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

Publication ,  Journal Article
Nuñez, CL; Froese, G; Meier, AC; Beirne, C; Depenthal, J; Kim, S; Mbélé, AE; Nordseth, A; Poulsen, JR
Published in: Ecosphere
December 1, 2019

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.

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Published In

Ecosphere

DOI

EISSN

2150-8925

Publication Date

December 1, 2019

Volume

10

Issue

12

Related Subject Headings

  • 4102 Ecological applications
  • 3103 Ecology
  • 0608 Zoology
  • 0602 Ecology
  • 0501 Ecological Applications
 

Citation

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Nuñez, C. L., Froese, G., Meier, A. C., Beirne, C., Depenthal, J., Kim, S., … Poulsen, J. R. (2019). Stronger together: comparing and integrating camera trap, visual, and dung survey data in tropical forest communities. Ecosphere, 10(12). https://doi.org/10.1002/ecs2.2965
Nuñez, C. L., G. Froese, A. C. Meier, C. Beirne, J. Depenthal, S. Kim, A. E. Mbélé, A. Nordseth, and J. R. Poulsen. “Stronger together: comparing and integrating camera trap, visual, and dung survey data in tropical forest communities.” Ecosphere 10, no. 12 (December 1, 2019). https://doi.org/10.1002/ecs2.2965.
Nuñez CL, Froese G, Meier AC, Beirne C, Depenthal J, Kim S, et al. Stronger together: comparing and integrating camera trap, visual, and dung survey data in tropical forest communities. Ecosphere. 2019 Dec 1;10(12).
Nuñez, C. L., et al. “Stronger together: comparing and integrating camera trap, visual, and dung survey data in tropical forest communities.” Ecosphere, vol. 10, no. 12, Dec. 2019. Scopus, doi:10.1002/ecs2.2965.
Nuñez CL, Froese G, Meier AC, Beirne C, Depenthal J, Kim S, Mbélé AE, Nordseth A, Poulsen JR. Stronger together: comparing and integrating camera trap, visual, and dung survey data in tropical forest communities. Ecosphere. 2019 Dec 1;10(12).

Published In

Ecosphere

DOI

EISSN

2150-8925

Publication Date

December 1, 2019

Volume

10

Issue

12

Related Subject Headings

  • 4102 Ecological applications
  • 3103 Ecology
  • 0608 Zoology
  • 0602 Ecology
  • 0501 Ecological Applications