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Stronger together : comparing and integrating camera trap, visual, and dung survey data in tropical forest communities

Stronger together : comparing and integrating camera trap, visual, and dung survey data in tropical forest communities

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NUNEZ, Chase L., Graden FROESE, Amelia C. MEIER, Chris BEIRNE, Johanna DEPENTHAL, Seokmin KIM, Alex EBANG MBÉLÉ, Anna NORDSETH, John R. POULSEN, 2019. Stronger together : comparing and integrating camera trap, visual, and dung survey data in tropical forest communities. In: Ecosphere. Ecological Society of America (ESA). 10(12), e02965. eISSN 2150-8925. Available under: doi: 10.1002/ecs2.2965

@article{Nunez2019-12-19Stron-52866, title={Stronger together : comparing and integrating camera trap, visual, and dung survey data in tropical forest communities}, year={2019}, doi={10.1002/ecs2.2965}, number={12}, volume={10}, journal={Ecosphere}, author={Nunez, Chase L. and Froese, Graden and Meier, Amelia C. and Beirne, Chris and Depenthal, Johanna and Kim, Seokmin and Ebang Mbélé, Alex and Nordseth, Anna and Poulsen, John R.}, note={Article Number: e02965} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/52866"> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/28"/> <dc:rights>Attribution 3.0 Unported</dc:rights> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:contributor>Kim, Seokmin</dc:contributor> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/52866/3/Nunez_2-1nlddymne4nrb3.pdf"/> <dc:contributor>Poulsen, John R.</dc:contributor> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/28"/> <dc:creator>Nordseth, Anna</dc:creator> <dc:contributor>Ebang Mbélé, Alex</dc:contributor> <dc:contributor>Nordseth, Anna</dc:contributor> <dcterms:title>Stronger together : comparing and integrating camera trap, visual, and dung survey data in tropical forest communities</dcterms:title> <dcterms:issued>2019-12-19</dcterms:issued> <dc:creator>Meier, Amelia C.</dc:creator> <dc:creator>Depenthal, Johanna</dc:creator> <dc:creator>Ebang Mbélé, Alex</dc:creator> <dc:contributor>Nunez, Chase L.</dc:contributor> <dc:creator>Poulsen, John R.</dc:creator> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/52866/3/Nunez_2-1nlddymne4nrb3.pdf"/> <dc:creator>Froese, Graden</dc:creator> <dc:contributor>Meier, Amelia C.</dc:contributor> <dc:language>eng</dc:language> <dc:contributor>Depenthal, Johanna</dc:contributor> <dc:creator>Nunez, Chase L.</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-02-17T09:17:37Z</dcterms:available> <dc:contributor>Froese, Graden</dc:contributor> <dcterms:abstract xml:lang="eng">Accurate estimations of animal populations are necessary for management, conservation, and policy decisions. However, methods for surveying animal communities disproportionately represent specific groups or guilds. For example, transect surveys can provide robust data for large arboreal species but underestimate cryptic or small‐bodied terrestrial species, whereas camera traps have the inverse tendency. The integration of information from multiple methodologies would provide the most complete inference on population size or responses to putative covariates, yet a simple, robust framework that allows integration and comparison of multiple data sources has been lacking. We use 27,813 counts of 35 species or species groups derived from concurrent visual transects, dung transects, and camera trap surveys in tropical forests and compare them within a generalized joint attribute modeling framework (GJAM) that both compares and integrates field‐collected dung, visual, and camera trap data to quantify the species‐ and trait‐specific differences in detection for each method. The effectiveness of survey method was strongly dependent on species, as well as animal traits. These differences in effectiveness contributed to meaningful differences in the reported strength of a known important covariate for animal communities (distance to nearest village). Data fusion through GJAM allows clear and unambiguous comparisons of the counts provided from each different methodology, the incorporation of trait information, and fusion of all three data streams to generate a more complete estimate of the effects of an anthropogenic disturbance covariate. Research and conservation resources are extremely limited, which often means that field campaigns attempt to maximize the amount of information gathered especially in remote, hard‐to‐access areas. Advances in these understudied areas will be accelerated by analytical methods that can fully leverage the total body of diverse biodiversity field data, even when they are collected using different methods. We demonstrate that survey methods vary in their effectiveness for counting species based on biological traits, but more importantly that generative models like GJAM can integrate data from multiple sources in one cohesive statistical framework to make improved inference in understudied environments.</dcterms:abstract> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/52866"/> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/3.0/"/> <dc:creator>Kim, Seokmin</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-02-17T09:17:37Z</dc:date> <dc:creator>Beirne, Chris</dc:creator> <dc:contributor>Beirne, Chris</dc:contributor> </rdf:Description> </rdf:RDF>

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