Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data

Effects of climate on the distribution and conservation of commonly observed European earthworms

Belowground biodiversity distribution does not necessarily reflect aboveground biodiversity patterns, but maps of soil biodiversity remain scarce because of limited data availability. Earthworms belong to the most thoroughly studied soil organisms and-in their role as ecosystem engineers-have a significant impact on ecosystem functioning. We used species distribution modeling (SDMs) and available data sets to map the spatial distribution of commonly observed (i.e., frequently recorded) earthworm species (Annelida, Oligochaeta) across Europe under current and future climate conditions. First, we predicted potential species distributions with commonly used models (i.e., MaxEnt and Biomod) and estimated total species richness (i.e., number of species in a 5 × 5 km grid cell). Second, we determined how much the different types of protected areas covered predicted earthworm richness and species ranges (i.e., distributions) by estimating the respective proportion of the range area. Earthworm species richness was high in central western Europe and low in northeastern Europe. This pattern was mainly associated with annual mean temperature and precipitation seasonality, but the importance of predictor variables to species occurrences varied among species. The geographical ranges of the majority of the earthworm species were predicted to shift to eastern Europe and partly decrease under future climate scenarios. Predicted current and future ranges were only poorly covered by protected areas, such as national parks. More than 80% of future earthworm ranges were on average not protected at all (mean [SD] = 82.6% [0.04]). Overall, our results emphasize the urgency of considering especially vulnerable earthworm species, as well as other soil organisms, in the design of nature conservation measures.

Geographic And Taxonomic Occurrence R-based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data

Premise

Digitized biodiversity data offer extensive information; however, obtaining and processing biodiversity data can be daunting. Complexities arise during data cleaning, such as identifying and removing problematic records. To address these issues, we created the R package Geographic And Taxonomic Occurrence R-based Scrubbing (gatoRs).

Methods and Results

The gatoRs workflow includes functions that streamline downloading records from the Global Biodiversity Information Facility (GBIF) and Integrated Digitized Biocollections (iDigBio). We also created functions to clean downloaded specimen records. Unlike previous R packages, gatoRs accounts for differences in download structure between GBIF and iDigBio and allows for user control via interactive cleaning steps.

Conclusions

Our pipeline enables the scientific community to process biodiversity data efficiently and is accessible to the R coding novice. We anticipate that gatoRs will be useful for both established and beginning users. Furthermore, we expect our package will facilitate the introduction of biodiversity-related concepts into the classroom via the use of herbarium specimens.

Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists

Biodiversity monitoring usually involves drawing inferences about some variable of interest across a defined landscape from observations made at a sample of locations within that landscape. If the variable of interest differs between sampled and nonsampled locations, and no mitigating action is taken, then the sample is unrepresentative and inferences drawn from it will be biased. It is possible to adjust unrepresentative samples so that they more closely resemble the wider landscape in terms of "auxiliary variables." A good auxiliary variable is a common cause of sample inclusion and the variable of interest, and if it explains an appreciable portion of the variance in both, then inferences drawn from the adjusted sample will be closer to the truth. We applied six types of survey sample adjustment-subsampling, quasirandomization, poststratification, superpopulation modeling, a "doubly robust" procedure, and multilevel regression and poststratification-to a simple two-part biodiversity monitoring problem. The first part was to estimate the mean occupancy of the plant Calluna vulgaris in Great Britain in two time periods (1987-1999 and 2010-2019); the second was to estimate the difference between the two (i.e., the trend). We estimated the means and trend using large, but (originally) unrepresentative, samples from a citizen science dataset. Compared with the unadjusted estimates, the means and trends estimated using most adjustment methods were more accurate, although standard uncertainty intervals generally did not cover the true values. Completely unbiased inference is not possible from an unrepresentative sample without knowing and having data on all relevant auxiliary variables. Adjustments can reduce the bias if auxiliary variables are available and selected carefully, but the potential for residual bias should be acknowledged and reported.

