuable landscapes and species being overlooked in multispecies prioritizations. Collectively, these analyses help to quantify the sensitivity of spatial prioritizations to different assumptions about species' responses to climate change and provide a framework for enabling efficient conservation investment despite substantial biological uncertainty.

Evaluating species richness: Biased ecological inference results from spatial heterogeneity in detection probabilities

Accurate estimates of species richness are necessary to test predictions of ecological theory and evaluate biodiversity for conservation purposes. However, species richness is difficult to measure in the field because some species will almost always be overlooked due to their cryptic nature or the observer's failure to perceive their cues. Common measures of species richness that assume consistent observability across species are inviting because they may require only single counts of species at survey sites. Single-visit estimation methods ignore spatial and temporal variation in species detection probabilities related to survey or site conditions that may confound estimates of species richness. We used simulated and empirical data to evaluate the bias and precision of raw species counts, the limiting forms of jackknife and Chao estimators, and multispecies occupancy models when estimating species richness to evaluate whether the choice of estimator can affect inferences about the relationships between environmental conditions and community size under variable detection processes. Four simulated scenarios with realistic and variable detection processes were considered. Results of simulations indicated that (1) raw species counts were always biased low, (2) single-visit jackknife and Chao estimators were significantly biased regardless of detection process, (3) multispecies occupancy models were more precise and generally less biased than the jackknife and Chao estimators, and (4) spatial heterogeneity resulting from the effects of a site covariate on species detection probabilities had significant impacts on the inferred relationships between species richness and a spatially explicit environmental condition. For a real data set of bird observations in northwestern Alaska, USA, the four estimation methods produced different estimates of local species richness, which severely affected inferences about the effects of shrubs on local avian richness. Overall, our results indicate that neglecting the effects of site covariates on species detection probabilities may lead to significant bias in estimation of species richness, as well as the inferred relationships between community size and environmental covariates.

The effect of range changes on the functional turnover, structure and diversity of bird assemblages under future climate scenarios

Animal assemblages fulfill a critical set of ecological functions for ecosystems that may be altered substantially as climate change-induced distribution changes lead to community disaggregation and reassembly. We combine species and community perspectives to assess the consequences of projected geographic range changes for the diverse functional attributes of avian assemblages worldwide. Assemblage functional structure is projected to change highly unevenly across space. These differences arise from both changes in the number of species and changes in species' relative local functional redundancy or distinctness. They sometimes result in substantial losses of functional diversity that could have severe consequences for ecosystem health. Range expansions may counter functional losses in high-latitude regions, but offer little compensation in many tropical and subtropical biomes. Future management of local community function and ecosystem services thus relies on understanding the global dynamics of species distributions and multiscale approaches that include the biogeographic context of species traits.

Elevational distribution and extinction risk in birds

Mountainous regions are hotspots of terrestrial biodiversity. Unlike islands, which have been the focus of extensive research on extinction dynamics, fewer studies have examined mountain ranges even though they face increasing threats from human pressures – notably habitat conversion and climate change. Limits to the taxonomic and geographical extent and resolution of previously available information have precluded an explicit assessment of the relative role of elevational distribution in determining extinction risk. We use a new global species-level avian database to quantify the influence of elevational distribution (range, maximum and midpoint) on extinction risk in birds at the global scale. We also tested this relationship within biogeographic realms, higher taxonomic levels, and across phylogenetic contrasts. Potential confounding variables (i.e. phylogenetic, distributional, morphological, life history and niche breadth) were also tested and controlled for. We show that the three measures of elevational distribution are strong negative predictors of avian extinction risk, with elevational range comparable and complementary to that of geographical range size. Extinction risk was also found to be positively associated with body weight, development and adult survival, but negatively associated with reproduction and niche breadth. The robust and consistent findings from this study demonstrate the importance of elevational distribution as a key driver of variation in extinction dynamics in birds. Our results also highlight elevational distribution as a missing criterion in current schemes for quantifying extinction risk and setting species conservation priorities in birds. Further research is recommended to test for generality across non-avian taxa, which will require an advance in our knowledge of species’ current elevational ranges and increased efforts to digitise and centralise such data.

