DSpace Community:http://hdl.handle.net/10174/382024-03-29T06:06:47Z2024-03-29T06:06:47ZENM2020: A Free Online Course and Set of Resources on Modeling Species' Niches and DistributionsPeterson, TownsenNaimi, Babakhttp://hdl.handle.net/10174/357122023-11-22T11:11:36Z2022-03-01T00:00:00ZTitle: ENM2020: A Free Online Course and Set of Resources on Modeling Species' Niches and Distributions
Authors: Peterson, Townsen; Naimi, Babak
Abstract: The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades—including a maturation of relevant theory and key concepts—but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an “Overview” talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.2022-03-01T00:00:00ZA data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss AlpsAmini Tehrani, NasrinNaimi, BabakJaboyedoff, Michelhttp://hdl.handle.net/10174/357112023-11-22T11:10:33Z2022-06-30T23:00:00ZTitle: A data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss Alps
Authors: Amini Tehrani, Nasrin; Naimi, Babak; Jaboyedoff, Michel
Abstract: 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.2022-06-30T23:00:00ZUnderstanding the summer roosting habitat selection of the greater mouse-tailed bat (Rhinopoma microphyllum) and the small mouse-tailed bat (Rhinopoma muscatellum) in IranAskaripour, NarimanAshrafi, SohrabRoshan Ara, SaharNaimi, Babakhttp://hdl.handle.net/10174/357072023-11-22T11:07:20Z2022-09-30T23:00:00ZTitle: Understanding the summer roosting habitat selection of the greater mouse-tailed bat (Rhinopoma microphyllum) and the small mouse-tailed bat (Rhinopoma muscatellum) in Iran
Authors: Askaripour, Nariman; Ashrafi, Sohrab; Roshan Ara, Sahar; Naimi, Babak
Abstract: Roost for bats, which are responsible for a wide range of vital ecological and economic services, is crucial. Their availability affects both the geographic occurrence and the diversity of bat communities. Hence, understanding how bats use roosts and variables that influence these patterns could contribute to the development of management plans to ensure their survival. In this study, species distribution modeling of two bat species, the greater mouse-tailed bat (Rhinopoma microphyllum) and the small mouse-tailed bat (Rhinopoma muscatellum), were carried out using the sdm package in R. To do so, 16 environmental variables were used as the predictors to explore their relationships with the occurrence of the two species using 12 modeling algorithms. The prediction models for each species were then combined into an ensemble model. The random forest modeling algorithm showed better performance than the other individual models in this modeling. Moreover, the prediction performance of the ensemble model was more substantial than all the individual models for both species. For the greater mouse-tailed bat, elevation, annual mean temperature, temperature seasonality, and distance to roads-railways were identified as the essential variables for summer roosting habitat selection. Meanwhile, distance to roads-railways, annual mean temperature, elevation, and distance to the ridge were significant for the small mouse-tailed bat. Since this study facilitates the management of future and suitable habitats by identifying important environmental conditions, it can be used in conservation plans.2022-09-30T23:00:00ZRevisiting the minimum set cover, the maximal coverage problems and a maximum benefit area selection problem to make climate‐change‐concerned conservation plans effectiveAlagador, DiogoCerdeira, Jorge O.http://hdl.handle.net/10174/280212020-10-07T08:02:50Z2020-07-17T23:00:00ZTitle: Revisiting the minimum set cover, the maximal coverage problems and a maximum benefit area selection problem to make climate‐change‐concerned conservation plans effective
Authors: Alagador, Diogo; Cerdeira, Jorge O.
Abstract: 1. Informed decisions for the selection of protected areas (PAs) are grounded in two general problems in Operations Research: the minimum set covering problem (minCost), where a set of ecological constraints are established as conservation targets and the minimum cost PAs are found, and the maximal coverage problem (maxCoverage) where the constraint is uniquely economic (i.e., a fixed budget) and the goal is to maximize the number of species having conservation targets adequately covered.
2. We adjust minCost and maxCoverage to accommodate the dynamic effects of climate change on species’ ranges. The selection of sites is replaced by the selection of time-ordered sequences of sites (climate change corridors), and an estimate of the persistence of each species in corridors is calculated according to the expected suitability of each site in the respective time period and the capacity of species to disperse between consecutive sites along corridors. In these problems, conservation targets are expressed as desired (and attainable) species persistence levels. We also introduce a novel problem (minShortfall) that combines minCost and maxCoverage. Unlike these two problems, minShortfall allows persistence targets to be missed and minimizes the sum of those gaps (i.e., target shortfalls), subject to a limited budget.
3. We illustrate the three problems with a case study using climatic suitability estimates for ten mammal species in the Iberian Peninsula under a climate change scenario until 2080. We compare solutions of the three problems with respect to species persistence and PA costs, under distinct settings of persistence targets, number of target-fulfilled species, and budgets. The solutions from different problems differed with regard to the areas to prioritize, their timings and the species whose persistence targets were fulfilled. This analysis also allowed identifying groups of species sharing corridors in optimal solutions, thus allowing important financial savings in site protection.
4. We suggest that enhancing species persistence is an adequate approach to cope with habitat shifts due to climate change. We trust the three problems discussed can provide complementary and valuable support for planners to anticipate decisions in order that the negative effects of climate change on species’ persistence are minimized.2020-07-17T23:00:00Z