North American Breeding Bird Survey Research Articles

Modeling avian full annual cycle distribution and population trends with citizen science data.

Information on species’ distributions, abundances, and how they change over time is central to the study of the ecology and conservation of animal populations. This information is challenging to obtain at landscape scales across range‐wide extents for two main reasons. First, landscape‐scale processes that affect populations vary throughout the year and across species’ ranges, requiring high‐resolution, year‐round data across broad, sometimes hemispheric, spatial extents. Second, while citizen science projects can collect data at these resolutions and extents, using these data requires appropriate analysis to address known sources of bias. Here, we present an analytical framework to address these challenges and generate year‐round, range‐wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long‐distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate occurrence and abundance across a range of spatial scales throughout the annual cycle. Additionally, we generate intra‐annual estimates of the range, intra‐annual estimates of the associations between species and characteristics of the landscape, and interannual trends in abundance for breeding and non‐breeding seasons. The range‐wide population trajectories for Wood Thrush show a close correspondence between breeding and non‐breeding seasons with steep declines between 2010 and 2013 followed by shallower rates of decline from 2013 to 2016. The breeding season range‐wide population trajectory based on the independently collected and analyzed North American Breeding Bird Survey data also shows this pattern. The information provided here fills important knowledge gaps for Wood Thrush, especially during the less studied migration and non‐breeding periods. More generally, the modeling framework presented here can be used to accurately capture landscape scale intra‐ and interannual distributional dynamics for broadly distributed, highly mobile species.

Landsat 8 TIRS-derived relative temperature and thermal heterogeneity predict winter bird species richness patterns across the conterminous United States

The thermal environment limits species ranges through its influence on physiology and resource distributions and thus affects species richness patterns over broad spatial scales. Understanding how temperature drives species richness patterns is particularly important in the context of global change and for effective conservation planning. Landsat 8's Thermal Infrared Sensor (TIRS) allows direct mapping of temperature at moderate spatial resolutions (100 m, downscaled by the USGS to 30 m), overcoming limitations inherent in coarse interpolated weather station data that poorly capture fine-scale temperature patterns over broad areas. TIRS data thus offer the unique opportunity to understand how the thermal environment influences species richness patterns. Our aim was to develop and assess the ability of TIRS-based temperature metrics to predict patterns of winter bird richness across the conterminous United States during winter, a period of marked temperature stress for birds. We used TIRS data from 2013-2018 to derive metrics of relative temperature and intra-seasonal thermal heterogeneity. To quantify winter bird richness across the conterminous US, we tabulated the richness only for resident bird species, i.e., those species that do not move between the winter and breeding seasons, from the North American Breeding Bird Survey, the most extensive survey of birds in the US. We expected that relative temperature and thermal heterogeneity would have strong positive associations with winter bird richness because colder temperatures heighten temperature stress for birds, and thermal heterogeneity is a proxy for thermal niches and potential thermal refugia that can support more species. We further expected that both the strength of the effects and the relative importance of these variables would be greater for species with greater climate sensitivity, such as small-bodied species and climate-threatened species (i.e., those with large discrepancies between their current and future distributions following projected climate change). Consistent with our predictions, relative temperature and thermal heterogeneity strongly positively influenced winter bird richness patterns, with statistical models explaining 37.3% of the variance in resident bird richness. Thermal heterogeneity was the strongest predictor of small-bodied and climate-threatened species in our models, whereas relative temperature was the strongest predictor of large-bodied and climate-stable species. Our results demonstrate the important role that the thermal environment plays in governing winter bird richness patterns and highlight the previously underappreciated role that intra-seasonal thermal heterogeneity may have in supporting high winter bird species richness. Our findings thus illustrate the exciting potential for TIRS data to guide conservation planning in an era of global change.

