Vegetation canopy height shapes bats’ occupancy: a remote sensing approach

dc.contributor.authorMartins, F.C.
dc.contributor.authorGodinho, S.
dc.contributor.authorGuiomar, N.
dc.contributor.authorMedinas, D.
dc.contributor.authorRebelo, H.
dc.contributor.authorSegurado, P.
dc.contributor.authorMarques, J.T.
dc.date.accessioned2024-12-19T16:51:01Z
dc.date.available2024-12-19T16:51:01Z
dc.date.issued2024-07-17
dc.description.abstractAnthropogenic activities have significantly altered land cover on a global scale. These changes often have a negative effect on biodiversity limiting the distribution of species. The extent of the effect on species’ distribution depends on the landscape composition and configuration at a local and landscape level. To better understand this effect on a large scale, we evaluated how land cover and vegetation structure shape bat species’ occurrence while considering species’ imperfect detection. We hypothesize that intensification of anthropogenic activities in agriculture, for example, reduces heterogeneity of land cover and vegetation structure, and thereby, limits bat occurrence. To investigate this, we conducted acoustic bat sampling across 59 locations in southern Portugal, each with three spatial replicates. We derived fine-scale vegetation structural metrics by combining spaceborne LiDAR (GEDI) and synthetic aperture radar data (Sentinel-1 and ALOS/PALSAR-2). Additionally, we included land cover metrics and high-resolution climate data from CHELSA. Our findings revealed an important relationship between bat species’ occupancy and vegetation structure, particularly with vegetation canopy height. Moreover, forest and shrubland proportions were the main land cover types influencing bat species responses. All species’ best-ranking occupancy models included at least one climatic variable (temperature, humidity, or potential evapotranspiration), demonstrating the importance of climate when predicting bat distribution. Our acoustic surveys had a species’ detection probability varying from 0.19 to 0.86, and it was influenced by night conditions. These findings underscore the importance of modeling imperfect detection, especially for highly vagile and elusive organisms like bats. Our results demonstrate the effectiveness of using vegetation and landscape metrics derived from high-resolution remote sensing data to model species distribution in the context of biodiversity monitoring and conservation.por
dc.description.sponsorshipThis work is funded by National Funds through FCT – Foundationfor Science and Technology under the Project UIDB/05183/2020.Field work was funded by: POSEUR, Portugal 2020, EuropeanUnion - Cohesion Fund and the Environmental Fund (POSEUR-03-2215-FC-000097). Foundation for Science and Technology(FCT) funded FCM via the national PhD research studentship(2020.05448.BD). SG is supported by the FUEL-SAT project“Integration of multi-source satellite data for wildland fuel map-ping: the role of remote sensing for an effective wildfire fuelmanagement” from FCT (PCIF/GRF/0116/ 2019). NG and JTM arefunded by FCT under the Project UIDB/05183/2020. HR is sup-ported by FCT under FCT cE3c- Centre for Ecology, Evolution andEnvironmental Changes, unit funding UIDB/00329/2020 (DOI10.54499/UIDB/00329/2020). MED and CHANGE research insti-tutes are funded FCT with project references: MED UIDB/05183/2020 (DOI 10.54499/UIDB/05183/2020) and CHANGE LA/P/0121/2020 (DOI 10.54499/LA/P/0121/2020). Forest Research Centre(CEF) and TERRA are funded by FCT, respectively by projectreferences UIDB/00239/2020 (DOI 10.54499/UIDB/00239/2020)and LA/P/0092/2020 (DOI 10.54499/LA/P/0092/2020). MED,CHANGE, CEF and TERRA contributed to the work and theopen access of this research.por
dc.identifier.authoremaild49669@alunos.uevora.pt
dc.identifier.authoremailsgodinho@uevora.pt
dc.identifier.authoremailnunogui@uevora.pt
dc.identifier.authoremaildenism@uevora.pt
dc.identifier.authoremailhugo.rebelo@cibio.up.pt
dc.identifier.authoremailpsegurado@isa.ulisboa.pt
dc.identifier.authoremailjtiagom@uevora.pt
dc.identifier.citationMartins, F. C., Godinho, S., Guiomar, N., Medinas, D., Rebelo, H., Segurado, P., & Marques, J. T. (2024). Vegetation canopy height shapes bats’ occupancy: a remote sensing approach. GIScience & Remote Sensing, 61(1). https://doi.org/10.1080/15481603.2024.2374150por
dc.identifier.doi10.1080/15481603.2024.2374150por
dc.identifier.scientificarea221por
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/15481603.2024.2374150
dc.identifier.urihttp://hdl.handle.net/10174/37613
dc.language.isoporpor
dc.peerreviewedyespor
dc.publisherTaylor & Francispor
dc.rightsopenAccesspor
dc.subjectLand coverpor
dc.subjectSpecies occurrencepor
dc.subjectGEDIpor
dc.subjectvegetation canopy heightpor
dc.subjectBatspor
dc.titleVegetation canopy height shapes bats’ occupancy: a remote sensing approachpor
dc.typearticlepor
degois.publication.issue61por
degois.publication.titleGIScience & Remote Sensingpor
degois.publication.volume1por

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