Species distribution models for a migratory bird based on citizen science and satellite tracking data

King Rail (Rallus elegans) presence in the Midwestern United States is predicted by local-scale factors and avian community

The King Rail (Rallus elegans) is a wetland dependent species of conservation concern. Our objective was to gain a better understanding of the breeding habitat associations of King Rails in the Midwestern United States and the relationship of this species to other obligate marsh birds using occupancy and MaxEnt models. To collect data pertaining to occupancy, we placed trail cameras at 50 random points in coastal wetlands in the western Lake Erie basin where calls of King Rails were continuously broadcast at night. Data pertaining to other marsh bird species were collected via call-broadcast surveys and camera surveys at each sample point. For MaxEnt modeling, we obtained presence data for King Rails and other obligate marsh birds from eBird and habitat data from GIS databases. Trail cameras and call-broadcast surveys captured 10 detections of King Rails at nine sites, an 18% naive occupancy rate. King Rail occupancy was positively related to amount of interspersion, average water depth, and percent cover of emergent vegetation at local scales within a 5-m radius. Our MaxEnt models indicated that, at a broader scale, the presence of other rail species such as the Sora (Porzana carolina) may be more important for predicting King Rail presence than other marsh birds or coarse wetland categories such as "emergent vegetation." Our results could help wetland managers to predict where King Rails occur and to adapt management plans to incorporate King Rail conservation.

Sampling methodology influences habitat suitability modeling for chiropteran species

Technological advances increase opportunities for novel wildlife survey methods. With increased detection methods, many organizations and agencies are creating habitat suitability models (HSMs) to identify critical habitats and prioritize conservation measures. However, multiple occurrence data types are used independently to create these HSMs with little understanding of how biases inherent to those data might impact HSM efficacy. We sought to understand how different data types can influence HSMs using three bat species (Lasiurus borealis, Lasiurus cinereus, and Perimyotis subflavus). We compared the overlap of models created from passive-only (acoustics), active-only (mist-netting and wind turbine mortalities), and combined occurrences to identify the effect of multiple data types and detection bias. For each species, the active-only models had the highest discriminatory ability to tell occurrence from background points and for two of the three species, active-only models preformed best at maximizing the discrimination between presence and absence values. By comparing the niche overlaps of HSMs between data types, we found a high amount of variation with no species having over 45% overlap between the models. Passive models showed more suitable habitat in agricultural lands, while active models showed higher suitability in forested land, reflecting sampling bias. Overall, our results emphasize the need to carefully consider the influences of detection and survey biases on modeling, especially when combining multiple data types or using single data types to inform management interventions. Biases from sampling, behavior at the time of detection, false positive rates, and species life history intertwine to create striking differences among models. The final model output should consider biases of each detection type, particularly when the goal is to inform management decisions, as one data type may support very different management strategies than another.

Projected Shifts in Bird Distribution in India under Climate Change

Global climate change is causing unprecedented impacts on biodiversity. In India, there is little information available regarding how climate change affects biodiversity at the taxon/group level, and large-scale ecological analyses have been lacking. In this study, we demonstrated the applicability of eBird and GBIF (Global Biodiversity Information Facility), and produced national-scale forecasts to examine the possible impacts of climate change on terrestrial avifauna in India. Using data collected by citizen scientists, we developed fine-tuned Species Distribution Models (SDMs) and predicted 1091 terrestrial bird species that would be distributed in India by 2070 on two climatic surfaces (RCP 4.5 and 8.5), using Maximum Entropy-based species distribution algorithms. Of the 1091 species modelled, our findings indicate that 66–73% of bird species in India will shift to higher elevations or shift northward, and 58–59% of bird species (RCP 4.5 and 8.5) would lose a portion of their distribution ranges. Furthermore, distribution ranges of 41–40% of bird species would increase. Under both RCP scenarios (RCP 4.5 and 8.5), bird species diversity will significantly increase in regions above 2500 m in elevation. Both RCP scenarios predict extensive changes in the species richness of the western Himalayas, Sikkim, northeast India, and the western Ghats regions by 2070. This study has resulted in novel, high-resolution maps of terrestrial bird species richness across India, and we predict predominantly northward shifts in species ranges, similar to predictions made for avifauna in other regions, such as Europe and the USA.