Using lorelograms to measure and model correlation in binary data: Applications to ecological studies

Abstract Tools for describing correlation structures in binary data are underrepresented in the ecological literature, and methods commonly applied to continuous data are inappropriate because of intrinsic features of binary data (e.g. variance dependent on the mean). Describing how correlation changes in time and space, or in response to different stimuli, can provide insights into the ecological processes generating observed patterns. Moreover, proper modelling of correlation structures is necessary for reliably estimating covariate effects. We introduce ecologists to the lorelogram (Heagerty & Zeger, 1998), a tool used to identify and describe dependency structures in binary data. Lorelograms can help guide data aggregation efforts to facilitate independence, or alternatively, to inform appropriate structures for modelling correlated data. We developed the r‐package, lorelogram, for estimating and plotting lorelograms and show how it can be used to identify various dependency structures using simulated data. We analyse data from the North American Breeding Bird Survey and camera trap data from a carnivore study in Minnesota to demonstrate how the lorelogram can describe spatial and temporal dependencies, respectively. In the latter case, we show how the lorelogram can identify short‐term dependencies (e.g. individuals that linger at the camera site for several minutes), longer‐term dependencies (i.e. diel activity patterns) and effects of site‐specific covariates such as attractants. We then illustrate how this information can be incorporated in a modelling framework that accounts for these correlation structures (e.g. when modelling daily activity patterns). The lorelogram is a promising tool for quantifying correlation in binary data over space or time. In addition to the applications presented in our paper, it could be used to identify independent sampling units for occupancy modelling or to quantify behavioural responses to covariates such as anthropogenic stressors or recent presence of prey, predators or competitors.

The Influence of Population Growth and Wind on Vagrancy in a North American Passerine

Understanding the causes of vagrancy among migratory bird species is of increasing importance as climate change threatens species' survival. Vagrancy may serve to safeguard populations from environmental change through expansion of their geographic ranges. To dissect underlying causes of vagrancy, we analysed data on occurrence of vagrant Ash-throated Flycatchers Myiarchus cinerascens to the east coast of North America and population growth within their core breeding range, to test to what extent vagrancy is driven by population growth and the production of young that have a proclivity to explore new places. We also tested to what extent vagrancy is related to drift by prevailing winds, through analysis of synoptic weather maps of North America. Our analyses aimed to quantify which factors most strongly influence interannual variation in the number of Ash-throated Flycatchers reaching the east coast of North America. We obtained records of vagrants from ‘North American Birds’, population data from the North American Breeding Bird Survey (BBS), reproductive success from Monitoring Avian Productivity and Survivorship (MAPS) databases, and synoptic weather maps from the NOAA NCEP North American Regional Reanalysis database. We found that vagrancy was predominantly explained by the growing breeding population size as indexed by BBS data. In addition, we found significant effects of annual production of young within the breeding range, as well as three measures of air circulation across North America. Our models indicated an important role of population growth, with additional effects of reproductive success and predominant airflow affecting the incidence of vagrancy. Years of high reproductive success bring larger numbers of Ash-throated Flycatchers to the east, and this number is enhanced when weather conditions are especially favourable.

Modeling spatially and temporally complex range dynamics when detection is imperfect

Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range dynamics. In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. The model is flexible and can accommodate data from a range of sampling designs that provide information about both occupancy and detection probability. Output from the model can be used to create distribution maps and to estimate indices of temporal range dynamics. We demonstrate the utility of this approach by modeling long-term range dynamics of 10 eastern North American birds using data from the North American Breeding Bird Survey. We anticipate this framework will be particularly useful for modeling species’ distributions over large spatial scales and for quantifying range dynamics over long temporal scales.

Recent global changes have decoupled species richness from specialization patterns in North American birds

AbstractAimTheory suggests that increasing productivity and climate stability towards the tropics favours specialization, thus contributing to the latitudinal richness gradient. A positive relationship between species richness and specialization should therefore emerge as a fundamental biogeographical pattern. However, land‐use and climate changes disproportionally increase the local extirpation risk for specialists, potentially weakening the relationship between richness and specialization. Here, we quantify empirically the richness–specialization prediction and test how 50 years of climate and land‐use change has affected the richness–specialization relationship.LocationUSA.Time period1966–2015.Major taxa studiedBirds.MethodsWe used the North American Breeding Bird Survey to quantify bird community richness and specialization to habitat and climate. We (a) quantify temporal change in the slope of the richness–specialization relationship, using a generalized mixed model; (b) assess how this change translates spatially, using generalized additive models; and (c) attribute spatio‐temporal change in the richness–specialization relationship to land use, climate and topographic drivers.ResultsWe found evidence for a positive but weak richness–specialization relationship in bird communities that greatly weakened over time. Given that specialization was not the main driver of richness, this relationship did not translate spatially into a linear spatial covariation between richness and specialization. Instead, the spatial covariation in richness and specialization followed a unimodal pattern, the peak of which shifted towards less specialized communities over time. These temporal changes were associated with precipitation change, decreasing temperature stability and land use.Main conclusionsRecent climate and land‐use changes have induced two contrasting types of community responses. In human‐dominated areas, the decoupling of richness and specialization drove a general trend for biotic homogenization. In areas of low human impact experiencing increasing climate harshness, specialization increased, whereas richness decreased. Our results offer new support for specialization as a key driver of macroecological diversity patterns and show that global changes are weakening this fundamental macroecological pattern.

