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A Sensitivity Analysis of the Application of Integrated Species Distribution Models to Mobile Species: A Case Study with the Endangered Baird’s Tapir

Published online by Cambridge University Press:  12 June 2019

Cody J Schank ,
Michael V Cove ,
Marcella J Kelly ,
Clayton K Nielsen ,
Georgina O’Farrill ,
Ninon Meyer ,
Christopher A Jordan ,
Jose F González-Maya ,
Diego J Lizcano  and
Ricardo Moreno
...Show all authors
Cody J Schank*
Affiliation:
Department of Geography and the Environment, The University of Texas at Austin, Austin, TX 78712, USA Global Wildlife Conservation, Austin, TX, USA
Michael V Cove
Affiliation:
Department of Applied Ecology, North Carolina State University, Raleigh, NC 27695, USA
Marcella J Kelly
Affiliation:
Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA 24061, USA
Clayton K Nielsen
Affiliation:
Department of Forestry and Cooperative Wildlife Research Laboratory, Southern Illinois University, Carbondale, IL 62901-6504, USA
Georgina O’Farrill
Affiliation:
Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, Toronto, Ontario, M5S 3G5, Canada
Ninon Meyer
Affiliation:
El Colegio de la Frontera Sur, Departamento de Conservacion de la Biodiversidad, Lerma, Campeche, Mexico Fundación Yaguara-Panama, Ciudad del Saber, Panama
Christopher A Jordan
Affiliation:
Global Wildlife Conservation, Austin, TX, USA Panthera, New York, NY, USA Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
Jose F González-Maya
Affiliation:
Proyecto de Conservación de Aguas y Tierras, ProCAT Colombia/Internacional, Bogotá, Colombia
Diego J Lizcano
Affiliation:
Departamento Central de Investigación, Universidad Laica Eloy Alfaro de Manabí, Manta, Ecuador The Nature Conservancy, Bogotá, Colombia
Ricardo Moreno
Affiliation:
Fundación Yaguara-Panama, Ciudad del Saber, Panama Smithsonian Tropical Research Institute, Balboa, Panama
Michael Dobbins
Affiliation:
Department of Geography, University of Florida, Gainesville, FL, USA
Victor Montalvo
Affiliation:
Instituto Internacional en Conservación y Manejo de Vida Silvestre, Universidad Nacional, Heredia 3000-1350, Costa Rica
Juan Carlos Cruz Díaz
Affiliation:
Instituto Internacional en Conservación y Manejo de Vida Silvestre, Universidad Nacional, Heredia 3000-1350, Costa Rica Department of Environmental Conservation, University of Massachusetts Amherst, MA, 01003, USA
Gilberto Pozo Montuy
Affiliation:
Conservación de la Biodiversidad del Usumacinta A.C., Emiliano Zapata, Tabasco, C.P. 86990, Mexico
J Antonio de la Torre
Affiliation:
Instituto de Ecología, UNAM, Laboratorio de Ecología y Conservación de Vertebrados Terrestres, Ap. Postal 70-275, C.P. 04510 Ciudad Universitaria, Mexico Bioconciencia A.C., Ciudad de México, Mexico
Esteban Brenes-Mora
Affiliation:
Nai Conservation, San José, Costa Rica Escuela de Biología, Universidad de Costa Rica, Ciudad Universitaria, San José 2060, CostaRica
Margot A Wood
Affiliation:
Conservation International, Arlington, VA, USA
Jessica Gilbert
Affiliation:
Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX, USA
Walter Jetz
Affiliation:
Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, UK
Jennifer A Miller
Affiliation:
Department of Geography and the Environment, The University of Texas at Austin, Austin, TX 78712, USA
*
Author for correspondence: Cody J Schank, Email: [email protected]
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Summary

Species distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird’s tapir (Tapirus bairdii) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the individual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subsampling prior to implementation.

