Deme -
A group of individuals more genetically similar to each other than to other individuals (Wells and Richmond 1995).
A randomly interbreeding local population (Meffe and Carroll 1994).
Population -
A group of organisms of the same species occupying a particular space at a particular time (Krebs 1972).
A group of conspecific individuals that is demographically, genetically, or spatially disjunct from other groups or individuals (Wells and Richmond 1995).
- A collection of demes with strong connections between adjacent demes.
Metapopulation -
A set of spatially disjunct populations, among which there is some immigration (Wells and Richmond 1995).
A population of several subpopulations in scattered habitat patches separated from each other by nonhabitat (Levin 1970).
- A collection of populations.
Subspecies population -
A collection of metapopulations in a region.
Very rare dispersals maintain genetic similarity.
Demographic independence may be nearly complete.
Occupied habitat patches may be separated by large areas of nonhabitat.
Species population -
The collection of “subspecies” encompassing the entire distribution of the species.
Defines the entire geographic range of the species.
May encompass substantial differences in phenotypes and genotypes.
Spatial distributions of individuals, habitats, and populations profoundly affect all of the characteristics, responses, and dynamics of a metapopulation.
Describing these spatial distributions is an essential step in understanding the dynamics of populations and designing studies of these populations.
Delimiting the subunits of a metapopulation is an essential step in making statements and predictions about the population components (see Lecture Notes).
Statistical population
The sampling universe
All of the individual observations about which inferences are to be made.
Example: If our focus was wolverines in the Northern Rocky Mountains, then our statistical population would be all wolverines or wolverine habitat in the Northern Rocky Mountains.
Note that the statistical population which is sampled is governed by your specific hypotheses and the data you gather to test them; for example, measurement of survival of rainbow trout in a specific lake cannot be used to describe survival of rainbow trout in all lakes. The statistical population from which survival is determined is that of rainbow trout in that lake only.
Sample
Collection of individual observations selected by a specified procedure.
Example: Geographic areas of potential wolverine habitat selected by simple random sampling.
| To make generalized inferences about wildlife populations, hypothesis tests should account for all potentially confounding variables, and samples from that population need to be taken with spatial and temporal replication. |
A. Historic locations
B. Projecting from mapped habitat
C. Sampling for presence/absence
D. Other types of data?
see O'Neil et al. (2005).
a. input,
b. data storage and retrieval,
c. manipulation and analysis, and
d. output.
What exists at a particular site or location?
Where are certain conditions met?
What changes have occurred over time and where have those changes occurred?
What are the social, economic, or environmental impacts of a particular change in land use.
What will happen if the existing land use for a particular site is altered to another type of use? (i.e., “What if?” questions)
1. Type of data:
Spatial data - represented by points, lines, or polygons.
Attribute data - data describing the feature
2. Format of spatial data:
Raster - A grid is used to represent the study area. Location of features within the grid is depicted by values in the cells overlaying the feature. Cell size can vary tremendously depending on the size of the study area and objectives for the GIS.
Vector - Geographic features are depicted by coordinates of points, lines, and polygons. Points represent small features such as nest locations. Lines represent linear features such as roads and streams. Polygons represent features such as forests, wetlands, and ecoregions.
Advantages/disadvantages of raster vs. vector (see Koeln et al. 1994:546)
Most modern GIS systems handle both raster and vector data but usually are designed for one data type.
3. Data layers or overlays.
Maintenance and analysis of spatial data
Maintenance and analysis of attribute data
Integrated analysis of spatial and attribute data
Output formatting.
Information generated from the GIS and resulting decisions made with that information can be accurate only if the initial data are accurate.
The ability to change map scales and to overlay maps can be deceiving; the user must be aware of the imprecision inherent in all cartography and the ways errors compound when map scales are changed or when maps are merged.
Mapping, monitoring, and/or evaluating habitat
Analyzing radiotelemetry data
Protection of biological diversity
Predicting wildlife densities
Modeling spatial distributions
Examining cumulative impacts of habitat loss or alteration
Other?
GIS and the Mallard Population Model (see Koeln et al. 1994)
GAP Analysis (see Scott et al. 1993)
Bailey, R. G. 1996. Ecosystem geography. Springer-Verlag Inc., New York, New York. 204pp.
Gallant, A. L., T. R. Whittier, D. P. Larsen, J. M. Omernik, and R. M. Hughes. 1989. Regionalization as a tool for managing environmental resources. U.S. Environmental Protection Agency, Evironmental Research Laboratory, EPA/600/3-89/060. 152pp.
Koeln, G. T., L. M. Cowardin, and L. L. Strong. 1994. Geographic information systems. Pages 540-566 in T. A. Bookhout, editor. Research and management techniques for wildlife and habitats. Fifth edition. The Wildlife Society, Bethesda, Maryland.
Meffe, G. K., and C. R. Carroll. 1994. Principles of conservation biology. Sinauer Associates, Inc., Boston, Massachusetts. 600pp.
O'Neil, T. A., P. Bettinger, B. G. Marcot, B. W. Luscombe, G. T. Koeln, H. J. Bruner, C. Barrett, J. A. Pollock, and S. Bernatas. 2005. Applications of spatial technologies in wildlife biology. Pages 418-464 in C. E. Braun, editor. Techniques for wildlife investigations and management. Sixth edition. The Wildlife Society, Bethesda, Maryland.
Scott, J. M., F. Davis, B. Csuti, F. Noss, B. Butterfield, C. Groves, H. Anderson, S. Caico, F. D'erchia, T. C. Edwards, Jr., J. Ulliman, and R. G. Wright. 1993. GAP analysis: a geographic approach to protection of biological diversity. Wildlife Monographs 123.
Wells, J. V., and M. E. Richmond. 1995. Populations, metapopulations, and species populations: what are they and who should care? Wildlife Society Bulletin 23:458-462.
Revised: 02 September 2011