Spatially explicit power analysis for detecting occupancy trends for multiple species.
| Title: | Spatially explicit power analysis for detecting occupancy trends for multiple species. |
|---|---|
| Authors: | Southwell DM; Quantitive and Applied Ecology Group, School of BioSciences, University of Melbourne, Melbourne, Victoria, 3010, Australia.; Einoder LD; Flora and Fauna Division, Department of Environment and Natural Resources, P.O. Box 496, Palmerston, Northern Territory, 0831, Australia.; Lahoz-Monfort JJ; Quantitive and Applied Ecology Group, School of BioSciences, University of Melbourne, Melbourne, Victoria, 3010, Australia.; Fisher A; Flora and Fauna Division, Department of Environment and Natural Resources, P.O. Box 496, Palmerston, Northern Territory, 0831, Australia.; Gillespie GR; Flora and Fauna Division, Department of Environment and Natural Resources, P.O. Box 496, Palmerston, Northern Territory, 0831, Australia.; Wintle BA; Quantitive and Applied Ecology Group, School of BioSciences, University of Melbourne, Melbourne, Victoria, 3010, Australia.; Threatened Species Recovery Hub, National Environmental Science Program, School of BioSciences, University of Melbourne, Melbourne, Victoria, 3010, Australia. |
| Source: | Ecological applications : a publication of the Ecological Society of America [Ecol Appl] 2019 Sep; Vol. 29 (6), pp. e01950. Date of Electronic Publication: 2019 Jul 16. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Language: | English |
| Journal Info: | Publisher: Ecological Society of America Country of Publication: United States NLM ID: 9889808 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1051-0761 (Print) Linking ISSN: 10510761 NLM ISO Abbreviation: Ecol Appl Subsets: MEDLINE |
| Imprint Name(s): | Publication: Washington, D.C. : Ecological Society of America; Original Publication: Tempe, AZ : The Society, 1991- |
| MeSH Terms: | Birds* ; Ecosystem*; Animals ; Australia ; Ecology ; Environmental Monitoring |
| Abstract: | Assessing the statistical power to detect changes in wildlife populations is a crucial yet often overlooked step when designing and evaluating monitoring programs. Here, we developed a simulation framework to perform spatially explicit statistical power analysis of biological monitoring programs for detecting temporal trends in occupancy for multiple species. Using raster layers representing the spatial variation in current occupancy and species-level detectability for one or multiple observation methods, our framework simulates changes in occupancy over space and time, with the capacity to explicitly model stochastic disturbances at monitoring sites (i.e., dynamic landscapes). Once users specify the number and location of sites, the frequency and duration of surveys, and the type of detection method(s) for each species, our framework estimates power to detect occupancy trends, both across the landscape and/or within nested management units. As a case study, we evaluated the power of a long-term monitoring program to detect trends in occupancy for 136 species (83 birds, 33 reptiles, and 20 mammals) across and within Kakadu, Litchfield, and Nitmiluk National Parks in northern Australia. We assumed continuation of an original monitoring design implemented since 1996, with the addition of camera trapping. As expected, power to detect trends was sensitive to the direction and magnitude of the change in occupancy, detectability, initial occupancy levels, and the rarity of species. Our simulations suggest that monitoring has at least an 80% chance at detecting a 50% decline in occupancy for 22% of the modeled species across the three parks over the next 15 yr. Monitoring is more likely to detect increasing occupancy trends, with at least an 80% chance at detecting a 50% increase in 87% of species. The addition of camera-trapping increased average power to detect a 50% decline in mammals compared with using only live trapping by 63%. We provide a flexible tool that can help decision-makers design and evaluate monitoring programs for hundreds of species at a time in a range of ecological settings, while explicitly considering the distribution of species and alternative sampling methods.; (© 2019 by the Ecological Society of America.) |