Keywords

species distribution modeloccupancy modeleffective sampling areapoint process modeldensityconservationtapirTapirus bairdii
Type
Research Paper
Information
Environmental Conservation , Volume 46 , Issue 3: Thematic Section: Bringing Species and Ecosystems Together with Remote Sensing Tools to Develop New Biodiversity Metrics and Indicators , September 2019 , pp. 184 - 192
Copyright
© Foundation for Environmental Conservation 2019 

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References

Arino, O, Perez, JJR, Kalogirou, V, Bontemps, S, Defourny, P, Van Bogaert, E (2012) Global land cover map for 2009 (GlobCover 2009). PANGAEA [www document]. URL https://doi.pangaea.de/10.1594/PANGAEA.787668.Google Scholar
Bailey, DW, Gross, JE, Laca, EA (1996) Mechanisms that result in large herbivore grazing distribution patterns. Journal of Range Management 49: 386–400.CrossRefGoogle Scholar
Barber, CP, Cochrane, MA, Souza, CM Jr, Laurance, WF (2014) Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biological Conservation 177, 203–209.CrossRefGoogle Scholar
Bean, WT, Stafford, R, Brashares, JS (2012) The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography 35: 250–258.CrossRefGoogle Scholar
Beck, J, Böller, M, Erhardt, A, Schwanghart, W (2014) Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecological Informatics 19: 10–15.CrossRefGoogle Scholar
Botello, F, Romero-Calderón, AG, Sánchez-Hernández, J, Hernández, O, López-Villegas, G, Sánchez-Cordero, V (2017) Densidad poblacional del tapir centroamericano (Tapirella bairdii) en bosque mesófilo de montaña en Totontepec Villa de Morelos, Oaxaca, México. Revista Mexicana de Biodiversidad 88: 918–923.CrossRefGoogle Scholar
Boyce, MS, Vernier, PR, Nielsen, SE, Schmiegelow, FKA (2002) Evaluating resource selection functions. Ecological Modelling 157: 281–300.CrossRefGoogle Scholar
Brooks, DM, Bodmer, RE, Matola, S (1997) Tapir Action Plan. Campo Grande, Brazil: IUCN/SSC Tapir Specialist Group.Google Scholar
Carbajal-Borges, JP, Godínez-Gómez, O, Mendoza, E (2014) Density, abundance and activity patterns of the endangered Tapirus bairdii in one of its last strongholds in southern Mexico. Tropical Conservation Science 7: 100–114.CrossRefGoogle Scholar
Cove, MV, Pardo Vargas, LE, de la Cruz, JC, Spínola, RM, Jackson, VL, Saénz, JC, Chassot, O (2014) Factors influencing the occurrence of the endangered Baird’s tapir Tapirus bairdii: potential flagship species for a Costa Rican biological corridor. Oryx 48: 402–409.CrossRefGoogle Scholar
de la Torre, JA, Rivero, M, Camacho, G, `lvarez-Márquez, LA (2017) Assessing occupancy and habitat connectivity for Baird’s tapir to establish conservation priorities in the Sierra Madre de Chiapas, Mexico. Journal for Nature Conservation 41: 16–25.CrossRefGoogle Scholar
Dorazio, RM (2014) Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Global Ecology and Biogeography 23: 1472–1484.CrossRefGoogle Scholar
Efford, MG, Dawson, DK (2012) Occupancy in continuous habitat. Ecosphere 3: 1–15.CrossRefGoogle Scholar
Elith, J, Leathwick, JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40: 677–697.CrossRefGoogle Scholar
Engler, R, Guisan, A, Rechsteiner, L (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology 41: 263–274.CrossRefGoogle Scholar
Eugster, MJA, Schlesinger, T (2010) osmar: OpenStreetMap and R. R-Journal [www document]. URL http://osmar.r-forge.r-project.org/RJpreprint.pdf.Google Scholar
Ferreguetti, ÁC, Tomás, WM, Bergallo, HG (2017) Density, occupancy, and detectability of lowland tapirs, Tapirus terrestris, in Vale Natural Reserve, southeastern Brazil. Journal of Mammalogy 98: 114–123.CrossRefGoogle Scholar
Fielding, AH, Bell, JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38–49.CrossRefGoogle Scholar
Fithian, W, Elith, J, Hastie, T, Keith, DA (2015) Bias correction in species distribution models: pooling survey and collection data for multiple species. Methods in Ecology and Evolution 6: 424–438.CrossRefGoogle ScholarPubMed