Nesting success and potential nest predators of the red Junglefowl (Gallus gallus jabouillei) based on camera traps and artificial nest experiments

Breeding success is an important factor determining fecundity with nest predation being the main factor limiting avian breeding success. Understanding of nest predation and its influencing factors are highly significant to explore the dynamics of bird populations and developing appropriate conservation strategies. In two breeding seasons of the year 2020 and 2021, natural nests of the red junglefowl (Gallus gallus jabouillei) were systematically searched and monitored using infrared camera, in two nature reserves (Datian and Bangxi) of tropical Hainan island, China. Results showed that breeding season of the red junglefowl is mainly from March to July, with April being the breeding peak. The clutch size was 5.15 ± 1.28 (n = 13), and nesting success of natural nests was 31.2%, with nest predation accounting for 45.4% of nest failure. Artificial nest experiments showed that predation rates of artificial nests were 25% (Datian, 2020), 6.67% (Datian, 2021), and 0% (Bangxi, 2020). Rodents, reptiles, and coucals are the main nest predators of red junglefowls, while activities of Hainan Eld’s deers (Panolia siamensis) may interfere with the reproduction of red junglefowls. We suggest that the conservation management policies should consider the impacts on junglefowls’ breeding success when reconstructing the suitable habitat of the Hainan Eld’s deer.

Habitat suitability for Dickcissels (Spiza americana) during spring and fall migration: A species distribution modeling approach

Introduction

Migratory songbirds have experienced consistent population declines over the past half-century. An important, though often overlooked, contributor to these declines is the loss and degradation of stopover areas during migration, which are critical for allowing migrants to rest and refuel, and arrive in good condition at breeding and nonbreeding grounds.

Methods

Here, we used publicly available eBird occurrence data to build species distribution models examining habitat suitability of the Dickcissel, a grassland-associated neotropical migrant, during spring and fall migration. Our models included variables representing land cover type, climate, and net primary productivity.

Results

Land cover variables were consistently better predictors of Dickcissel occurrence than climate or net primary productivity, suggesting that land cover type has a stronger impact on stopover habitat suitability than environmental factors. During spring migration, Dickcissel occurrence probability decreased with shrubland cover and precipitation seasonality. During fall migration, occurrence probability increased with proximity to water and human development, and decreased with increasing net primary productivity. We detected positive associations during both spring and fall with annual mean temperature and temperature seasonality, and a negative association with forest cover. Surprisingly, our models detected no strong associations with grassland or agriculture, despite the importance of these habitat types for Dickcissels during the breeding and nonbreeding seasons.

Discussion

Ultimately, our results suggest that Dickcissels exhibit greater flexibility when selecting stopover sites compared to breeding or nonbreeding areas, although their avoidance of forest and shrubland may indicate that they will be negatively impacted by further woody encroachment into open habitats across their migratory routes.

Integrating eDNA and citizen science observations to model distribution of a temperate freshwater turtle near its northern range limit

Background

To determine species distributions and the factors underlying them, reliable occurrence data are crucial. Assembling such data can be challenging for species with cryptic life histories or that occur at low densities.

Methods

We developed species-specific eDNA protocols, from sampling through data interpretation, to detect the common musk turtle (Sternotherus odoratus) and tested whether eDNA occurrences change our understanding of the species distribution and the factors that shape its northern range limit. We used Species Distribution Models (SDMs) with full parameter optimization on citizen science observations of S. odoratus in Southern Ontario alone and together with eDNA occurrences.

Results

Our eDNA protocol was robust and sensitive. SDMs built from traditional observations and those supplemented with eDNA detections were comparable in prediction accuracy. However, models with eDNA detections suggested that the distribution of S. odoratus in Southern Ontario is underestimated, especially near its northern range limit, and that it is shaped by thermal conditions, hydrology, and elevation. Our study underscores the promise of eDNA for surveying cryptic aquatic organisms in undocumented areas, and how such insights can help us to improve our understanding of species distributions.