| References: | Bailey, L. L., J. E. Hines, J. D. Nichols, and D. I. MacKenzie. 2007. Sampling design trade-offs in occupancy studies with imperfect detection: Examples and software. Ecological Applications 17:281-290.; Balme, G. A., L. T. B. Hunter, and R. Slotow. 2009. Evaluating methods for counting cryptic carnivores. Journal of Wildlife Management 73:433-441.; Barata, I. M., R. A. Griffiths, and M. S. Ridout. 2017. The power of monitoring: optimizing survey designs to detect occupancy changes in a rare amphibian population. Scientific Reports 7, 16491.; Burgman, M. A., D. R. Breininger, B. W. Duncan, and S. Ferson. 2001. Setting reliability bounds on habitat suitability indices. Ecological Applications 11:70-78.; Einoder, L., D. Southwell, J. Lahoz-Monfort, G. Gillespie, and B. Wintle. 2018. Occupancy and detectability modelling of vertebrates in northern Australia using multiple sampling methods. PLoS ONE 13:e0203304.; Ellis, M. M., J. S. Ivan, and M. K. Schwartz. 2014. Spatially explicit power analyses for occupancy-based monitoring of wolverine in the U.S. Rocky Mountains. Conservation Biology 28:52-62.; Ellis, M. M., J. S. Ivan, J. M. Tucker, and M. K. Schwartz. 2015. rSPACE: Spatially based power analysis for conservation and ecology. Methods in Ecology and Evolution 6:621-625.; Field, S. A., A. J. Tyre, and H. P. Possingham. 2005. Optimizing allocation of monitoring effort under economic and observational constraints. Journal of Wildlife Management 69:473-482.; Fiske, I., and R. Chandler. 2011. unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. Journal of Statistical Software 43:1-23.; Galvez, N., G. Guillera-Arroita, F. A. V. St John, E. Schuttler, D. W. Macdonald, and Z. G. Davies. 2018. A spatially integrated framework for assessing socioecological drivers of carnivore decline. Journal of Applied Ecology 55:1393-1405.; Gaston, K. J., T. M. Blackburn, J. J. D. Greenwood, R. D. Gregory, R. M. Quinn, and J. H. Lawton. 2000. Abundance-occupancy relationships. Journal of Applied Ecology 37:39-59.; Gerber, L. R., D. P. DeMaster, and P. M. Kareiva. 1999. Gray whales and the value of monitoring data in implementing the US Endangered Species Act. Conservation Biology 13:1215-1219.; Gill, A. M., P. G. Ryan, P. H. R. Moore, and M. Gibson. 2000. Fire regimes of World Heritage Kakadu National Park, Australia. Austral Ecology 25:616-625.; Gillespie, G. R., K. Brennan, T. Gentles, B. Hill, J. Low Choy, T. Mahney, A. Stevens, and D. Stokeld. 2015. A guide for the use of remote cameras for wildlife surveys in northern Australia. National Environmental Research Program, Northern Australia Hub, Charles Darwin University, Casuarina, Northern Territory, USA.; Guillera-Arroita, G., and J. J. Lahoz-Monfort. 2012. Designing studies to detect differences in species occupancy: power analysis under imperfect detection. Methods in Ecology and Evolution 3:860-869.; Guillera-Arroita, G., M. S. Ridout, and B. J. T. Morgan. 2010. Design of occupancy studies with imperfect detection. Methods in Ecology and Evolution 1:131-139.; Hijmans, R. J., and J. van Etten. 2012. raster: Geographic analysis and modelling with raster data. http://CRAN.R-project.org/package=raster.; Holling, C. S. 1978. Adaptive environmental assessment and management. John Wiley and Sons, London, UK.; Karafyllidis, I., and A. Thanailakis. 1997. A model for predicting forest fire spreading using cellular automata. Ecological Modelling 99:87-97.; Lahoz-Monfort, J. J., G. Guillera-Arroita, and B. A. Wintle. 2014. Imperfect detection impacts the performance of species distribution models. Global Ecology and Biogeography 23:504-515.; Latif, Q. S., M. M. Ellis, V. A. Saab, and K. Mellen-McLean. 2018. Simulations inform design of regional occupancy-based monitoring for a sparsely distributed, territorial species. Ecology and Evolution 8:1171-1185.; Legg, C. J., and L. Nagy. 2006. Why most conservation monitoring is, but need not be, a waste of time. Journal of Environmental Management 78:194-199.; Loos, J., J. Hanspach, H. von Wehrden, M. Beldean, C. I. Moga, and J. Fischer. 2015. Developing robust field survey protocols in landscape ecology: a case study on birds, plants and butterflies. Biodiversity and Conservation 24:33-46.; Mackenzie, D. I., and J. A. Royle. 2005. Designing occupancy studies: general advice and allocating survey effort. Journal of Applied Ecology 42:1105-1114.; McCarthy, M. A., and G. A. Carey. 