Foerster, CR, Vaughan, C (2002) Home range, habitat use, and activity of Baird’s tapir in Costa Rica. Biotropica 34: 423–437.CrossRefGoogle Scholar
Foster, RJ, Harmsen, BJ (2012) A critique of density estimation from camera-trap data. Journal of Wildlife Management 76: 224–236.CrossRefGoogle Scholar
Franklin, J (2010) Mapping Species Distributions: Spatial Inference and Prediction. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
García, M, Jordan, CA, O’Farril, G, Poot, C, Meyer, N, Estrada, N, … Ruiz-Galeano, M (2016) Tapirus bairdii. The IUCN Red List of Threatened Species [www document]. URL http://dx.doi.org/10.2305/IUCN.UK.2016-1.RLTS.T21471A45173340.en.CrossRefGoogle Scholar
Golub, GH, Van Loan, CF (2012) Matrix Computations. Baltimore, MD, USA: Johns Hopkins University Press.Google Scholar
González-Maya, JF, Schipper, J, Polidoro, B, Hoepker, A, Zárrate-Charry, D, Belant, JL (2012) Baird’s tapir density in high elevation forests of the Talamanca region of Costa Rica. Integrative Zoology 7: 381–388.CrossRefGoogle ScholarPubMed
Hansen, MC, Potapov, PV, Moore, R, Hancher, M, Turubanova, SA, Tyukavina, A, … Townshend, JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342: 850–853.CrossRefGoogle ScholarPubMed
Hijmans, RJ, van Etten, J, Cheng, J, Mattiuzzi, M, Sumner, M, Greenberg, JA, … Wueest, R (2016) Package ‘raster’ [www document]. URL http://healthstat.snu.ac.kr/CRAN/web/packages/raster/raster.pdf.Google Scholar
Hirzel, AH, Le Lay, G, Helfer, V, Randin, C, Guisan, A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling 199: 142–152.CrossRefGoogle Scholar
Jordan, CA (2015) The dynamics of wildlife and environmental knowledge in a bioculturally diverse coupled natural and human system in the Caribbean region of Nicaragua. PhD thesis. East Lansing, MI, USA: Michigan State University.Google Scholar
Jordan, CAJ, Hoover, B, Dans, AJ, Schank, C, Miller, JA (2019) The impact of Hurricane Otto on Baird’s tapir movement in Nicaragua’s Indio Maíz Biological Reserve. In: Movement Ecology of Neotropical Forest Mammals, eds Reyna-Hurtado, R, Chapman, CA, pp. 5–20. New York, NY, USA: Springer.CrossRefGoogle Scholar
Jordan, CA, Schank, CJ, Urquhart, GR, Dans, AJ (2016) Terrestrial mammal occupancy in the context of widespread forest loss and a proposed interoceanic canal in Nicaragua’s decreasingly remote south Caribbean region. PLoS One 11: e0151372.CrossRefGoogle Scholar
Karger, DN, Conrad, O, Böhner, J, Kawohl, T, Kreft, H, Soria-Auza, RW, … Kessler, M (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4: 170122 CrossRefGoogle ScholarPubMed
Koshkina, V, Wang, Y, Gordon, A, Dorazio, RM, White, M, Stone, L (2017) Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection. Methods in Ecology and Evolution 8: 420–430.CrossRefGoogle Scholar
Lahoz-Monfort, JJ, Guillera-Arroita, G, Wintle, BA (2014) Imperfect detection impacts the performance of species distribution models. Global Ecology and Biogeography 23: 504–515.CrossRefGoogle Scholar
Lobo, JM, Jiménez-Valverde, A, Hortal, J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33: 103–114.CrossRefGoogle Scholar
MacKenzie, DI, Nichols, JD, Hines, JE, Knutson, MG, Franklin, AB (2003) Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84: 2200–2207.CrossRefGoogle Scholar
MacKenzie, DI, Nichols, JD, Royle, JA, Pollock, KH, Bailey, LL, Hines, JE (2006) Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence. Amsterdam, The Netherlands: Elsevier.Google Scholar
MacKenzie, DI, Royle, JA (2005) Designing occupancy studies: general advice and allocating survey effort. Journal of Applied Ecology 42: 1105–1114.CrossRefGoogle Scholar
Mair, L, Ruete, A (2016) Explaining spatial variation in the recording effort of citizen science data across multiple taxa. PLoS One 11: e0147796.CrossRefGoogle ScholarPubMed