Citizen science reveals waterfowl responses to extreme winter weather

Global climate change is increasing the frequency and severity of extreme climatic events (ECEs) which may be especially detrimental during late-winter when many species are surviving on scarce resources. However, monitoring animal populations relative to ECEs is logistically challenging. Crowd-sourced datasets may provide opportunity to monitor species' responses to short-term chance phenomena such as ECEs. We used 14 years of eBird-a global citizen science initiative-to examine distribution changes for seven wintering waterfowl species across North America in response to recent extreme winter polar vortex disruptions. To validate inferences from eBird, we compared eBird distribution changes against locational data from 362 GPS-tagged Mallards (Anas platyrhynchos) in the Mississippi Flyway. Distributional shifts between eBird and GPS-tagged Mallards were similar following an ECE in February 2021. In general, the ECE affected continental waterfowl population distributions; however, responses were variable across species and flyways. Waterfowl distributions tended to stay near wintering latitudes or moved north at lesser distances compared with non-ECE years, suggesting preparedness for spring migration was a stronger "pull" than extreme weather was a "push" pressure. Surprisingly, larger-bodied waterfowl with grubbing foraging strategies (i.e., geese) delayed their northward range shift during ECE years, whereas smaller-bodied ducks were less affected. Lastly, wetland obligate species shifted southward during ECE years. Collectively, these results suggest specialized foraging strategies likely related to resource limitations, but not body size, necessitate movement from extreme late-winter weather in waterfowl. Our results demonstrate eBird's potential to monitor population-level effects of weather events, especially severe ECEs. eBird and other crowd-sourced datasets can be valuable to identify species which are adaptable or vulnerable to ECEs and thus, begin to inform conservation policy and management to combat negative effects of global climate change.

Estimating the movements of terrestrial animal populations using broad-scale occurrence data

As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.

Bioacoustics for in situ validation of species distribution modelling: An example with bats in Brazil

Species distribution modelling (SDM) gained importance on biodiversity distribution and conservation studies worldwide, including prioritizing areas for public policies and international treaties. Useful for large-scale approaches and species distribution estimates, it is a plus considering that a minor fraction of the planet is adequately sampled. However, minimizing errors is challenging, but essential, considering the uses and consequences of such models. In situ validation of the SDM outputs should be a key-step—in some cases, urgent. Bioacoustics can be used to validate and refine those outputs, especially if the focal species’ vocalizations are conspicuous and species-specific. This is the case of echolocating bats. Here, we used extensive acoustic monitoring (>120 validation points over an area of >758,000 km², and producing >300,000 sound files) to validate MaxEnt outputs for six neotropical bat species in a poorly-sampled region of Brazil. Based on in situ validation, we evaluated four threshold-dependent theoretical evaluation metrics’ ability in predicting models’ performance. We also assessed the performance of three widely used thresholds to convert continuous SDMs into presence/absence maps. We demonstrated that MaxEnt produces very different outputs, requiring a careful choice on thresholds and modeling parameters. Although all theoretical evaluation metrics studied were positively correlated with accuracy, we empirically demonstrated that metrics based on specificity-sensitivity and sensitivity-precision are better for testing models, considering that most SDMs are based on unbalanced data. Without independent field validation, we found that using an arbitrary threshold for modelling can be a precarious approach with many possible outcomes, even after getting good evaluation scores. Bioacoustics proved to be important for validating SDMs for the six bat species analyzed, allowing a better refinement of SDMs in large and under-sampled regions, with relatively low sampling effort. Regardless of the species assessing method used, our research highlighted the vital necessity of in situ validation for SDMs.

Exploring hybrid consensus models to assess roadkill

Accurate information provided by reliable models is essential for identifying hotspots and mitigating roadkill. However, existing methods, such as kernel density estimation (KDE) and maximum entropy modeling (ME) may individually identify only a subset of the suitable locations for mitigation, because KDE cannot detect hotspots once local abundances are depressed, and ME may only partially identify current hotspots due to imperfect discrimination skill. Here, we propose a hybrid consensus modeling (HCM) approach that leverages the strengths of both KDE and ME by using their consensus to identify the core subset of hotspots. We collected herpetofauna (amphibians and reptiles) roadkill data (N = 839) along four roads in Taiwan (R.O.C.) to evaluate the statistical performance and theoretical mitigation efficiency of HCM, KDE and ME, and to compare the allocation among roads, spatial clustering, and environmental conditions in the identified hotspots. HCM was applied on the herpetofauna dataset as well as separately on amphibians and reptiles. Although the discrimination skill of KDE and ME models for both target clades together was good to excellent (AUC_KDE = 0.944, AUC_ME = 0.822), the highest theoretical mitigation efficiency, was displayed by HCM Consensus (2.89), followed by KDE (2.58), and ME (1.91). Furthermore, we show that theoretical mitigation efficiency increases with decreasing spatial clustering (Moran's I). Given pervasive budget constraints, we recommend to limit permanent mitigation measures such as fenced culverts to HCM Consensus hotspots, temporary measures to KDE hotspots, and to target additional monitoring at ME hotspots.