Climate change in our backyards: the reshuffling of North America's winter bird communities

Much of the recent changes in North American climate have occurred during the winter months, and as result, overwintering birds represent important sentinels of anthropogenic climate change. While there is mounting evidence that bird populations are responding to a warming climate (e.g., poleward shifts) questions remain as to whether these species-specific responses are resulting in community-wide changes. Here, we test the hypothesis that a changing winter climate should favor the formation of winter bird communities dominated by warm-adapted species. To do this, we quantified changes in community composition using a functional index--the Community Temperature Index (CTI)--which measures the balance between low- and high-temperature dwelling species in a community. Using data from Project FeederWatch, an international citizen science program, we quantified spatiotemporal changes in winter bird communities (n = 38 bird species) across eastern North America and tested the influence of changes in winter minimum temperature over a 22-year period. We implemented a jackknife analysis to identify those species most influential in driving changes at the community level and the population dynamics (e.g., extinction or colonization) responsible for these community changes. Since 1990, we found that the winter bird community structure has changed with communities increasingly composed of warm-adapted species. This reshuffling of winter bird communities was strongest in southerly latitudes and driven primarily by local increases in abundance and regional patterns of colonization by southerly birds. CTI tracked patterns of changing winter temperature at different temporal scales ranging from 1 to 35 years. We conclude that a shifting winter climate has provided an opportunity for smaller, southerly distributed species to colonize new regions and promote the formation of unique winter bird assemblages throughout eastern North America.

Estimating the spatial and temporal distribution of species richness within Sequoia and Kings Canyon National Parks

Evidence for significant losses of species richness or biodiversity, even within protected natural areas, is mounting. Managers are increasingly being asked to monitor biodiversity, yet estimating biodiversity is often prohibitively expensive. As a cost-effective option, we estimated the spatial and temporal distribution of species richness for four taxonomic groups (birds, mammals, herpetofauna (reptiles and amphibians), and plants) within Sequoia and Kings Canyon National Parks using only existing biological studies undertaken within the Parks and the Parks' long-term wildlife observation database. We used a rarefaction approach to model species richness for the four taxonomic groups and analyzed those groups by habitat type, elevation zone, and time period. We then mapped the spatial distributions of species richness values for the four taxonomic groups, as well as total species richness, for the Parks. We also estimated changes in species richness for birds, mammals, and herpetofauna since 1980. The modeled patterns of species richness either peaked at mid elevations (mammals, plants, and total species richness) or declined consistently with increasing elevation (herpetofauna and birds). Plants reached maximum species richness values at much higher elevations than did vertebrate taxa, and non-flying mammals reached maximum species richness values at higher elevations than did birds. Alpine plant communities, including sagebrush, had higher species richness values than did subalpine plant communities located below them in elevation. These results are supported by other papers published in the scientific literature. Perhaps reflecting climate change: birds and herpetofauna displayed declines in species richness since 1980 at low and middle elevations and mammals displayed declines in species richness since 1980 at all elevations.

Accounting for imperfect detection in ecology: a quantitative review

Detection in studies of species abundance and distribution is often imperfect. Assuming perfect detection introduces bias into estimation that can weaken inference upon which understanding and policy are based. Despite availability of numerous methods designed to address this assumption, many refereed papers in ecology fail to account for non-detection error. We conducted a quantitative literature review of 537 ecological articles to measure the degree to which studies of different taxa, at various scales, and over time have accounted for imperfect detection. Overall, just 23% of articles accounted for imperfect detection. The probability that an article incorporated imperfect detection increased with time and varied among taxa studied; studies of vertebrates were more likely to incorporate imperfect detection. Among articles that reported detection probability, 70% contained per-survey estimates of detection that were less than 0.5. For articles in which constancy of detection was tested, 86% reported significant variation. We hope that our findings prompt more ecologists to consider carefully the detection process when designing studies and analyzing results, especially for sub-disciplines where incorporation of imperfect detection in study design and analysis so far has been lacking.

Beyond a warming fingerprint: individualistic biogeographic responses to heterogeneous climate change in California

Understanding recent biogeographic responses to climate change is fundamental for improving our predictions of likely future responses and guiding conservation planning at both local and global scales. Studies of observed biogeographic responses to 20th century climate change have principally examined effects related to ubiquitous increases in temperature - collectively termed a warming fingerprint. Although the importance of changes in other aspects of climate - particularly precipitation and water availability - is widely acknowledged from a theoretical standpoint and supported by paleontological evidence, we lack a practical understanding of how these changes interact with temperature to drive biogeographic responses. Further complicating matters, differences in life history and ecological attributes may lead species to respond differently to the same changes in climate. Here, we examine whether recent biogeographic patterns across California are consistent with a warming fingerprint. We describe how various components of climate have changed regionally in California during the 20th century and review empirical evidence of biogeographic responses to these changes, particularly elevational range shifts. Many responses to climate change do not appear to be consistent with a warming fingerprint, with downslope shifts in elevation being as common as upslope shifts across a number of taxa and many demographic and community responses being inconsistent with upslope shifts. We identify a number of potential direct and indirect mechanisms for these responses, including the influence of aspects of climate change other than temperature (e.g., the shifting seasonal balance of energy and water availability), differences in each taxon's sensitivity to climate change, trophic interactions, and land-use change. Finally, we highlight the need to move beyond a warming fingerprint in studies of biogeographic responses by considering a more multifaceted view of climate, emphasizing local-scale effects, and including a priori knowledge of relevant natural history for the taxa and regions under study.