Enhancing VGI application semantics by accounting for spatial bias

ABSTRACTVolunteered geographic information (VGI) is becoming an important source of geospatial big data that support many applications. The application semantics of VGI, i.e. how well VGI reflects the real-world geographic phenomena of interest to the application, is essential for any VGI applications. VGI observations often are spatially biased (e.g. spatially clustered). Spatial bias poses challenges on VGI application semantics because it may impede the quality of inferences made from VGI. Using species distribution modeling (SDM) as an example application, this article argues that spatial bias impedes VGI application semantics, as gauged by SDM model performance, and accounting for bias enhances application semantics. VGI observations from eBird were used in a case study for modeling the distribution of the American Robin (Turdus migratorius) in U.S. T. migratorius observations from the North American Breeding Bird Survey were used as independent validation data for model performance evaluation. A grid-based strategy was adopted to filter eBird species observations to reduce spatial bias. Evaluations show that spatial bias in species observations degrades SDM model performance and filtering species observations improves model performance. This study demonstrates that VGI application semantics can be enhanced by accounting for the spatial bias in VGI observations.

Species-specific and temporal scale-dependent responses of birds to drought.

Global climate change is increasing the frequency and intensity of weather extremes, including severe droughts in many regions. Drought can impact organisms by inhibiting reproduction, reducing survival and abundance, and forcing range shifts. For birds, considering temporal scale by averaging drought-related variables over different time lengths (i.e., temporal grains) captures different hydrologic attributes which may uniquely influence food supplies, vegetation greenness/structure, and other factors affecting populations. However, studies examining drought impacts on birds often assess a single temporal grain without considering that different species have different life histories that likely determine the temporal grain of their drought response. Furthermore, while drought is known to influence bird abundance and drive between-year range shifts, less understood is whether it causes within-range changes in species distributions. Our objectives were to (a) determine which temporal grain of drought (if any) is most related to bird presence/absence and whether this response is species specific; and (b) assess whether drought alters bird distributions by quantifying probability of local colonization and extinction as a function of drought intensity. We used North American Breeding Bird Survey data collected over 16years, generalized linear mixed models, and dynamic occupancy models to meet these objectives. Different bird species responded to drought at different temporal grains, with most showing the strongest signal at annual or near-annual grains. For all drought-responsive species, increased drought intensity at any temporal grain always correlated with decreased occupancy. Additionally, colonization/extinction analyses indicated that one species, the dickcissel (Spiza americana), is more likely to colonize novel areas within the southern/core portion of its range during drought. Considering drought at different temporal grains, along with hydrologic attributes captured by each grain, may better reveal mechanisms behind drought impacts on birds and other organisms, and therefore improve understanding of how global climate change impacts species and the landscapes they inhabit.

Mind the gap: The Painted Bunting (Passerina ciris) breeds in central Alabama and eastern Mississippi

Ornithologists have long been fascinated by an apparent disjunction in the breeding distribution of the Painted Bunting (Passerina ciris), with previous studies reporting a 550 km gap between a coastal population along the south Atlantic and an interior population in the southcentral United States. However, these studies have overlooked breeders in the Black Belt Ecoregion of central Alabama and eastern Mississippi. Using data from our own fieldwork, the North American Breeding Bird Survey, eBird, and breeding bird atlas projects, we document that the Painted Bunting occurs within the gap described in previous publications. The presence of these birds raises further questions regarding the status of the Painted Bunting in the Southeast and may be relevant to future research and conservation efforts targeting this species.

Modeling effects of crop production, energy development and conservation-grassland loss on avian habitat.

