Satellite Remote Sensing for Conservation ActionCase Studies from Aquatic and Terrestrial Ecosystems
Book contents
- Satellite Remote Sensing for Conservation Action
- Satellite Remote Sensing for Conservation Action
- Copyright page
- Contents
- Contributors
- Preface
- Acknowledgements
- 1A Brief Introduction to Conservation and Conservation Remote Sensing
- 2Introduction to Remote Sensing for Conservation Practitioners
- 3Satellite Remote Sensing for the Conservation of East Asia’s Coastal Wetlands
- 4Global Forest Maps in Support of Conservation Monitoring
- 5Wildfire Monitoring with Satellite Remote Sensing in Support of Conservation
- 6Ecosystem Functioning Observations for Assessing Conservation in the Doñana National Park, Spain
- 7Predicting Mule Deer Harvests in Real Time
- 8Lessons Learned from WhaleWatch
- 9The Evolution of Remote Sensing Applications Vital to Effective Biodiversity Conservation and Sustainable Development
- 10Operational Conservation Remote Sensing
- Index
- Plate Section
- References
7 - Predicting Mule Deer Harvests in Real Time
Integrating Satellite Remote Sensing Measures of Forage Quality and Climate in Idaho, United States
Published online by Cambridge University Press: 23 July 2018
Book contents
- Satellite Remote Sensing for Conservation Action
- Satellite Remote Sensing for Conservation Action
- Copyright page
- Contents
- Contributors
- Preface
- Acknowledgements
- 1A Brief Introduction to Conservation and Conservation Remote Sensing
- 2Introduction to Remote Sensing for Conservation Practitioners
- 3Satellite Remote Sensing for the Conservation of East Asia’s Coastal Wetlands
- 4Global Forest Maps in Support of Conservation Monitoring
- 5Wildfire Monitoring with Satellite Remote Sensing in Support of Conservation
- 6Ecosystem Functioning Observations for Assessing Conservation in the Doñana National Park, Spain
- 7Predicting Mule Deer Harvests in Real Time
- 8Lessons Learned from WhaleWatch
- 9The Evolution of Remote Sensing Applications Vital to Effective Biodiversity Conservation and Sustainable Development
- 10Operational Conservation Remote Sensing
- Index
- Plate Section
- References
Summary
Ungulates are an important group of species across the world that have strong ecological impacts on terrestrial vegetation and food-webs, as well as being economically valued for recreational hunting, bushmeat, and impacts on agriculture. Consequently, it would be useful to predict their population dynamics ahead of time for many management and conservation applications, yet there are almost no cases of prediction being used to guide management of these key species. In the case of recreational harvest, wildlife managers across the world are often faced with setting harvest quotas of ungulates one or two years before harvest implementation. These lags between determining the harvest quotas and the actual harvest period can often induce undesirable population oscillations of game species. This can also have consequences for other aspects of the ecosystem, including threatened or declining species. Here, we illustrate a predictive harvest management model applied to improving the harvest of mule deer, an economically and ecologically important ungulate across the state of Idaho, USA. Previously developed predictive models of key population parameters such as overwinter fawn survival were developed that linked to remotely sensed measures of vegetation productivity and snow cover from the MODIS platform. Models of these demographic rates were then included in an integrated population model that could forecast overwinter survival in late autumn when hunting seasons are set in Idaho. These models enabled managers to adjust their harvest quotas for the subsequent autumn based on readily available remotely sensed data in real-time. We demonstrate the improvements to harvest management of mule deer by comparing what harvests would have been with and without remotely sensed data. We also provide lessons for the necessary management and operational conditions that needed to be present in the Idaho Department of Fish and Game to enable such a successful, centralised, prediction system with recommendations for other management and conservation agencies. In conclusion, remote sensing measures of terrestrial environmental conditions can be a powerful tool to improve the management of ungulates worldwide.