Assessing the utility of statistical adjustments for imperfect detection in tropical conservation science

In recent years, there has been a fast development of models that adjust for imperfect detection. These models have revolutionized the analysis of field data, and their use has repeatedly demonstrated the importance of sampling design and data quality. There are, however, several practical limitations associated with the use of detectability models which restrict their relevance to tropical conservation science.

We outline the main advantages of detectability models, before examining their limitations associated with their applicability to the analysis of tropical communities, rare species and large‐scale data sets. Finally, we discuss whether detection probability needs to be controlled before and/or after data collection.

Models that adjust for imperfect detection allow ecologists to assess data quality by estimating uncertainty and to obtain adjusted ecological estimates of populations and communities. Importantly, these models have allowed informed decisions to be made about the conservation and management of target species.

Data requirements for obtaining unadjusted estimates are substantially lower than for detectability‐adjusted estimates, which require relatively high detection/recapture probabilities and a number of repeated surveys at each location. These requirements can be difficult to meet in large‐scale environmental studies where high levels of spatial replication are needed, or in the tropics where communities are composed of many naturally rare species. However, while imperfect detection can only be adjusted statistically, covariates of detection probability can also be controlled through study design. Using three study cases where we controlled for covariates of detection probability through sampling design, we show that the variation in unadjusted ecological estimates from nearly 100 species was qualitatively the same as that obtained from adjusted estimates. Finally, we discuss that the decision as to whether one should control for covariates of detection probability through study design or statistical analyses should be dependent on study objectives.

Synthesis and applications. Models that adjust for imperfect detection are an important part of an ecologist's toolkit, but they should not be uniformly adopted in all studies. Ecologists should never let the constraints of models dictate which questions should be pursued or how the data should be analysed, and detectability models are no exception. We argue for pluralism in scientific methods, particularly where cost‐effective applied ecological science is needed to inform conservation policy at a range of different scales and in many different systems.

Models that adjust for imperfect detection are an important part of an ecologist's toolkit, but they should not be uniformly adopted in all studies. Ecologists should never let the constraints of models dictate which questions should be pursued or how the data should be analysed, and detectability models are no exception. We argue for pluralism in scientific methods, particularly where cost‐effective applied ecological science is needed to inform conservation policy at a range of different scales and in many different systems.

Guidelines for a priori grouping of species in hierarchical community models

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process "borrows" from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.

Using historical and experimental data to reveal warming effects on ant assemblages

Historical records of species are compared with current records to elucidate effects of recent climate change. However, confounding variables such as succession, land-use change, and species invasions make it difficult to demonstrate a causal link between changes in biota and changes in climate. Experiments that manipulate temperature can overcome this issue of attribution, but long-term impacts of warming are difficult to test directly. Here we combine historical and experimental data to explore effects of warming on ant assemblages in southeastern US. Observational data span a 35-year period (1976–2011), during which mean annual temperatures had an increasing trend. Mean summer temperatures in 2010–2011 were ∼2.7°C warmer than in 1976. Experimental data come from an ongoing study in the same region, for which temperatures have been increased ∼1.5–5.5°C above ambient from 2010 to 2012. Ant species richness and evenness decreased with warming under natural but not experimental warming. These discrepancies could have resulted from differences in timescales of warming, abiotic or biotic factors, or initial species pools. Species turnover tended to increase with temperature in observational and experimental datasets. At the species level, the observational and experimental datasets had four species in common, two of which exhibited consistent patterns between datasets. With natural and experimental warming, collections of the numerically dominant, thermophilic species, Crematogaster lineolata, increased roughly two-fold. Myrmecina americana, a relatively heat intolerant species, decreased with temperature in natural and experimental warming. In contrast, species in the Solenopsis molesta group did not show consistent responses to warming, and Temenothorax pergandei was rare across temperatures. Our results highlight the difficulty of interpreting community responses to warming based on historical records or experiments alone. Because some species showed consistent responses to warming based on thermal tolerances, understanding functional traits may prove useful in explaining responses of species to warming.