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  1. Article: The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type.

    Prajzlerová, Dominika / Barták, Vojtěch / Keil, Petr / Moudrý, Vítězslav / Zikmundová, Markéta / Balej, Petr / Leroy, François / Rocchini, Duccio / Perrone, Michela / Malavasi, Marco / Šímová, Petra

    International journal of applied earth observation and geoinformation : ITC journal

    2024  Volume 128, Page(s) 103763

    Abstract: To identify areas of high biodiversity and prioritize conservation efforts, it is crucial to understand the drivers of species richness patterns and their scale dependence. While classified land cover products are commonly used to explain bird species ... ...

    Abstract To identify areas of high biodiversity and prioritize conservation efforts, it is crucial to understand the drivers of species richness patterns and their scale dependence. While classified land cover products are commonly used to explain bird species richness, recent studies suggest that unclassified remote-sensed images can provide equally good or better results. In our study, we aimed to investigate whether unclassified multispectral data from Landsat 8 can replace image classification for bird diversity modeling. Moreover, we also tested the Spectral Variability Hypothesis. Using the Atlas of Breeding Birds in the Czech Republic 2014-2017, we modeled species richness at two spatial resolutions of approx. 131 km
    Language English
    Publishing date 2024-03-28
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1569-8432
    ISSN 1569-8432
    DOI 10.1016/j.jag.2024.103763
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Species distribution models affected by positional uncertainty in species occurrences can still be ecologically interpretable

    Gábor, Lukáš / Jetz, Walter / Zarzo‐Arias, Alejandra / Winner, Kevin / Yanco, Scott / Pinkert, Stefan / Marsh, Charles J. / Rogan, Matthew S. / Mäkinen, Jussi / Rocchini, Duccio / Barták, Vojtěch / Malavasi, Marco / Balej, Petr / Moudrý, Vítězslav

    Ecography. 2023 June, v. 2023, no. 6 p.e06358-

    2023  

    Abstract: Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty of underlying species occurrence data. Previous work has documented the effect of ... ...

    Abstract Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty of underlying species occurrence data. Previous work has documented the effect of positional uncertainty on model predictive performance, but its consequences for inference about species–environment relationships remain largely unknown. Here we use over 12 000 combinations of virtual and real environmental variables and virtual species, as well as a real case study, to investigate how accurately SDMs can recover species–environment relationships after applying known positional errors to species occurrence data. We explored a range of environmental predictors with various spatial heterogeneity, species' niche widths, sample sizes and magnitudes of positional error. Positional uncertainty decreased predictive model performance for all modeled scenarios. The absolute and relative importance of environmental predictors and the shape of species–environmental relationships co‐varied with a level of positional uncertainty. These differences were much weaker than those observed for overall model performance, especially for homogenous predictor variables. This suggests that, at least for the example species and conditions analyzed, the negative consequences of positional uncertainty on model performance did not extend as strongly to the ecological interpretability of the models. Although the findings are encouraging for practitioners using SDMs to reveal generative mechanisms based on spatially uncertain data, they suggest greater consequences for applications utilizing distributions predicted from SDMs using positionally uncertain data, such as conservation prioritization and biodiversity monitoring.
    Keywords biodiversity ; case studies ; geographical distribution ; model validation ; prioritization ; spatial variation ; uncertainty
    Language English
    Dates of publication 2023-06
    Publishing place Blackwell Publishing Ltd
    Document type Article ; Online
    Note JOURNAL ARTICLE
    ZDB-ID 1112659-0
    ISSN 0906-7590
    ISSN 0906-7590
    DOI 10.1111/ecog.06358
    Database NAL-Catalogue (AGRICOLA)

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