2002. Fire regimes in landscapes: models and realities. Pages 77-93 in R. Bradstock, J. Williams, and M. Gill, editors. Flammable Australia: the fire regimes and biodiversity of a continent. Cambridge University Press, Cambridge, UK.; McCarthy, M. A., et al. 2013. The influence of abundance on detectability. Oikos 122:717-726.; Nichols, J. D., and B. K. Williams. 2006. Monitoring for conservation. Trends in Ecology & Evolution 21:668-673.; Nimmo, D. G., L. T. Kelly, L. M. Farnsworth, S. J. Watson, and A. F. Bennett. 2014. Why do some species have geographically varying responses to fire history? Ecography 37:805-813.; Possingham, H. P., B. A. Wintle, R. A. Fuller, and L. N. Joseph. 2012. The Conservation return on investment from ecological monitoring. Pages 49-61 in D. B. Lindenmayer and P. Gibbons, editors. Making biodiversity monitoring happen in Australia. CSIRO Publishing, Melbourne, Victoria, Australia.; Prendergast, J. R., R. M. Quinn, J. H. Lawton, B. C. Eversham, and D. W. Gibbons. 1993. Rare species, the coincidence of diversity hotspots and conservation strategies. Nature 365:335-337.; R Development Core Team. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.R-project.org.; Rabinowitz, D. 1981. Seven forms of rarity. Pages 205-217 in H. Synge, editor. The biological aspects of rare plant conservation. John Wiley & Sons, Chichester, UK.; Rhodes, J. R., A. J. Tyre, N. Jonzen, C. A. McAlpine, and H. P. Possingham. 2006. Optimizing presence-absence surveys for detecting population trends. Journal of Wildlife Management 70:8-18.; Sewell, D., T. J. C. Beebee, and R. A. Griffiths. 2010. Optimising biodiversity assessments by volunteers: The application of occupancy modelling to large-scale amphibian surveys. Biological Conservation 143:2102-2110.; Sewell, D., G. Guillera-Arroita, R. A. Griffiths, and T. J. C. Beebee. 2012. When Is a species declining? Optimizing survey effort to detect population changes in reptiles. PLoS ONE 7:e43387. https://doi.org/10.1371/journal.pone.0043387.; Smith, J., S. Legge, A. James, and K. Tuft. 2017. Optimising camera trap deployment design across multiple sites for species inventory surveys. Pacific Conservation Biology 23:43-51.; Stanley, T. R., and J. A. Royle. 2005. Estimating site occupancy and abundance using indirect detection indices. Journal of Wildlife Management 69:874-883.; Steenweg, R., J. Whittington, M. Hebblewhite, A. Forshner, B. Johnston, D. Petersen, B. Shepherd, and P. M. Lukacs. 2016. Camera-based occupancy monitoring at large scales: Power to detect trends in grizzly bears across the Canadian Rockies. Biological Conservation 201:192-200.; Steidl, R. J., J. P. Hayes, and E. Schauber. 1997. Statistical power analysis in wildlife research. Journal of Wildlife Management 61:270-279.; Strayer, D. L. 1999. Statistical power of presence-absence data to detect population declines. Conservation Biology 13:1034-1038.; Thomas, L., and F. Juanes. 1996. The importance of statistical power analysis: An example from Animal Behaviour. Animal Behaviour 52:856-859.; Thorn, M., M. Green, P. W. Bateman, S. Waite, and D. M. Scott. 2011. Brown hyaenas on roads: Estimating carnivore occupancy and abundance using spatially auto-correlated sign survey replicates. Biological Conservation 144:1799-1807.; Wheeler, B. D. 1988. Species richness, species rarity and conservation evaluation of rich-fen vegetation in lowland England and Wales. Journal of Applied Ecology 25:331-352.; Woinarski, J. C. Z., D. J. Milne, and G. Wanganeen. 2001. Changes in mammal populations in relatively intact landscapes of Kakadu National Park, Northern Territory, Australia. Austral Ecology 26:360-370.; Woinarski, J. C. Z., et al. 2010. Monitoring indicates rapid and severe decline of native small mammals in Kakadu National Park, northern Australia. Wildlife Research 37:116-126.; Woinarski, J. C. Z., et al. 2012. Monitoring indicates greater resilience for birds than for mammals in Kakadu National Park, northern Australia. Wildlife Research 39:397-407. |
| Grant Information: | International Australian Government's National Environmental Science Programme; International LTERN |
| Contributed Indexing: | Keywords: Kakadu; occupancy; optimal monitoring; population declines; simulation; spatially explicit power analysis; species distribution modeling; statistical power |
| Entry Date(s): | Date Created: 20190613 Date Completed: 20191011 Latest Revision: 20200108 |
| Update Code: | 20260130 |
| DOI: | 10.1002/eap.1950 |
| PMID: | 31187919 |
| Database: | MEDLINE |
Journal Article; Research Support, Non-U.S. Gov't