Assessing the usefulness of citizen science data for habitat suitability modelling: opportunistic reporting versus sampling based on a systematic protocol

Aim: To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high‐quality absences from OR based on questionnaires sent to observers. Location: Both datasets cover the same large area (Sweden) and time period (2000–2013). Methods: Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data was assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR. Results: For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species–environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found. Main conclusions: Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist

Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability

Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains

The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales.Peer reviewe

Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains

The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales

Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains

The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales

The future distribution of wetland birds breeding in Europe validated against observed changes in distribution

Wetland bird species have been declining in population size worldwide as climate warming and land-use change affect their suitable habitats. We used species distribution models (SDMs) to predict changes in range dynamics for 64 non-passerine wetland birds breeding in Europe, including range size, position of centroid, and margins. We fitted the SDMs with data collected for the first European Breeding Bird Atlas and climate and land-use data to predict distributional changes over a century (the 1970s-2070s). The predicted annual changes were then compared to observed annual changes in range size and range centroid over a time period of 30 years using data from the second European Breeding Bird Atlas. Our models successfully predicted ca. 75% of the 64 bird species to contract their breeding range in the future, while the remaining species (mostly southerly breeding species) were predicted to expand their breeding ranges northward. The northern margins of southerly species and southern margins of northerly species, both, predicted to shift northward. Predicted changes in range size and shifts in range centroids were broadly positively associated with the observed changes, although some species deviated markedly from the predictions. The predicted average shift in core distributions was ca. 5 km yr(-1) towards the north (5% northeast, 45% north, and 40% northwest), compared to a slower observed average shift of ca. 3.9 km yr(-1). Predicted changes in range centroids were generally larger than observed changes, which suggests that bird distribution changes may lag behind environmental changes leading to 'climate debt'. We suggest that predictions of SDMs should be viewed as qualitative rather than quantitative outcomes, indicating that care should be taken concerning single species. Still, our results highlight the urgent need for management actions such as wetland creation and restoration to improve wetland birds' resilience to the expected environmental changes in the future

The future distribution of wetland birds breeding in Europe validated against observed changes in distribution

Publisher Copyright: © 2022 The Author(s). Published by IOP Publishing Ltd.Wetland bird species have been declining in population size worldwide as climate warming and land-use change affect their suitable habitats. We used species distribution models (SDMs) to predict changes in range dynamics for 64 non-passerine wetland birds breeding in Europe, including range size, position of centroid, and margins. We fitted the SDMs with data collected for the first European Breeding Bird Atlas and climate and land-use data to predict distributional changes over a century (the 1970s-2070s). The predicted annual changes were then compared to observed annual changes in range size and range centroid over a time period of 30 years using data from the second European Breeding Bird Atlas. Our models successfully predicted ca. 75% of the 64 bird species to contract their breeding range in the future, while the remaining species (mostly southerly breeding species) were predicted to expand their breeding ranges northward. The northern margins of southerly species and southern margins of northerly species, both, predicted to shift northward. Predicted changes in range size and shifts in range centroids were broadly positively associated with the observed changes, although some species deviated markedly from the predictions. The predicted average shift in core distributions was ca. 5 km yr-1 towards the north (5% northeast, 45% north, and 40% northwest), compared to a slower observed average shift of ca. 3.9 km yr-1. Predicted changes in range centroids were generally larger than observed changes, which suggests that bird distribution changes may lag behind environmental changes leading to 'climate debt'. We suggest that predictions of SDMs should be viewed as qualitative rather than quantitative outcomes, indicating that care should be taken concerning single species. Still, our results highlight the urgent need for management actions such as wetland creation and restoration to improve wetland birds' resilience to the expected environmental changes in the future.Peer reviewe

The distribution of upland breeding waders at multiple spatial scales

Species distribution models are a valuable tool in conservation and research. This thesis addressed three common constraints of such models: the lack of detailed vegetation data for large areas. the inclusion of variables at arbitrary spatial scales which can lead to wrong conclusions as processes are scale dependent. 3) the potentially large number of variables and associated problems in variable selection when models include the landscape context. A novel technique combining a multiple scale species distribution model with image interpretation classified 24 detailed vegetation communities and an additional class for trees and bushes of the Yorkshire Dales at a high resolution (5 m) with overall high accuracies (87 - 92%). A novel selection procedure was developed capable of selecting important variables at appropriate spatial scales from a large number of variables. Models were presented on the example of curlew (Numenius arquata) and lapwing (Vanellus vanellus) in the Yorkshire Dales. Predictive ability of most resulting • models was moderate to high (AUC = 0.84 - 0.97). Lapwing presence was positively associated with gentle slopes and negatively with soil of low fertility at a large scale (10 km) while locally (250 - 500 m) lapwing preferred soil with impeded drainage and acid grassland. Curlew presence was negatively associated with westerly facing slope and curlew density positively with south facing slope (above a threshold of 14%) at large scales (9 - 10 km). Curlew presence was negatively associated with freely draining soil and settlements and positively with bogs at small scales (250 - 750 m). Near paths, density of curlew was higher if less of the path was in view at a small scale (250 m). Hatching success of curlew was strongly negatively related with settlements within 1 km. At nests, curlew preferred vegetation of intermediate height (ea. 40 cm).EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Forecast the response of forest birds to climate change and forest management: does citizen science data provide accurate predictions?

In the context of global changes and biodiversity mass extinction, species distribution models (SDMs) are of major importance for conservation and management. In particular, such models can be used for mapping spatial distribution of endangered species and forecasting their response to climate and land-use change. In Sweden, intensive forestry has caused a strong decline of forest biodiversity. Climate change is also expected to cause range contractions for northern-boreal species which are at the limit of their range boundaries. As SDMs require a large amount of data, ideally collected over large spatial and temporal scales, citizen science based on volunteer reporting of species can constitute a promising alternative. However, citizen science data (CSD) also have several drawbacks (e.g. presence only, uneven sampling effort). Furthermore, CSD still lack validation by comparison to systematically collected data. The aim of our study is to assess the reliability of CSD for forecasting species occurrence in response to various management and climate scenarios. We compare the predictions obtained by two different and independent citizen science datasets, opportunistic reports (OR) from Artportalen, and systematically collected data (SC) from Svensk Fågeltaxering. Both datasets cover the same large spatial area (whole of Sweden) and time period (2000-2013). As the latter have a well-defined sampling design and protocol and engage experienced observers, we used this dataset as reference to assess the accuracy of models based on OR. Absence data have been inferred from OR based on questionnaires sent to observers evaluating their skills and reporting habits. We then built species distribution models (logistic regression) according to climate and environmental predictors (allowing non-linear effects and interactions). We further used these models to forecast the responses of multiple species to various climate scenarios (based on IPCC projections) and forest management scenarios (simulations in Heureka Forestry Decision Support System). The assessment focused on eight forest bird species that encompass a diversity of functional traits and ecological preferences. This allowed us to test how the models performances vary between species. We also expected different species responses to climate and management scenario with potential trade-offs to conciliate the different species requirements. More generally, our study provide further evidences for the relevance of using CSD to forecast species distribution. In particular, our results highlight that OR and SC have different strengths and limitations. Opportunistic reports seems provide more accurate predictions for rare or cryptic species, whereas SC perform better in sparsely populated areas.peerReviewe

Inferred Three-toed woodpecker absences

Inferred absences for three-toed woodpecker. MatchYear 2000: Observations in years 2000-2002; MatchYear 2005: Observations in years 2003-2007; MatchYear 2010: Observations in years 2008-2013. Spatial reference for x and y coordinates: EPSG 3021