Ecological niche contributes to the persistence of the Western x Glaucous-winged Gull hybrid zone

Hybrid zones occur in nature when populations with limited reproductive barriers overlap in space. Many hybrid zones persist over time, and different models have been proposed to explain how selection can maintain hybrid zone stability. More empirical studies are needed to elucidate the role of ecological adaptation in maintaining stable hybrid zones. Here, we investigated the role of exogenous factors in maintaining a hybrid zone between western gulls (Larus occidentalis) and glaucous-winged gulls (L. glaucescens). We used ecological niche models (ENMs) and niche similarity tests to quantify and examine the ecological niches of western gulls, glaucous-winged gulls, and their hybrids. We found evidence of niche divergence between all three groups. Our results best support the bounded superiority model, providing further evidence that exogenous selection favoring hybrids may be an important factor in maintaining this stable hybrid zone.

Meta-analysis reveals less sensitivity of non-native animals than natives to extreme weather worldwide

Extreme weather events (EWEs; for example, heatwaves, cold spells, storms, floods and droughts) and non-native species invasions are two major threats to global biodiversity and are increasing in both frequency and consequences. Here we synthesize 443 studies and apply multilevel mixed-effects metaregression analyses to compare the responses of 187 non-native and 1,852 native animal species across terrestrial, freshwater and marine ecosystems to different types of EWE. Our results show that marine animals, regardless of whether they are non-native or native, are overall insensitive to EWEs, except for negative effects of heatwaves on native mollusks, corals and anemone. By contrast, terrestrial and freshwater non-native animals are only adversely affected by heatwaves and storms, respectively, whereas native animals negatively respond to heatwaves, cold spells and droughts in terrestrial ecosystems and are vulnerable to most EWEs except cold spells in freshwater ecosystems. On average, non-native animals displayed low abundance in terrestrial ecosystems, and decreased body condition and life history traits in freshwater ecosystems, whereas native animals displayed declines in body condition, life history traits, abundance, distribution and recovery in terrestrial ecosystems, and community structure in freshwater ecosystems. By identifying areas with high overlap between EWEs and EWE-tolerant non-native species, we also provide locations where native biodiversity might be adversely affected by their joint effects and where EWEs might facilitate the establishment and/or spread of non-native species under continuing global change.

An operational workflow for producing periodic estimates of species occupancy at national scales

Policy makers require high-level summaries of biodiversity change. However, deriving such summaries from raw biodiversity data is a complex process involving several intermediary stages. In this paper, we describe an operational workflow for generating annual estimates of species occupancy at national scales from raw species occurrence data, which can be used to construct a range of policy-relevant biodiversity indicators. We describe the workflow in detail: from data acquisition, data assessment and data manipulation, through modelling, model evaluation, application and dissemination. At each stage, we draw on our experience developing and applying the workflow for almost a decade to outline the challenges that analysts might face. These challenges span many areas of ecology, taxonomy, data science, computing and statistics. In our case, the principal output of the workflow is annual estimates of occupancy, with measures of uncertainty, for over 5000 species in each of several defined 'regions' (e.g. countries, protected areas, etc.) of the UK from 1970 to 2019. This data product corresponds closely to the notion of a species distribution Essential Biodiversity Variable (EBV). Throughout the paper, we highlight methodologies that might not be applicable outside of the UK and suggest alternatives. We also highlight areas where the workflow can be improved; in particular, methods are needed to mitigate and communicate the risk of bias arising from the lack of representativeness that is typical of biodiversity data. Finally, we revisit the 'ideal' and 'minimal' criteria for species distribution EBVs laid out in previous contributions and pose some outstanding questions that should be addressed as a matter of priority. Going forward, we hope that this paper acts as a template for research groups around the world seeking to develop similar data products.