Habitat prediction modelling for vulture conservation in Gangetic-Thar-Deccan region of India

Ecologically and economically important obligate scavengers like vultures are under threat of extinction in the old world. Several resident and migratory vulture sites and individuals are hosted by the Gangetic-Thar-Deccan region of India with varied landscapes. The landscape is under threat from anthropogenic activities and climate change impacting the habitat. Therefore, habitat suitability of vultures was analysed using species distribution model, MaxEnt, ensemble of global circulation models (CCSM4, HadGEM2AO and MIROC5), citizen science and expert collected data. Altogether, 51 models were developed and their robustness was assessed to be good for conservation purpose (AUC range 0.719-0.906). Predicted unsuitable and suitable area categories of all vultures, resident vultures and migratory vultures were identified for the present and future years (2050 and 2070) under moderate and extreme emission scenarios (RCP 4.5 and RCP 8.5). The short-term and long-term area suitability change varied between 1 and 3%. Area suitability differences were also noticed among larger (global) and smaller (local) geographical areas. The bioenvironmental parameters (land use, land cover and human footprint) played a major role in habitat determination in the current scenario. Bioclimatic factors, like precipitation parameters (precipitation seasonality bio 15 and annual precipitation bio12) and temperature parameters (isothermality bio 3 and temperature seasonality bio04), were the main model determining covariates for future prediction. An earlier hypothesis of higher suitability of forest and lower suitability of agriculture area tested in this study stood modified. Implications of the results are discussed, and conservation strategies are suggested with an advice of global strategy and local execution.

From Phenology and Habitat Preferences to Climate Change: Importance of Citizen Science in Studying Insect Ecology in the Continental Scale with American Red Flat Bark Beetle, Cucujus clavipes, as a Model Species

Simple Summary

Studies of widely distributed species often are problematic as such research usually needs to engage a lot of time, a large team of field workers, and big financial support before good quality data will be collected. Citizen scientists allow to study different aspects of species biology and ecology with significantly reduced basic operational costs of such studies. Based on the data deposited in the iNaturalist database, we studied the ecology of the American flat bark beetle in the entire area of its species range. The results clearly show high value of citizen science, particularly in studies focused on habitat preferences and phenology in both recognized subspecies of this taxon.

Abstract

The American red flat bark beetle, Cucujus clavipes, is a wide distributed saproxylic species divided into two subspecies: ssp. clavipes restricted to eastern regions of North America and ssp. puniceus occurring only in western regions of this continent. Unique morphological features, including body shape and body coloration, make this species easy to recognize even for amateurs. Surprisingly, except some studies focused on physiological adaptations of the species, the ecology of C. clavipes was almost unstudied. Based on over 500 records collected by citizen scientists and deposited in the iNaturalist data base, we studied phenological activity of adult beetles, habitat preferences and impact of future climate change for both subspecies separately. The results clearly show that spp. clavipes and ssp. puniceus can be characterized by differences in phenology and macrohabitat preferences, and their ranges do not overlap at any point. Spp. clavipes is found as more opportunistic taxon occurring in different forests as well as in urban and agricultural areas with tree vegetation always in elevations below 500 m, while elevational distribution of ssp. puniceus covers areas up to 2300 m, and the beetle was observed mainly in forested areas. Moreover, we expect that climate warming will have negative influence on both subspecies with the possible loss of proper niches at level even up to 47–70% of their actual ranges during next few decades. As the species is actually recognized as unthreatened and always co-occurs with many other species, we suggest, because of its expected future habitat loss, to pay more attention to conservationists for possible negative changes in saproxylic insects and/or forest fauna in North America. In addition, as our results clearly show that both subspecies of C. clavipes differ ecologically, which strongly supports earlier significant morphological and physiological differences noted between them, we suggest that their taxonomical status should be verified by molecular data, because very probably they represent separate species.

