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Different Techniques Biologist Use to Study Mammals in the Field

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Zoologists and wildlife biologists study animals and other wildlife and how they interact with their ecosystems

They study the physical characteristics of animals, animal behaviors, and the impacts humans have on wildlife and natural habitats. Zoologists and wildlife biologists perform a variety of scientific tests and experiments. For example, they take blood samples from animals to assess their nutrition levels, check animals for disease and parasites, and tag animals in order to track them. Although the roles and abilities of zoologists and wildlife biologists often overlap, zoologists typically conduct scientific investigations and basic research on particular types of animals, such as birds or amphibians, whereas wildlife biologists are more likely to study specific ecosystems or animal populations, such as a particular at-risk species. Wildlife biologists also do applied work, such as the conservation and management of wildlife populations.

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Counting animals to estimate their population sizes is often essential for their management and conservation. Since practitioners frequently rely on indirect observations of animals, it is important to better understand the relationship between such indirect indices and animal abundance

The Formozov-Malyshev-Pereleshin (FMP) formula provides a theoretical foundation for understanding the relationship between animal track counts and the true density of species. Although this analytical method potentially has universal applicability wherever animals are readily detectable by their tracks, it has long been unique to Russia and remains widely underappreciated. In this paper, we provide a test of the FMP formula by isolating the influence of animal travel path tortuosity (i.e., convolutedness) on track counts. We employed simulations using virtual and empirical data, in addition to a field test comparing FMP estimates with independent estimates from line transect distance sampling. We verify that track counts (total intersections between animals and transects) are determined entirely by density and daily movement distances. Hence, the FMP estimator is theoretically robust against potential biases from specific shapes or patterns of animal movement paths if transects are randomly situated with respect to those movements (i.e., the transects do not influence animals’ movements). However, detectability (the detection probability of individual animals) is not determined simply by daily travel distance but also by tortuosity, so ensuring that all intersections with transects are counted regardless of the number of individual animals that made them becomes critical for an accurate density estimate. Additionally, although tortuosity has no bearing on mean track encounter rates, it does affect encounter rate variance and therefore estimate precision.

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For some population studies, simply determining whether a species is present in an area is sufficient for conducting the planned data analysis. For example, biologists attempting to conserve a threatened salamander may need to monitor the extent of the species’ range and degree of population fragmentation on a land ownership. One hypothetical approach is to map all streams in which the salamander is known to be present, as well as additional streams that may qualify as the habitat type for the species in the region. To monitor changes in salamander distribution, data collection could consist of a survey along randomly selected reaches in each of the streams to determine if at least one individual (or some alternative characteristic such as egg mass) is present. Using only a list that includes the stream reach (i.e., the unique identifier), the survey year, and an occupancy indicator variable, a biologist could prepare a time series of maps displaying all of the streams by year and distinguish the subset of streams that were known to be occupied by the salamander. Such an approach could support a qualitative assessment of changes in the species distribution pattern, thereby attaining the program’ objectives, and generate new hypotheses as to the cause of the observed changes

It is far easier to determine if there is at least one individual of the target species on a sampling unit than it is to count all of the individuals. Determining with confidence that a species is not present on a sampling unit also requires more intensive sampling than collecting count or frequency data because it is so difficult to dismiss the possibility that an individual eluded detection.Studies of habitat relationships or cause-and-effect responses require coordinated sampling of the target population and environmental measurements or stressors to which the population may respond. Data collection efforts tend to be complex, requiring multiple sampling protocols for the target population, study site attributes, and landscape pattern metrics. The funding required to conduct research studies typically limits their application to species or populations in greatest need of management planning such as those listed as threatened or endangered. Manipulative studies are often carried out to generate the necessary data, but when these focus on a threatened species, ethical questions regarding the conduct of the experiment placing the species at even great risk, at least locally, often emerge. Hence it is often monitoring of both environmental conditions and aspects of population density or fitness that are used to assess associations in trends between population parameters and environmental parameters.Clearly the scope of inference will influence the type of sampling technique used. Breeding bird atlas techniques commonly use large grids placed over entire states to assess the occurrence of species in a grid cell. Such approaches and those of the Breeding Bird Survey (Sauer et al. 2008) can be conducted through volunteer efforts. On the other hand, monitoring the trends in reproductive rates of northern spotted owls, northern goshawks, or grizzly bears over their geographic ranges requires a huge budget to collect the level of population data over large areas needed to understand trends. Great care must be taken when deciding what technique to use because both budgets and sample size requirements enter into logistics. Indeed, it is often the tradeoff between more detailed data and the cost of producing those data that drive decisions regarding monitoring designs for species at risk.The array of techniques available to sample animals is vast and summarized elsewhere in techniques manuals (e.g., Bookhout 1994).

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Summing up, during the planning phase it is important to contact other experts and/or projectcoordinators to gain access to information about the region, habitat and fauna.Information about on-site logistics, e.g. accessibility of field sites (foot/vehicle),storage options for vouchers (esp. in the tropics), lab space (if needed),availability of drinking water, medical care, maps, etc. are helpful for planning.Please inform and prepare yourself also about human health concerns anddisease risks

It might also be useful to coordinate your survey with otherscientists, for example with botanists, who would provide habitat descriptions.

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Bookhout, T.A. (ed). 1994. Research and management techniques for wildlife and habitats. The Wildlife Society, Bethesda, Maryland. 740 pp.

Bryce, S.A., R.M. Hughes, and P.R. Kaufman. 2002. Development of a bird integrity index: using bird assemblages as indicators of riparian condition. Environmental Management 30:294–310.

Rosenstock, S.S., D.R. Anderson, K.M. Giesen, T. Leukering, and M.F. Carter. 2002. Landbird counting techniques: Current practices and an alternative. Auk 119:46–53.

Rudran, R, T.H. Kunz, S.C. Jarman, and A.P. Smith. 1996. Observational techniques for nonvolant mammals. Pages 81–114 In D.E. Wilson, F.R. Cole, J.D. Nichols, R. Rudran, and M.S. Foster, Editors. Measuring and monitoring biological diversity. Standard methods for mammals. Smithsonian Institution, Washington, DC, USA.

Sauer, J.R., J.E. Hines, and J. Fallon. 2008. The North American Breeding Bird Survey, Results and Analysis 1966 – 2007. Version 5.15.2008. USGS Patuxent Wildlife Research Center, Laurel, MD

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