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- Satellite Remote Sensing for Conservation ActionCase Studies from Aquatic and Terrestrial Ecosystems, pp. 194 - 228Publisher: Cambridge University PressPrint publication year: 2018
References
Ahrestani, F. S., Hebblewhite, M., and Post, E. S. (2013). The importance of observation versus process error in analyses of global ungulate populations. Scientific Reports, 3, 03125.CrossRefGoogle ScholarPubMed
Ahrestani, F. S., Hebblewhite, M., Smith, B., Running, S. W., and Post, E. (2016). Dynamic complexity and stability of herbivore populations at the species distribution scale. Ecology, 97, 3184–3194.CrossRefGoogle Scholar
Apollonio, M., Andersen, R., and Putman, R. (2010a). European Ungulates and Their Management in the 21st Century. Cambridge: Cambridge University Press.Google Scholar
Apollonio, M., Andersen, R., and Putman, R. (2010b). Present Status and Future Challenges for European Ungulate Management. Cambridge: Cambridge University Press.Google Scholar
Besbeas, P., Freeman, S. N., Morgan, B. J. T., and Catchpole, E. A. (2002). Integrating mark–recapture–recovery and census data to estimate animal abundance and demographic parameters. Biometrics, 58, 540–547.CrossRefGoogle ScholarPubMed
Bishop, C. J., White, G. C., Freddy, D. J., Watkins, B. E., and Stephenson, T. R. (2009). Effect of enhanced nutrition on mule deer population rate of change. Wildlife Monographs, 172, 1–28.CrossRefGoogle Scholar
Borowik, T., Pettorelli, N., Sonnichsen, L., and Jedrzejewska, B. (2013). Normalized difference vegetation index (NDVI) as a predictor of forage availability for ungulates in forest and field habitats. European Journal of Wildlife Research, 59, 675–682.CrossRefGoogle Scholar
Brashares, J. S., Arcese, P., Sam, M. K., et al. (2004). Bushmeat hunting, wildlife declines, and fish supply in West Africa. Science, 306, 1180–1183.CrossRefGoogle ScholarPubMed
Brodie, J., Johnson, H. E., Mitchell, M. S., et al. (2013). Relative influence of human harvest, carnivores and weather on adult female elk survival across western North America. Journal of Applied Ecology, 50, 295–305.CrossRefGoogle Scholar
Caswell, H. (2000). Prospective and retrospective perturbation analyses: their roles in conservation biology. Ecology, 81, 619–627.CrossRefGoogle Scholar
Clark, S. G. and Miloy, C. (2014). The North American model of wildlife conservation: an analysis of challenges and adaptive options. In Clark, S. G. and Rutherford, M.B., eds., Large Carnivore Conservation: Integrating Science And Policy In The North American West. Chicago, IL: University of Chicago Press, pp. 289–324.CrossRefGoogle Scholar
Cote, S. D., Rooney, T. P., Tremblay, J. P., Dussault, C., and Waller, D. M. (2004). Ecological impacts of deer overabundance. Annual Review of Ecology Evolution and Systematics, 35, 113–147.CrossRefGoogle Scholar
Eldenshink, J. (2006). A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska. Photogrammetry Engineering and Remote Sensing, 72, 1027–1035.CrossRefGoogle Scholar
Fryxell, F. M., Packer, C., McCann, K. S., Solberg, E. J., and Saether, B. E. (2010). Resource management cycles and the sustainability of harvested wildlife populations. Science, 328, 903–907.CrossRefGoogle ScholarPubMed
Gaillard, J.-M., Festa-Bianchet, M., Yoccoz, N. G., Loison, A., and Toigo, C. (2000). Temporal variation in fitness components and population dynamics of large herbivores. Annual Review of Ecology and Systematics, 31, 367–393.CrossRefGoogle Scholar
Gordon, I., Hester, A. J., and Festa-Bianchet, M. (2004). The management of wild large herbivores to meet economic, conservation and environmental objectives. Journal of Applied Ecology, 41, 1021–1031.CrossRefGoogle Scholar