What Will Remain? Predicting the Representation in Protected Areas of Suitable Habitat for Endangered Tropical Avifauna in Borneo under a Combined Climate- and Land-Use Change Scenario

The responses of threatened tropical avian species to projected climate change and land-use change are important for evaluating the ability of the existing protected areas to provide habitat to these species under future scenarios in biodiversity hotspots. This study uses Maxent, a species distribution model that employs a maximum entropy machine learning approach to map the spatial distributions of habitats suitable for the International Union for Conservation of Nature threatened birds under present and future climate and land-use change in Borneo. We find that the existing protected areas provide very low coverage of the threatened bird species’ suitable habitat areas (95%CI = 9.3–15.4%). Analysis of habitat suitability projections for 18 species of threatened birds suggests that in 2050, under Special Report on Emissions Scenarios A1B and B1, avian species with currently little suitable habitat may gain area but lose in the proportion of this that is protected. Large-ranged species are likely to lose habitat area and this will inflate the proportion of this remaining in protected areas. The present availability of suitable habitat was the most important determinant of future habitat availability under both the scenarios. Threat level, as measured by the International Union for Conservation of Nature and the habitat preferences considered here, Lowland or Lowland–Montane, are poor predictors of the amount of habitat contraction or expansion undergone by the species.

Coordinator Perceptions When Assessing the Impact of Citizen Science towards Sustainable Development Goals

Tracking progress towards the United Nations’ Sustainable Development Goals (SDGs) requires high-quality, timely, and accessible data, often in areas where data are rarely available. Problems exist due to socioeconomic variations between countries and the qualitative nature of certain indicators in their definition. Citizen science has the potential to contribute to several SDGs. However, whilst citizen science’s potential to contribute towards SDGs is well documented, limitations exist when measuring the impact that citizen science has made toward SDG progress. To better understand the issues and prospective solutions surrounding impact assessment towards SDG progress, this work presents the outcomes of semi-structured interviews with citizen science project coordinators. They reveal the complex nature of impact assessment within a citizen science context. Coordinators demonstrate greater confidence when the project is easier to relate to the SDGs, and the project methodology can objectively measure indicators. Issues exist, however, when considering SDGs with a broader, global context, those more difficult to link to project goals and when the project’s impact on them happens at timescales beyond the funding period. If the full potential of citizen-science contributions to the SDGs is to be realised, approaches are needed to fully consider practitioners’ needs and motivations.

Assessing biophysical and socio-economic impacts of climate change on regional avian biodiversity

Climate change threatens biodiversity directly by influencing biophysical variables that drive species' geographic distributions and indirectly through socio-economic changes that influence land use patterns, driven by global consumption, production and climate. To date, no detailed analyses have been produced that assess the relative importance of, or interaction between, these direct and indirect climate change impacts on biodiversity at large scales. Here, we apply a new integrated modelling framework to quantify the relative influence of biophysical and socio-economically mediated impacts on avian species in Vietnam and Australia and we find that socio-economically mediated impacts on suitable ranges are largely outweighed by biophysical impacts. However, by translating economic futures and shocks into spatially explicit predictions of biodiversity change, we now have the power to analyse in a consistent way outcomes for nature and people of any change to policy, regulation, trading conditions or consumption trend at any scale from sub-national to global.

Spatiotemporal Variation of Siberian Crane Habitats and the Response to Water Level in Poyang Lake Wetland, China