Birds are essential components of most ecosystems and provide many services valued by society. However, many populations have undergone striking declines as their habitats have been lost or degraded by human activities. Terrestrial grasslands are vital habitat for birds in the North American Prairie Pothole Region (PPR), but grassland conversion and fragmentation from agriculture and energy-production activities have destroyed or degraded millions of hectares. Conservation grasslands can provide alternate habitat. In the United States, the Conservation Reserve Program (CRP) is the largest program maintaining conservation grasslands on agricultural lands, but conservation grasslands in the PPR have declined by over 1 million ha since the program’s zenith in 2007. We used an ecosystem-services model (InVEST) parameterized for the PPR to quantify grassland-bird habitat remaining in 2014 and to assess the degradation status of the remaining grassland-bird habitat as influenced by crop and energy (i.e., oil, natural gas, and wind) production. We compared our resultant habitat-quality ratings to grassland-bird abundance data from the North American Breeding Bird Survey to confirm that ratings were related to grassland-bird abundance. Of the grassland-bird habitat remaining in 2014, about 19% was degraded by crop production that occurred within 0.1 km of grassland habitats, whereas energy production degraded an additional 16%. We further quantified the changes in availability of grassland-bird habitat under various land-cover scenarios representing incremental losses (10%, 25%, 50%, 75%, and 100%) of CRP grasslands from 2014 levels. Our model identified 1 million ha (9%) of remaining grassland-bird habitat in the PPR that would be lost or degraded if all CRP conservation grasslands were returned to crop production. Grassland regions world-wide face similar challenges in maintaining avian habitat in the face of increasing commodity and energy production to sate the food and energy needs of a growing world population. Identifying ways to model the impacts of the tradeoff between food and energy production and wildlife production is an important step in creating solutions.

BbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey

bbsAssistant: An R package for downloading and handling data and information from the North American Breeding Bird Survey

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference (design based, model based), and scientific paradigms (confirmatory, exploratory). We distinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass‐participation internet‐based projects with opportunistic data collection lacking sampling design, and examined overarching objectives, design, analysis, volunteer training, and performance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high‐quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state‐of‐the‐art analytical methods, and well‐supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

Hindcasting the impacts of land-use changes on bird communities with species distribution models of Bird Atlas data.

Habitat loss and degradation induced by human development are among the major threats to biodiversity worldwide. In this study, we tested our ability to predict the response of bird communities (128 species) to land-use changes in southern Quebec (~483,100km2 ) over the last 30yr (between 1984-1989 and 2010-2014) by using species distribution models (299,302 occurrences in 30,408 locations) from a hindcasting perspective. Results were grouped by functional guilds to infer potential impacts on ecosystem services, and to relate model transferability (i.e., ability of our models to be generalized to other times and scales) to specific functional and life-history traits. Overall, our models were able to accurately predict, both in space and time, habitat suitability for 69% of species, especially for granivorous, nonmigrant, tree-nesting species, and species that are tied to agricultural areas under intensive use. These findings indicate that model transferability depends upon specific functional and life-history traits, providing further evidence that species' ecologies affect the ability of models to accurately predict bird distributions. Declining bird species were mostly short-distance migrants that were associated with open habitats (agricultural and nonproductive forest) with aerial insectivorous or granivorous diets, which may be related to agricultural intensification and land abandonment. Land-use changes were positive for some forest bird species that were mainly associated with mixed and deciduous forests, generalist diets and tree-nesting strategies. Yet cavity-nesting birds have suffered substantial reductions in their distributions, suggesting that cumulative effects of intensive logging and wildfires on mature forests pose a threat for forest-specialist species. Habitat suitability changes predicted by our coarse-scale species distribution models partially agreed with the long-term trends reported by the North American Breeding Bird Survey. Our findings confirm land-use change as a key driving force for shaping bird communities in southern Quebec, together with the need to explicitly incorporate it into global change scenarios that better inform decision-makers on conservation and management.

Extending the Latent Dirichlet Allocation model to presence/absence data: A case study on North American breeding birds and biogeographical shifts expected from climate change.

