A data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss Alps

It is essential to accurately model species distributions and biodiversity in response to many ecological and conservation challenges. The primary means of reliable decision-making on conservation priority are the data on the distributions and abundance of species. However, finding data that is accurate and reliable for predicting species distribution could be challenging. Data could come from different sources, with different designs, coverage, and potential sampling biases. In this study, we examined the emerging methods of modelling species distribution that integrate data from multiple sources such as systematic or standardized and casual or occasional surveys. We applied two modelling approaches, “data-pooling” and “ model-based data integration” that each involves combining various datasets to measure environmental interactions and clarify the distribution of species. Our paper demonstrates a reliable data integration workflow that includes gathering information on model-based data integration, creating a sub-model of each dataset independently, and finally, combining it into a single final model. We have shown that this is a more reliable way of developing a model than a data pooling strategy that combines multiple data sources to fit a single model. Moreover, data integration approaches could improve the poor predictive performance of systematic small datasets, through model-based data integration techniques that enhance the predictive accuracy of Species Distribution Models. We also identified, consistent with previous research, that machine learning algorithms are the most accurate techniques to predict bird species distribution in our heterogeneous study area in the western Swiss Alps. In particular, tree-dependent ensembles of Random Forest (RF) contribute to a better understanding of the interactions between species and the environment

Toward community predictions: Multi‐scale modelling of mountain breeding birds' habitat suitability, landscape preferences, and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence‐only points) of bird species to find suitable modeling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi‐scale method of modeling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build an ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including “Bio11” (Mean Temperature of Coldest Quarter), and “Bio 4” (Temperature Seasonality), then in the focal variables including “Forest”, “Orchard”, and “Agriculture area” as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment

Toward community predictions : Multi-scale modelling of mountain breeding birds' habitat suitability, landscape preferences, and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence-only points) of bird species to find suitable modelling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi-scale method of modelling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including "Bio11" (Mean Temperature of Coldest Quarter), and "Bio 4" (Temperature Seasonality), then in the focal variables including "Forest", "Orchard", and "Agriculture area" as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment.Peer reviewe

Toward a set of essential biodiversity variables for assessing change in mountains globally

Mountain regions harbor unique and rich biodiversity, forming an important part of our global life support system. This rich biodiversity underpins the ecological intactness and functioning of mountain ecosystems, which are imperative for the provision of key ecosystem services. A considerable amount of data are required to assess ecological intactness and ecosystem functioning and, given the profound anthropogenic pressures many mountain regions are being subjected to, are urgently needed. However, data on mountain biodiversity remain lacking. The essential biodiversity variables (EBVs) framework can help focus efforts related to detecting, investigating, predicting, and managing global biodiversity change, but has not yet been considered in the context of mountains. Here, we review key biological processes and physical phenomena that strongly influence mountain biodiversity and ecosystems and elucidate their associations with potential mountain EBVs. We identify seven EBVs of highest relevance for tracking and understanding the most critical drivers and responses of mountain biodiversity change. If they are implemented, the selected EBVs will contribute useful information to inform management and policy interventions seeking to halt mountain biodiversity loss and maintain functional mountain ecosystems