The Poyang Lake wetland in China is the largest wintering destination for Siberian cranes worldwide. Understanding the spatiotemporal characteristics of crane habitats is of great importance for ecological environment governance and biodiversity protection. The shallow water, grassland, and soft mudflat regions of the Poyang Lake wetland are ideal habitats for wintering Siberian cranes. Based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) remote sensing images, habitat areas were extracted and associated with various water levels taken on multiple dates. Landscape metrics were applied to describe the spatial structural characteristics of the crane habitats, and spatial statistics are used to explore the cold and hot spots of their distribution. Moreover, three indicators including sustainability, stability, and variety were applied to evaluate the vulnerability of the crane habitats under different hydrological conditions. Our findings indicate: (a) The main crane habitats exhibit a gradual decreasing degree of fragmentation in time, an obvious uncertainty of shape complexity and a relatively stable connectivity. (b) The crane habitats have a consistent spatial pattern of highly aggregated distributions associated with various water levels. (c) The hot spots of the habitats formed multiple “sheet” belts centered on the “Lake Enclosed in Autumn” regions, while the cold spots indicate a spatial pattern of axial distributions. (d) The majority of the hot spots of the habitats were distributed in sub-lakes found in the southeast part of the Poyang Lake watershed and the Nanjishan and Wucheng nature reserves, while the cold spots were mainly distributed in the main channels of the basins of Poyang Lake. (e) The sustainable habitats were mainly distributed in the “Lake Enclosed in Autumn” regions and intensively aggregated in two national nature reserves. (f) Under conditions of extremely low to average water levels (5.3–11.46 m), an increase of water level causes a decrease of the stability and variety of the crane habitats and weakens the aggregation structure.

Assessment of the American Flamingo distribution, trends, and important breeding areas

The American Flamingo, Phoenicopterus ruber, is a charismatic bird distributed throughout the Caribbean, North and South America. Its wide distribution, the complexity of international monitoring due to its capacity for long-distance flying, and a focus mostly on local populations, make it difficult to understand the dynamics between sites. Here, we took advantage of the citizen eBird science project to present a global perspective on the distribution of the American Flamingo, and identify the potentially most important countries for breeding. We obtained 16,930 records for the Americas from the 1960s until October 2018, of which 9,283 could be used for our objectives. The eBird database indicated a considerable increase in the total number of records over the last decade (2010s), probably reflecting an increase in tourism facilities, research investment, technological advancement, interest in conservation, and the worldwide availability of eBird. We also observed a range extension in the Gulf of Mexico in the United States and a significant recolonization in the Florida Peninsula. The apparent range extension to the South is more likely to be linked to biases in the data; for example, in any given country the number of records might reflect either reporting efforts or actual numbers. eBird data confirmed that six countries host the main breeding colonies (Bahamas, Bonaire, Cuba, Ecuador, Mexico, and Venezuela). We suggest three additional countries as potential breeding areas for the species (Colombia, Curaçao, Turks and Caicos Islands) for which more field observations are necessary to support this possibility. This global appraisal of the distribution of the American Flamingo using citizen science data provides valuable information for national and international management and conservation programs such as the need to verify the species breeding status in areas where it appears to be expanding its distribution.

Support for a relationship between demography and modeled habitat suitability is scale dependent for the purple martin Progne subis

Species distribution models (SDMs) estimate habitat suitability for species in geographic space. They are extensively used in conservation under the assumption that there is a positive relationship between habitat suitability and species success and stability. Given the difficulties in obtaining demographic data across a species' range, this assumption is rarely tested. Here we provide a range-wide test of this relationship for the eastern subspecies of purple martin Progne subis subis. We build a well-supported SDM for the breeding range of the purple martin, and pair it with an unparalleled demographic dataset of nest success and local and regional abundance data for the species to test the proposed link between habitat suitability and fecundity and demography. We find a positive relationship between regional abundance and habitat suitability but no relationship between local abundance or fecundity and habitat suitability. Our data suggest that local success is driven largely by biotic and stochastic factors and raise the possibility that purple martins are experiencing a time lag in their distribution. More broadly our results call for caution in how we interpret SDMs and do not support the assumption that areas of high habitat suitability are the best areas for species persistence.

Community science validates climate suitability projections from ecological niche modeling

Climate change poses an intensifying threat to many bird species and projections of future climate suitability provide insight into how species may shift their distributions in response. Climate suitability is characterized using ecological niche models (ENMs), which correlate species occurrence data with current environmental covariates and project future distributions using the modeled relationships together with climate predictions. Despite their widespread adoption, ENMs rely on several assumptions that are rarely validated in situ and can be highly sensitive to modeling decisions, precluding their reliability in conservation decision-making. Using data from a novel, large-scale community science program, we developed dynamic occupancy models to validate near-term climate suitability projections for bluebirds and nuthatches in summer and winter. We estimated occupancy, colonization, and extinction dynamics across species' ranges in the United States in relation to projected climate suitability in the 2020s, and used a Gibbs variable selection approach to quantify evidence of species-climate relationships. We also included a Bird Conservation Region strata-level random effect to examine among-strata variation in occupancy that may be attributable to land-use and ecoregional differences. Across species and seasons, we found strong evidence that initial occupancy and colonization were positively related to 2020 climate suitability, illustrating an independent validation of projections from ENMs across a large geographic area. Random strata effects revealed that occupancy probabilities were generally higher than average in core areas and lower than average in peripheral areas of species' ranges, and served as a first step in identifying spatial patterns of occupancy from these community science data. Our findings lend much-needed support to the use of ENM projections for addressing questions about potential climate-induced changes in species' occupancy dynamics. More broadly, our work highlights the value of community scientist observations for ground-truthing projections from statistical models and for refining our understanding of the processes shaping species' distributions under a changing climate.