Understanding how species composition varies across space and time is fundamental to ecology. While multiple methods having been created to characterize this variation through the identification of groups of species that tend to co‐occur, most of these methods unfortunately are not able to represent gradual variation in species composition. The Latent Dirichlet Allocation (LDA) model is a mixed‐membership method that can represent gradual changes in community structure by delineating overlapping groups of species, but its use has been limited because it requires abundance data and requires users to a priori set the number of groups. We substantially extend LDA to accommodate widely available presence/absence data and to simultaneously determine the optimal number of groups. Using simulated data, we show that this model is able to accurately determine the true number of groups, estimate the underlying parameters, and fit with the data. We illustrate this method with data from the North American Breeding Bird Survey (BBS). Overall, our model identified 18 main bird groups, revealing striking spatial patterns for each group, many of which were closely associated with temperature and precipitation gradients. Furthermore, by comparing the estimated proportion of each group for two time periods (1997–2002 and 2010–2015), our results indicate that nine (of 18) breeding bird groups exhibited an expansion northward and contraction southward of their ranges, revealing subtle but important community‐level biodiversity changes at a continental scale that are consistent with those expected under climate change. Our proposed method is likely to find multiple uses in ecology, being a valuable addition to the toolkit of ecologists.

Temporal changes in abundance exhibit less spatial structure than abundance itself in North American birds

Species abundance is often spatially structured such that, within a species’ distribution, abundance peaks at one or more areas and declines from those points. Abundance may also increase or decrease over time, but the spatial structure of temporal changes in abundance has been infrequently examined. Here we use count data from the North American Breeding Bird Survey (BBS) to describe the spatial structure of yearly changes in abundance across the distributions of 135 species of land birds. For each species, we calculated the difference in the logarithms of the number of birds counted from one year to the next at survey routes within their distributions. We assessed the spatial structure of yearly changes in abundance using Moran’s I, a measure of spatial autocorrelation. For comparison, we also calculated Moran’s I for abundance, i.e., the logarithm of the number of birds counted on survey routes in each year. As expected, abundance was positively spatially autocorrelated (i.e. closer routes were more similar than expected by chance) at distances of up to a few hundred kilometers for most species. In contrast, changes in abundance showed little to no spatial autocorrelation. Resident species exhibited greater spatial structure in abundance than migrant species; however, the two groups did not differ in the degree of spatial structure in change in abundance. Variation in abundance over multiple years was mostly unrelated to the distance from the abundance-weighted center of species’ distributions. Temporal changes in abundance can occur at fine spatial scales for many species, but understanding the causes of such changes is challenging. These results fill a gap in the ecological literature and may have important implications for conservation planning.

The importance of core habitat for a threatened species in changing landscapes

Abstract Habitat loss, fragmentation, and alteration of the landscape matrix are interdependent processes, collectively responsible for most recent species extinctions. Thus, determining the extent to which these landscape processes affect animals is critical for conservation. However, researchers have often assumed that interdependent effects are independently related to animals’ responses, underestimating the importance of one or several landscape processes in driving species declines. We demonstrate how to disentangle the interdependent effects of habitat amount, fragmentation, and edge context on population size by assessing abundance of a rapidly declining grassland songbird species (grasshopper sparrow Ammodramus savannarum) in eastern Kansas (USA). We conducted >7,000 point count surveys at >2,000 sites over two breeding seasons, then modelled the direct, interactive, and indirect effects of landscape factors on abundance within spatial scales (200‐, 400‐, 800‐, and 1,600‐m radii) relevant to our focal species’ dispersal behaviour. Sparrow abundance correlated most strongly with landscape structure within 400‐m radii, increasing nonlinearly with grassland area and decreasing with the proportion of grassland near cropland or woody edges. Sparrows’ negative response to cropland edges was mostly an added, indirect consequence of reduced grassland area, whereas sparrows’ stronger negative response to woody edges was not attributable to variation in grassland area. Fragmentation and edge context mattered most in landscapes comprising c. 50%–80% grassland. Synthesis and applications. In our research, abundance of a threatened grassland songbird was influenced more by core grassland area (a function of total grassland area, fragmentation, and edge context) than total grassland area per se. Moreover, a local extinction threshold of c. 50% grassland indicated that small amounts of habitat were unsuitable for our focal species regardless of habitat configuration or matrix type. Local extinction thresholds in response to habitat area provide clear baseline targets for land managers; above those thresholds, configuration and the matrix can be modified to increase abundance of edge‐sensitive animals. Conflicting evidence in the literature regarding the importance of fragmentation and matrix features could be partially explained by species‐level traits, or methodological issues such as defining landscapes at ecologically arbitrary spatial scales, assessing landscape quality using species richness, and ignoring interactive and indirect effects.