A secure future? Human urban and agricultural land use benefits a flightless island-endemic rail despite climate change

Identifying environmental characteristics that limit species' distributions is important for contemporary conservation and inferring responses to future environmental change. The Tasmanian native hen is an island endemic flightless rail and a survivor of a prehistoric extirpation event. Little is known about the regional-scale environmental characteristics influencing the distribution of native hens, or how their future distribution might be impacted by environmental shifts (e.g. climate change). Using a combination of local fieldwork and species distribution modelling, we assess environmental factors shaping the contemporary distribution of the native hen, and project future distribution changes under predicted climate change. We find 37% of Tasmania is currently suitable for the native hens, owing to low summer precipitation, low elevation, human-modified vegetation and urban areas. Moreover, in unsuitable regions, urban areas can create 'oases' of habitat, able to support populations with high breeding activity by providing resources and buffering against environmental constraints. Under climate change predictions, native hens were predicted to lose only 5% of their occupied range by 2055. We conclude that the species is resilient to climate change and benefits overall from anthropogenic landscape modifications. As such, this constitutes a rare example of a flightless rail to have adapted to human activity.

Investigating the potential of social media and citizen science data to track changes in species' distributions

How to best track species as they rapidly alter their distributions in response to climate change has become a key scientific priority. Information on species distributions is derived from biological records, which tend to be primarily sourced from traditional recording schemes, but increasingly also by citizen science initiatives and social media platforms, with biological recording having become more accessible to the general public. To date, however, our understanding of the respective potential of social media and citizen science to complement the information gathered by traditional recording schemes remains limited, particularly when it comes to tracking species on the move with climate change. To address this gap, we investigated how species occurrence observations vary between different sources and to what extent traditional, citizen science, and social media records are complementary, using the Banded Demoiselle (Calopteryx splendens) in Britain as a case study. Banded Demoiselle occurrences were extracted from citizen science initiatives (iRecord and iNaturalist) and social media platforms (Facebook, Flickr, and Twitter), and compared with traditional records primarily sourced from the British Dragonfly Society. Our results showed that species presence maps differ between record types, with 61% of the citizen science, 58% of the traditional, and 49% of the social media observations being unique to that data type. Banded Demoiselle habitat suitability maps differed most according to traditional and social media projections, with traditional and citizen science being the most consistent. We conclude that (i) social media records provide insights into the Banded Demoiselle distribution and habitat preference that are different from, and complementary to, the insights gathered from traditional recording schemes and citizen science initiatives; (ii) predicted habitat suitability maps that ignore information from social media records can substantially underestimate (by over 3500 km² in the case of the Banded Demoiselle) potential suitable habitat availability.

Climatic Niche Differentiation between the Invasive Hornet Vespa velutina nigrithorax and Two Native Hornets in Europe, Vespa crabro and Vespa orientalis

The introduction and expansion of the Asian yellow-legged hornet (Vespa velutina nigrithorax) in Europe poses concern for multiple reasons, including biodiversity conservation. In addition to the predation of native insects (e.g., bees and wasps), this species may compete with native hornets due to an overlap of their climatic and trophic niches. The aim of this study is to investigate the realised climatic niche of V. v. nigrithorax and its response to climatic conditions and to evaluate the degree of overlap with the niches of the two native Vespa species present in Europe, Vespa crabro and Vespa orientalis. The niches of both native species partially overlap with the niche of the invasive species (Schoener’s D, 0.43 for V. crabro and 0.28 for V. orientalis), although some differences can be detected. V. crabro appears to be more adapted to cold and dry conditions than the invasive species, and V. orientalis is more adapted to arid climates. These differences may provide a competitive advantage to both native species in areas with a lower environmental suitability for V. v. nigrithorax, in the probable event that this species continues to spread, reaching all areas predicted to be suitable in Europe and in the Mediterranean basin.