ECOLOGICAL APPROACHES TO DEVELOP AND IMPROVE BIRD SPECIES DISTRIBUTION MODELS IN IHE WESTERN SWISS AIPS

It is universally accepted that the world has been losing biodiversity due to increasing pressure from anthropogenic activit ies . Vulnerable species populations are being more decreased leading to global biodiversity loss and, consequently, the extinction of species. Therefore, this needs an urgent consideration of the factors that affect the distribution of species and an understanding of how environmental change factors impact the distribution of species. Species Distribution Model (SDMs) could be used as the key tool for predicting habitat suitability for species and then applied to decision-making purposes and suggested and supported conservation decision-making Various factors (e.g., sample size, modelling technique, environmental variable) and errors/biases (i.e., false presences/absences) have been found to influence the predictive performance of th species and the SOM assembly (i.e., S-SDMs). Therefore, it is essential to consider how such factor that affect species distribution prediction could be improved. In the second chapter of my thesis, developed a multi-scale modeling approach that uses focal variables on different scales to evaluat the effectiveness of each predictor, and then we contained the final collection of variables to creat a set of ensemble small models (ESMs) and generate maps for species distribution as conservation tool. The findings of our analysis show that the most important variables were in th group of bioclimatic variables including "Biol1" (Mean Temperature of Coldest Quarter), and "Bi 4" (Temperature Seasonality), and the ESMs could be a strong and comprehensive method t better understand the ecosystem in a highly heterogeneous environment. In the third chapter, illustrate analysis to examine how bird SDMs can be developed and used to extract regionally bird spatial EBVs. I demonstrated that the suitability calculated by SDMs could be used as a spatia 'species distribution' EBV (SD EBV) and could illustrate the quality of the habitat and the impac of climate and land-use trends on bird populations and facilitate the monitoring and conservatio of birds over time and space. Finally, in the fourth chapter, I studied and analysed two differen approaches (data pooling and model-based data integration) to the incorporation of various bir data sets in order to determine the best bird data set for species distribution prediction in th Western Swiss Alps. My study has shown that gathering data on model-based data integratio could be more accurate than a data pooling approach. -- Il est universellement admis que le monde perd de la biodiversité en raison de la pression croissante des activités anthropiques. Les populations d'espèces vulnérables sont de plus en plus réduites, ce qui entraîne une perte de biodiversité mondiale et, par conséquent, l'extinction d'espèces. Il est donc urgen d'examiner les facteurs qui affectent la répartition des espèces et de comprendre comment les facteurs de changement environnemental influent sur la répartition des espèces. Les modèles de distribution des espèces (SDMs) pourraient être utilisés comme outil clé pour prévoir l'adéquation des habitats aux espèces, puis appliqués à des fins de prise de décision et suggérer et soutenir la prise de décision en matière de conservation. Divers facteurs (par exemple, la taille de l'échantillon, la technique de modélisation, la variable environnementale) et les erreurs/biais (c'est-à-dire les fausses présences/absences) ont été constatés pour influencer la performance prédictive des espèces et de l'ensemble des SOM (c'est-à-dire les S-SDMs). Il est donc essentiel d'examiner comment ces facteurs qu affectent la prévision de la distribution des espèces pourraient être améliorés. Dans le premier chapitre de ma thèse, j'ai développé une approche de modélisation multi-échelle qui utilise des variables focale à différentes échelles pour évaluer l'efficacité de chaque prédicteur, puis nous avons contenu la collection finale de variables pour créer un ensemble de petits modèles d'ensemble (ESMs) et génére des cartes de distribution des espèces comme outil de conservation. Les résultats de notre analyse montrent que les variables les plus importantes se trouvaient dans le groupe des variable bioclimatiques comprenant "Bioll" (température moyenne du trimestre le plus froid), et "Bio 4 (température saisonnière), et les ESMs pourraient être une méthode solide et complète pour mieux comprendre l'écosystème dans un environnement très hétérogène. Dans le deuxième chapitre, j'illustre l'analyse pour examiner comment les SOM des oiseaux peuvent être développées et utilisées pou extraire les EBV spatiales des oiseaux au niveau régional. J'ai démontré que l'adéquation calculée pa les SDMs pouvait être utilisée comme une de "distribution spatiale des espèces" EBV (SD EBV) e pouvait illustrer la qualité de l'habitat et l'impact du climat et des tendances d'utilisation des terres su les populations d'oiseaux et faciliter le suivi et la conservation des oiseaux dans le temps et l'espace Enfin, dans le quatrième chapitre, j'ai étudié et analysé deux approches différentes (mise en commun des données et intégration des données basées sur des modèles) pour l'incorporation de diver ensembles de données sur les oiseaux afin de déterminer le meilleur ensemble de données sur le oiseaux pour la prévision de la distribution des espèces dans les Alpes de Suisse occidentale. Mo analyse a montré que la collecte de données sur l'intégration de données basée sur un modèle pourrai être plus précise qu'une approche de mise en commun de données

Modeling current and future species distribution of breeding birds as regional essential biodiversity variables (SD EBVs) : A bird perspective in Swiss Alps

Changes in distribution and abundance of species affect the entirety of biodiversity and monitoring these changes is critical for the efficient conservation of integrity and functions of species population. However, acquiring accurate information on biodiversity over large spatial scales poses a challenge since such data is patchy and incomplete, if not unavailable, in many areas. This study aims at examining the applicability of a novel approach based on Species Distribution Models (SDMs) to develop spatial predictions of Essential Biodiversity variables (EBVs; variables to be quantified at certain points in time and space to monitor variations in biodiversity) for birds based on bird diversity metrics such as the distributions of properties of key bird habitats. A major objective of this study is to build bird SDMs which can be used to derive spatial EBVs for bird species at a regional scale. We used as predictors 16 environmental variables that are known to be ecologically meaningful for birds, including two bioclimatic variables (Bio17 = precipitation of driest quarter and Bio7 = temperature annual range) for three periods of 'current', 'future 2050', and 'future 2070', eleven landcover (land use) predictors, the normalized difference vegetation index, and two topographic variables (slope and topography). We used multiple modeling techniques to build presenceonly SDMs relating bird presence to environmental features of each species. Here, we show that the suitability estimated according to the SDMs can be used as a spatial 'species dis-tribution' EBV (SD EBV) and reflect the habitat quality and trends in land use and climatic impacts on populations of bird species. These developments could facilitate monitoring of bird species across space and time, ultimately helping to identify priority conservation areas, estimate habitat suitability and provide early warning signs regarding bird distribution trends. In general, bioclimatic variables, topography and forest structure were identified to have important ties to the species probability maps generated on the basis of the SDMs, signifying a dominant role of bioclimatic variable Bio17 in the development of habitat suitability patterns. (C) 2021 The Author(s). Published by Elsevier B.V.Peer reviewe

A data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss Alps

It is essential to accurately model species distributions and biodiversity in response to many ecological and conservation challenges. The primary means of reliable decision-making on conservation priority are the data on the distributions and abundance of species. However, finding data that is accurate and reliable for predicting species distribution could be challenging. Data could come from different sources, with different designs, coverage, and potential sampling biases. In this study, we examined the emerging methods of modelling species distribution that integrate data from multiple sources such as systematic or standardized and casual or occasional surveys. We applied two modelling approaches, “data-pooling” and “ model-based data integration” that each involves combining various datasets to measure environmental interactions and clarify the distribution of species. Our paper demonstrates a reliable data integration workflow that includes gathering information on model-based data integration, creating a sub-model of each dataset independently, and finally, combining it into a single final model. We have shown that this is a more reliable way of developing a model than a data pooling strategy that combines multiple data sources to fit a single model. Moreover, data integration approaches could improve the poor predictive performance of systematic small datasets, through model-based data integration techniques that enhance the predictive accuracy of Species Distribution Models. We also identified, consistent with previous research, that machine learning algorithms are the most accurate techniques to predict bird species distribution in our heterogeneous study area in the western Swiss Alps. In particular, tree-dependent ensembles of Random Forest (RF) contribute to a better understanding of the interactions between species and the environment.Peer reviewe