Taxonomic and functional diversity change is scale dependent

Estimates of recent biodiversity change remain inconsistent, debated, and infrequently assessed for their functional implications. Here, we report that spatial scale and type of biodiversity measurement influence evidence of temporal biodiversity change. We show a pervasive scale dependence of temporal trends in taxonomic (TD) and functional (FD) diversity for an ~50-year record of avian assemblages from North American Breeding Bird Survey and a record of global extinctions. Average TD and FD increased at all but the global scale. Change in TD exceeded change in FD toward large scales, signaling functional resilience. Assemblage temporal dissimilarity and turnover (replacement of species or functions) declined, while nestedness (tendency of assemblages to be subsets of one another) increased with scale. Patterns of FD change varied strongly among diet and foraging guilds. We suggest that monitoring, policy, and conservation require a scale-explicit framework to account for the pervasive effect that scale has on perceived biodiversity change.

Opportunities and challenges for big data ornithology

ABSTRACT Recent advancements in information technology and data acquisition have created both new research opportunities and new challenges for using big data in ornithology. We provide an overview of the past, present, and future of big data in ornithology, and explore the rewards and risks associated with their application. Structured data resources (e.g., North American Breeding Bird Survey) continue to play an important role in advancing our understanding of bird population ecology, and the recent advent of semistructured (e.g., eBird) and unstructured (e.g., weather surveillance radar) big data resources has promoted the development of new empirical perspectives that are generating novel insights. For example, big data have been used to study and model bird diversity and distributions across space and time, explore the patterns and determinants of broad-scale migration strategies, and examine the dynamics and mechanisms associated with geographic and phenological responses to global change. The appli...

Projected avifaunal responses to climate change across the U.S. National Park System.

Birds in U.S. national parks find strong protection from many longstanding and pervasive threats, but remain highly exposed to effects of ongoing climate change. To understand how climate change is likely to alter bird communities in parks, we used species distribution models relating North American Breeding Bird Survey (summer) and Audubon Christmas Bird Count (winter) observations to climate data from the early 2000s and projected to 2041–2070 (hereafter, mid-century) under high and low greenhouse gas concentration trajectories, RCP8.5 and RCP2.6. We analyzed climate suitability projections over time for 513 species across 274 national parks, classifying them as improving, worsening, stable, potential colonization, and potential extirpation. U.S. national parks are projected to become increasingly important for birds in the coming decades as potential colonizations exceed extirpations in 62–100% of parks, with an average ratio of potential colonizations to extirpations of 4.1 in winter and 1.4 in summer under RCP8.5. Average species turnover is 23% in both summer and winter under RCP8.5. Species turnover (Bray-Curtis) and potential colonization and extirpation rates are positively correlated with latitude in the contiguous 48 states. Parks in the Midwest and Northeast are expected to see particularly high rates of change. All patterns are more extreme under RCP8.5 than under RCP2.6. Based on the ratio of potential colonization and extirpation, parks were classified into overall trend groups associated with specific climate-informed conservation strategies. Substantial change to bird and ecological communities is anticipated in coming decades, and current thinking suggests managing towards a forward-looking concept of ecological integrity that accepts change and novel ecological conditions, rather than focusing management goals exclusively on maintaining or restoring a static set of historical conditions.

Misleading prioritizations from modelling range shifts under climate change

AbstractAimConservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated whether species distribution models could reliably rank changes in species range size under climate and land use change.LocationConterminous U.S.A.Time period1977–2014.Major taxa studiedPasserine birds.MethodsWe estimated ensembles of species distribution models based on historical North American Breeding Bird Survey occurrences for 190 songbirds, and generated predictions to recent years given c. 35 years of observed land use and climate change. We evaluated model predictions using standard metrics of discrimination performance and a more detailed assessment of the ability of models to rank species vulnerability to climate change based on predicted range loss, range gain, and overall change in range size.ResultsSpecies distribution models yielded unreliable and misleading assessments of relative vulnerability to climate and land use change. Models could not accurately predict range expansion or contraction, and therefore failed to anticipate patterns of range change among species. These failures occurred despite excellent overall discrimination ability and transferability to the validation time period, which reflected strong performance at the majority of locations that were either always or never occupied by each species.Main conclusionsModels failed for the questions and at the locations of greatest interest to conservation and management. This highlights potential pitfalls of multi‐taxa impact assessments under global change; in our case, models provided misleading rankings of the most impacted species, and spatial information about range changes was not credible. As modelling methods and frameworks continue to be refined, performance assessments and validation efforts should focus on the measures of risk and vulnerability useful for decision‐making.