Medici, EP, Carrillo, L, Montenegro, OL, Miller, PS, Carbonell, F, Chassot, O, … Mendoza, A (2005) Baird’s Tapir (Tapirus bairdii) Conservation Workshop Population and Habitat Viability Assessment (PHVA). Campo Grande, Brazil: IUCN/SSC Tapir Specialist Group.Google Scholar
Mejía-Correa, S, Diaz-Martinez, A, Molina, R (2014) Densidad y hábitos alimentarios de la danta Tapirus bairdii en el Parque Nacional Natural Los Katios, Colombia. Tapir Conservation 23: 16–23.Google Scholar
Miller, JA (2014) Virtual species distribution models: using simulated data to evaluate aspects of model performance. Progress in Physical Geography 38: 117–128.CrossRefGoogle Scholar
Naranjo-Piñera, E (1995) Abundancia y uso de hábitat del tapir (Tapirus bairdii) en un bosque tropical húmedo de Costa Rica. Vida Silvestre Neotropical 4: 20–31.Google Scholar
NASA (2017) MODIS Collection 6 NRT Hotspot/Active Fire Detections MCD14DL [www document]. URL https://doi.org/10.5067/FIRMS/MODIS/MCD14DL.NRT.006.CrossRefGoogle Scholar
Noss, AJ, Gardner, B, Maffei, L, Cuéllar, E, Montaño, R, Romero-Muñoz, A, … O’Connell, AF (2012) Comparison of density estimation methods for mammal populations with camera traps in the Kaa-Iya del Gran Chaco landscape. Animal Conservation 15: 527–535.CrossRefGoogle Scholar
Pettorelli, N, Ryan, S, Mueller, T, Bunnefeld, N (2011) The normalized difference vegetation index (NDVI): unforeseen successes in animal ecology. Climate Research 46: 15–27.CrossRefGoogle Scholar
Renner, IW, Elith, J, Baddeley, A, Fithian, W, Hastie, T, Phillips, SJ, … Warton, DI (2015) Point process models for presence-only analysis. Methods in Ecology and Evolution 6: 366–379.CrossRefGoogle Scholar
Reyna-Hurtado, R, Sanvicente-López, M, Pérez-Flores, J, Carrillo-Reyna, N, Calmé, S (2016) Insights into the multiannual home range of a Baird’s tapir (Tapirus bairdii) in the Maya Forest. THERYA 7: 271–276.CrossRefGoogle Scholar
Rota, CT, Fletcher, RJ Jr, Dorazio, RM (2009) Occupancy estimation and the closure assumption. Journal of Applied Ecology 46: 1173–1181.Google Scholar
Royle, JA, Chandler, RB, Sollmann, R, Gardner, B (2013) Spatial Capture–Recapture. Amsterdam, The Netherlands: Elsevier Science.Google ScholarPubMed
Royle, JA, Dorazio, RM (2008) Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Amsterdam, The Netherlands: Elsevier Science.Google Scholar
Rupprecht, F, Oldeland, J, Finckh, M (2011) Modelling potential distribution of the threatened tree species Juniperus oxycedrus: how to evaluate the predictions of different modelling approaches? Journal of Vegetation Science 22: 647–659.CrossRefGoogle Scholar
Schank, CJ (2018) Investigation of novel methods to predict the distribution, abundance, and connectivity of rare species: a case study for the conservation of Baird’s tapir. Doctoral dissertation. Austin, TX, USA: University of Texas at Austin.Google Scholar
Schank, CJ, Cove, MV, Kelly, MJ, Mendoza, E, O’Farrill, G, Reyna-Hurtado, R, … Miller, JA (2017) Using a novel model approach to assess the distribution and conservation status of the endangered Baird’s tapir. Diversity and Distributions 23: 1459–1471.CrossRefGoogle Scholar
UNEP-WCMC (2014) The World Database on Protected Areas [data set] [www document]. URL https://protectedplanet.net.Google Scholar
Warton, DI, Shepherd, LC (2010) Poisson point process models solve the ‘pseudo-absence problem’ for presence-only data in ecology. Annals of Applied Statistics 4: 1383–1402.CrossRefGoogle Scholar
White, GC (1982) Capture–Recapture and Removal Methods for Sampling Closed Populations. Los Alamos, NM, USA: Los Alamos National Laboratory.Google Scholar
Zurell, D, Berger, U, Cabral, JS, Jeltsch, F, Meynard, CN, Münkemüller, T, … Grimm, V (2010) The virtual ecologist approach: simulating data and observers. Oikos 119: 622–635.CrossRefGoogle Scholar
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