Elizabeth A. Freeman

EFFECTS OF DATA QUALITY ON ANALYSIS OF ECOLOGICAL PATTERN USING THE K̂(d) STATISTICAL FUNCTION

The K(d) function is a summary statistic of all plant–plant distances in a mapped area. It offers the potential for detecting both different types and scales of patterns in a single map. Two types of errors occur in maps of individual plants. Data management errors, caused by transcription errors or other mishandling, are large errors and apply to small numbers of plants. Measurement errors, caused by the mapping techniques and equipment, are small errors that apply to all plants. Simulation of known spatial patterns combined with increasing levels of both types of error showed that: (1) data management errors cause the spatial patterns identified by the statistical function K(d) to become less significant but do not cause a shift in scale of the identified patterns; and (2) measurement errors caused the spatial patterns identified by K(d) to become less significant and to shift to larger scales. The effects of measurement errors are inversely proportional to the scale of interaction between plants on the map. Detection of inhibition between points is more sensitive to measurement error than detection of clustering; detection of small clusters is more sensitive than detection of large clusters; and measurement error tends to cause an overestimation of clumping size. For patterns with inhibition, estimating minimum establishment distance is more sensitive to error than the maximum distance at which inhibition affects survival probability. Two examples of tree spatial distributions from the Wind River Canopy Crane Research Facility stem map data set were analyzed using the K(d) function. Clusters of Thuja plicata were detected and were much larger than levels of mapping error identified in the data. Significant inhibition occurs between large (dbh ≥20 cm) trees of all species at a scale much greater than the level of mapping error. However, the minimum distance of significant inhibition (i.e., the distance within which neighbors are never found) was on the order of the mapping error. Accurate identification of inhibition may not be possible using K(d).

Natural variability of benthic species composition in the Delaware Bay

Biological monitoring of aquatic biota is used to assess the impact of changes in the environment. Critical to the development of a sound biological monitoring protocol is the judicious selection of organisms and organism characteristics to be monitored. Accurate interpretations of change necessitate description of the natural variability of the system. We introduce a state-space model for compositional monitoring data, and illustrate how one can incorporate spatial structure and covariates to assess natural variability. The methods are illustrated on benthic survey data from Delaware Bay, and applied to proportional composition at the genus level. The distribution of benthic macroinvertebrates in Delaware Bay depends significantly on salinity. There is residual spatial dependence in the data after accounting for the salinity effect.