ORCID Profile
0000-0002-9432-895X
Current Organisation
Tohoku University
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Publisher: The University of Kansas
Date: 06-03-2022
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 participants globally through ,000 hours of viewing and ,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.
Publisher: Cold Spring Harbor Laboratory
Date: 11-08-2022
DOI: 10.1101/2022.08.08.503252
Abstract: Randomization tests are often used with species niche and distribution models to estimate model performance, test hypotheses, and measure methodological biases. Many of these tests involve building null models representing the hypothesis that there is no association between the species’ occurrences and the environmental predictors, then comparing the empirical model to null distributions built from these models. These null models are commonly based on points randomly selected with a uniform probability from the study area. However, spatial s ling bias, a near-universal feature of the occurrence data used to build niche and distribution models, results in a non-uniform probability of observing species in different areas even when species occurrences are unrelated to environmental predictors. Failing to account for this bias in randomization tests results in null distributions that do not accurately represent the null hypothesis, potentially leading to incorrect conclusions. In this study, we use simulations to demonstrate that uniform s ling in randomization tests can lead to unacceptable rates of type I error and poor estimates of methodological bias when spatial s ling bias is present in the occurrence data. We present a new method that incorporates a bias estimate into replicate simulations for these randomization tests, and show that this adjustment can reduce type I error rates to an acceptable level.
Publisher: California Digital Library (CDL)
Date: 09-2022
Publisher: Oxford University Press (OUP)
Date: 03-04-2018
Publisher: American Association for the Advancement of Science (AAAS)
Date: 05-08-2022
Abstract: Invertebrates constitute the majority of animal species and are critical for ecosystem functioning and services. Nonetheless, global invertebrate bio ersity patterns and their congruences with vertebrates remain largely unknown. We resolve the first high-resolution (~20-km) global ersity map for a major invertebrate clade, ants, using bio ersity informatics, range modeling, and machine learning to synthesize existing knowledge and predict the distribution of undiscovered ersity. We find that ants and different vertebrate groups have distinct features in their patterns of richness and rarity, underscoring the need to consider a ersity of taxa in conservation. However, despite their phylogenetic and physiological ergence, ant distributions are not highly anomalous relative to variation among vertebrate clades. Furthermore, our models predict that rarity centers largely overlap (78%), suggesting that general forces shape endemism patterns across taxa. This raises confidence that conservation of areas important for small-ranged vertebrates will benefit invertebrates while providing a “treasure map” to guide future discovery.
Publisher: Wiley
Date: 16-06-2022
DOI: 10.1111/ECOG.06295
Abstract: Tropical ecosystems are often bio ersity hotspots, and invertebrates represent the main underrepresented component of ersity in large‐scale analyses. This problem is partly related to the scarcity of data widely available to conduct these studies and the lack of systematic organization of knowledge about invertebrates' distributions in bio ersity hotspots. Here, we introduce and analyze a comprehensive data compilation of Amazonian ant ersity. Using records from 1817 to 2020 from both published and unpublished sources, we describe the ersity and distribution of ant species in the Brazilian Amazon Basin. Further, using high‐definition images and data from taxonomic publications, we build a comprehensive database of morphological traits for the ant species that occur in the region. In total, we recorded 1067 nominal species in the Brazilian Amazon Basin, with s ling locations strongly biased by access routes, urban centers, research institutions and major infrastructure projects. Large areas where ant s ling is non‐existent represent about 52% of the basin and are concentrated mainly in the northern, southeastern and western Brazilian Amazon. We found that distance to roads is the main driver of ant s ling in the Amazon. Contrary to our expectations, morphological traits had lower predictive power in predicting s ling bias than purely geographic variables. However, when geographic predictors were controlled, habitat stratum and traits contribute to explain the remaining variance. More species were recorded in better‐s led areas, but species richness estimation models suggest that areas in southern Amazonian edge forests are associated with especially high species richness. Our results represent the first trait‐based, large‐scale study for insects in Amazonian forests and a starting point for macroecological studies focusing on insect ersity in the Amazon Basin.
Location: No location found
Location: Japan
No related grants have been discovered for Jamie Michael Kass.