Griffin, K., Hebblewhite, M., Zager, P., et al. (2011). Neonatal mortality of elk driven by climate, predator phenology and predator diversity. Journal of Animal Ecology, 80, 1246–1257.CrossRefGoogle Scholar
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J. (2002). MODIS snow-cover products. Remote Sensing of Environment, 83, 181–194.CrossRefGoogle Scholar
Hebblewhite, M., Merrill, E. H., and McDermid, G. (2008). A multi-scale test of the forage maturation hypothesis for a partially migratory Montane elk population. Ecological Monographs, 78, 141–166.CrossRefGoogle Scholar
Hobbs, N. T. (1996). Modification of ecosystems by ungulates. Journal of Wildlife Management, 60, 695–713.CrossRefGoogle Scholar
Hobbs, N. T. and Hooten, M. B. (2015). Bayesian Models: A Statistical Primer for Ecologists. Princeton, NJ: Princeton University Press.Google Scholar
Huete, A., Didan, K., Miura, T., et al. (2002). Overview of the radiometric and biophysical performance of the MODIS Vegetation indices. Remote Sensing of Environment, 83, 195–213.CrossRefGoogle Scholar
Hurley, M. A., ed. (1992). Aerial Population Surveys. Blackfoot–Clearwater Elk Study – Progress Report. Helena, MT: Montana Department of Fish, Wildlife, and Parks.Google Scholar
Hurley, M. A. (2016). Mule Deer Population Dynamics in Space and Time: Ecological Modeling Tools for Managing Ungulates. Missoula, MT: University of Montana.Google Scholar
Hurley, M. A., Unsworth, J. W., Zager, P., Hebblewhite, M., et al. (2011). Demographic response of mule deer to experimental reduction of coyotes and mountain lions in southeastern Idaho. Wildlife Monographs, 178, 1–33.CrossRefGoogle Scholar
Hurley, M. A., Hebblewhite, M., Gaillard, J. M., et al. (2014). Functional analysis of normalized difference vegetation index curves reveals overwinter mule deer survival is driven by both spring and autumn phenology. Philosophical Transactions of the Royal Society of London B, 369, 20130196.CrossRefGoogle ScholarPubMed
Hurley, M. A., Hebblewhite, M., Lukacs, P. M., et al. (2017). Regional-scale models for predicting overwinter survival of juvenile ungulates. The Journal of Wildlife Management, doi: 10.1002/jwmg.21211.CrossRefGoogle Scholar
Idaho Department of Fish and Game (2013). Big Game Harvest Statewide, Idaho Department of Fish and Game. Boise, ID: Idaho Department of Fish and Game.Google Scholar
Johnson, H. E., Mills, L. S., Stephenson, T. R., and Wehausen, J. D. (2010). Population-specific vital rate contributions influence management of an endangered ungulate. Ecological Applications, 20, 1753–1765.CrossRefGoogle ScholarPubMed
Kaplan, E. L. and Meier, D. B. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457–481.CrossRefGoogle Scholar
Kery, M. and Schaub, M. (2012). Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective. San Diego, CA: Acadmic Press.Google Scholar
Lukacs, P. M., Mitchell, M. S., Hebblewhite, M. et al. (2018). Factors influencing elk recruitment across ecotypes in the Western United States. Journal of Wildlife Management, doi: 10.1002/jwmg.21438.CrossRefGoogle Scholar
Monteith, K. L., Bleich, V. C., Stephenson, T. R., et al. (2014). Life-history characteristics of mule deer: effects of nutrition in a variable environment. Wildlife Monographs, 186, 1–62.CrossRefGoogle Scholar
Nowak, J. J., Lukacs, P. M., Hurley, M. A., et al. (2017). Customized software to streamline routine analyses for wildlife management. Wildlife Society Bulletin, doi: 10.1002/wsb.841.CrossRefGoogle Scholar
Pettorelli, N. (2013). The Normalized Difference Vegetation Index, Oxford: Oxford University Press.CrossRefGoogle Scholar
