http://hdl.handle.net/10174/38 2026-05-18T17:53:10Z http://hdl.handle.net/10174/41945 Title: Entomologia tanatológica – insectos e vítimas do terramoto de 1755 inumadas no claustro do convento de Jesus (Academia das Ciências de Lisboa) e seu significado. Authors: Raimundo, Rui; Figueiredo, Diogo Abstract: In this work we try to determine if the remains of insects collected from the corpses buried beneath the cloister of Academy of Sciences in Lisbon, during the earthquake of 1755, could give some information about the cadavers. And if possible determine their post mortem interval (IPM). For this we identified the remains of the insects, recurring to their external morphological characters. Using procedures of forensic entomology we assume that the bodies were death before they were buried, a period of time never inferior to 10 days. 2006-01-01T00:00:00Z http://hdl.handle.net/10174/35712 Title: 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. 2022-03-01T00:00:00Z http://hdl.handle.net/10174/35711 Title: 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. 2022-06-30T23:00:00Z http://hdl.handle.net/10174/35707 Title: 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. 2022-09-30T23:00:00Z