Citizen science for predicting spatio-temporal patterns in seabird abundance during migration

Pelagic seabirds are elusive species which are difficult to observe, thus determining their spatial distribution during the migration period is a difficult task. Here we undertook the first long-term study on the distribution of migrating shearwaters from data gathered within the framework of citizen science projects. Specifically, we collected daily abundance (only abundance given presence) of Balearic shearwaters from 2005 to 2017 from the online databases Trektellen and eBird. We applied machine-learning techniques, specifically Random Forest regression models, to predict shearwater abundance during migration using 15 environmental predictors. We built separated models for pre-breeding and post-breeding migration. When evaluated for the total data sample, the models explained more than 52% of the variation in shearwater abundance. The models also showed good ability to predict shearwater distributions for both migration periods (correlation between observed and predicted abundance was about 70%). However, relative variable importance and variation among the models built with different training data subsamples differed between migration periods. Our results showed that data gathered in citizen science initiatives together with recently available high-resolution satellite imagery, can be successfully applied to describe the migratory spatio-temporal patterns of seabird species accurately. We show that a predictive modelling approach may offer a powerful and cost-effective tool for the long-term monitoring of the migratory patterns in sensitive marine species, as well as to identify at sea areas relevant for their protection. Modelling approaches can also be essential tools to detect the impacts of climate and other global changes in this and other species within the range of the training data.

Molossid unlimited: extraordinary extension of range and unusual vocalization patterns of the bat, Promops centralis

The big crested mastiff bat, Promops centralis, occurs in Central and South America, but knowledge of its ecology is limited due to its open space hunting strategy, making captures extremely challenging. Notwithstanding, members of the species produce echolocation calls that are easy to identify. After recording calls of P. centralis 1,500 km away from its known range in Brazil, we hypothesized that the distribution range of this species was probably greatly underestimated. To improve the accuracy of P. centralis’ real distribution, we employed acoustic surveys throughout parts of Brazil, conducted after a bibliographic review to gather additional records, and used MaxEnt to model the species’ potential distribution. We have found that P. centralis has a much wider distribution in South America than previously thought, adding more than 3.8 million km2 to its former known area. We also describe an unusual vocalization pattern of P. centralis, with individuals emitting at least three very distinct but highly variable calls. This study shows that bioacoustic surveys and species distribution models can complement traditional methodologies in studying species that are difficult to capture, such as P. centralis, potentially contributing to more effective conservation and management plans.

Climate change impact on ecosystem functions provided by birds in southeastern Amazonia

Although the impacts of climate change on biodiversity are increasing worldwide, few studies have attempted to forecast these impacts on Amazon Tropical Forest. In this study, we estimated the impact of climate change on Amazonian avian assemblages considering range shifts, species loss, vulnerability of ecosystem functioning, future effectiveness of current protected areas and potential climatically stable areas for conservation actions. Species distribution modelling based on two algorithms and three different scenarios of climate change was used to forecast 501 avian species, organized on main ecosystem functions (frugivores, insectivores and nectarivores) for years 2050 and 2070. Considering the entire study area, we estimated that between 4 and 19% of the species will find no suitable habitat. Inside the currently established protected areas, species loss could be over 70%. Our results suggest that frugivores are the most sensitive guild, which could bring consequences on seed dispersal functions and on natural regeneration. Moreover, we identified the western and northern parts of the study area as climatically stable. Climate change will potentially affect avian assemblages in southeastern Amazonia with detrimental consequences to their ecosystem functions. Information provided here is essential to conservation practitioners and decision makers to help on planning their actions.