Post, E. S., Brodie, J., Hebblewhite, M., et al. (2009). Global population dynamics and hot spots of response to climate change. Bioscience, 59, 489–499.CrossRefGoogle Scholar
Quality Deer Management Association (2017). QDMA’s whitetail report 2017: an annual report on the status of white-tailed deer. The Foundation of the Hunting Industry in North America. See www.qdma.com/wp-content/uploads/2017/03/WR-2017.pdf. Accessed 15 August 2017.Google Scholar
R Core Team (2014). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.Google Scholar
Ramsay, R. and Silverman, B. W. (2005). Functional Data Analysis. New York, NY: Springer.CrossRefGoogle Scholar
Ripple, W. J., Newsome, T. M., Wolf, C., et al. (2015). Collapse of the world’s largest herbivores. Science Advances, 1, e14000103.CrossRefGoogle ScholarPubMed
Royle, J. A. and Dorazio, R. M. (2006). Hierarchical models of animal abundance and occurrence. Journal of Agricultural Biological and Environmental Statistics, 11, 249–263.CrossRefGoogle Scholar
Samuel, M. D., Garton, E. O., Schlegel, M. W., and Carson, R. G. (1987). Visibility bias during aerial surveys of elk in northcentral Idaho. Journal of Wildlife Management, 51, 622–630.CrossRefGoogle Scholar
Samuel, M. D., Steinhorst, R. K., Garton, E. O., and Unsworth, J. W. (1992). Estimation of wildlife population ratios incorporating survey design and visibility bias. Journal of Wildlife Management, 54, 718–725.CrossRefGoogle Scholar
Schaub, M. and Kery, M. (2012). Combining information in hierarchical models improves inferences in population ecology and demographic population analyses. Animal Conservation, 15, 125–126.CrossRefGoogle Scholar
Shallow, J. R. T., Hurley, M. A., Monteith, K. L., and Bowyer, R. T. (2015). Cascading effects of habitat on maternal condition and life-history characteristics of neonatal mule deer. Journal of Mammalogy, 96, 194–205.CrossRefGoogle Scholar
Silvy, N. J. (2012). The Wildlife Techniques Manual: Volume 2: Management, 7th edn. Baltimore, MD: John Hopkins Press.Google Scholar
Sinclair, A. R. E., Fryxell, J., and Caughley, G., eds. (2005). Wildlife Ecology and Management. Oxford: Blackwell Science.Google Scholar
United States Fish and Wildlife Service (2016). Service distributes $1.1 billion to state wildlife agencies to support conservation, outdoor recreation, and job creation. Press Release, March 7. See www.fws.gov/news/ShowNews.cfm?ref=service-distributes-$1.1-billion-to-state-wildlife-agencies-to-support-&_ID=35495. Accessed 15 August 2017.Google Scholar
Unsworth, J. A., Leban, F. A., Leptich, D. J., Garton, E. O., and Zager, P., eds. (1994). Aerial Survey: User’s Manual, 2nd edn. Biose, ID: Idaho Department of Fish and Game.Google Scholar
Unsworth, J. A., Pac, D. F., White, G. C., and Bartmann, R. M. (1999). Mule deer survival in Colorado, Idaho, and Montana. Journal of Wildlife Management, 63, 315–326.CrossRefGoogle Scholar
Unsworth, J. W., Kuck, L., and Garton, E. O. (1990). Elk sightability model validation at the National Bison Range, Montana. Wildlife Society Bulletin, 18, 113–115.Google Scholar
White, M. A., de Beurs, K. M., Didan, K., et al. (2009). Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Global Change Biology, 15, 2335–2359.CrossRefGoogle Scholar
Williams, B. K., Nichols, J. D., and Conroy, M. J., eds. (2002). Analysis and Management of Animal Populations. New York, NY: Academic Press.Google Scholar
Zhang, X. Y., Friedl, M. A., Schaaf, C. B., et al. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84, 471–475.CrossRefGoogle Scholar
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