Applying landscape metrics to species distribution model predictions to characterize internal range structure and associated changes

Distributional shifts in species ranges provide critical evidence of ecological responses to climate change. Assessments of climate-driven changes typically focus on broad-scale range shifts (e.g. poleward or upward), with ecological consequences at regional and local scales commonly overlooked. While these changes are informative for species presenting continuous geographic ranges, many species have discontinuous distributions-both natural (e.g. mountain or coastal species) or human-induced (e.g. species inhabiting fragmented landscapes)-where within-range changes can be significant. Here, we use an ecosystem engineer species (Sabellaria alveolata) with a naturally fragmented distribution as a case study to assess climate-driven changes in within-range occupancy across its entire global distribution. To this end, we applied landscape ecology metrics to outputs from species distribution modelling (SDM) in a novel unified framework. SDM predicted a 27.5% overall increase in the area of potentially suitable habitat under RCP 4.5 by 2050, which taken in isolation would have led to the classification of the species as a climate change winner. SDM further revealed that the latitudinal range is predicted to shrink because of decreased habitat suitability in the equatorward part of the range, not compensated by a poleward expansion. The use of landscape ecology metrics provided additional insights by identifying regions that are predicted to become increasingly fragmented in the future, potentially increasing extirpation risk by jeopardising metapopulation dynamics. This increased range fragmentation could have dramatic consequences for ecosystem structure and functioning. Importantly, the proposed framework-which brings together SDM and landscape metrics-can be widely used to study currently overlooked climate-driven changes in species internal range structure, without requiring detailed empirical knowledge of the modelled species. This approach represents an important advancement beyond predictive envelope approaches and could reveal itself as paramount for managers whose spatial scale of action usually ranges from local to regional.

On the road: Anthropogenic factors drive the invasion risk of a wild solitary bee species

Complex biotic networks of invaders and their new environments pose immense challenges for researchers aiming to predict current and future occupancy of introduced species. This might be especially true for invasive bees, as they enter novel trophic interactions. Little attention has been paid to solitary, invasive wild bees, despite their increasing recognition as a potential global threat to biodiversity. Here, we present the first comprehensive species distribution modelling approach targeting the invasive bee Megachile sculpturalis, which is currently undergoing parallel range expansion in North America and Europe. While the species has largely colonised the most highly suitable areas of North America over the past decades, its invasion of Europe seems to be in its early stages. We showed that its current distribution is largely explained by anthropogenic factors, suggesting that its spread is facilitated by road and maritime traffic, largely beyond its intrinsic dispersal ability. Our results suggest that M. sculpturalis is likely to be negatively affected by future climate change in North America, while in Europe the potential suitable areas at-risk of invasion remain equally large. Based on our study, we emphasise the role of expert knowledge for evaluation of ecologically meaningful variables implemented and interpreted for species distribution modelling. We strongly recommend that the monitoring of this and other invasive pollinator species should be prioritised in areas identified as at-risk, alongside development of effective management strategies.

Unravelling the Stability of Nightingale Song Over Time and Space Using Open, Citizen Science and Shared Data

Open science approaches enable and facilitate the investigation of many scientific questions in bioacoustics, such as studies on the temporal and spatial evolution of song, as in vocal dialects. In contrast to previous dialect studies, which mostly focused on songbird species with a small repertoire, here we studied the common nightingale (Luscinia megarhynchos), a bird species with a complex and large repertoire. To study dialects on the population level in this species, we used recordings from four datasets: an open museum archive, a citizen science platform, a citizen science project, and shared recordings from academic researchers. We conducted to the date largest temporal and geographic dialect study of birdsong including recordings from 1930 to 2019 and from 13 European countries, with a geographical coverage of 2,652 km of linear distance. To examine temporal stability and spatial dialects, a catalog of 1,868 song types of common nightingales was created. Instead of dialects, we found a high degree of stability over time and space in both, the sub-categories of song and in the occurrence of song types. For example, the second most common song type in our datasets occurred over nine decades and across Europe. In our case study, open and citizen science data proved to be equivalent, and in some cases even better, than data shared by an academic research group. Based on our results, we conclude that the combination of diverse and open datasets was particularly useful to study the evolution of song in a bird species with a large repertoire.

Data integration methods to account for spatial niche truncation effects in regional projections of species distribution

Many species distribution models (SDMs) are built with precise but geographically restricted presence-absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen-science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species' niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger-scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger-scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.