Wildlife biologists use knowledge about wildlife‐habitat relationships to create habitat models to predict species occurrence across a landscape. Researchers attribute limitations in predictive ability of a habitat model to data deficiencies, missing parameters, error introduced by specifications of the statistical model, and natural variation. Few wildlife biologists, however, have incorporated intra‐ and interspecific interactions (e.g., conspecific attraction, competition, predator‐prey relationships) to increase predictive accuracy of habitat models. Based on our literature review and preliminary data analysis, conspecific attraction can be a primary factor influencing habitat selection in wildlife. Conspecific attraction can lead to clustered distributions of wildlife within available habitat, reducing the predictive ability of habitat models based on vegetative and geographic parameters alone. We suggest wildlife biologists consider incorporating a parameter in habitat models for the clustered distribution of individuals within available habitat and investigate the mechanisms leading to clustered distributions of species, especially conspecific attraction.
Human intrusion, the mere presence of people in the environment, has become a dominant form of disturbance in many landscapes. Some forms of intrusion from recreationists and other groups occur repeatedly and can seriously alter avian reproduction, survival, and habitat use. Accordingly, repeated intrusion has the potential to cause impacts that accumulate through time and that are manifested as progressive declines in avian richness and abundance. From 1989 to 1993, we experimentally assessed whether or not temporally cumulative impacts occurred in Wyoming bird communities as a result of repeated intrusion by solitary hikers; the intrusions lasted 1‐2 h each week during 10 consecutive weeks of each year's breeding season. We tested a priori hypotheses about declines in overall richness and abundance, relative richness and abundance for sets of common and uncommon species, richness and abundance for six guilds, and separate abundances of four common species. Relative richness and abundance for the set of common species were the only metrices to exhibit significant declines between years during the 5‐yr period. The declines in these variables, however, were not cumulative. At a statistical power level of 0,85, minimum detectable differences for many variables were small enough to have allowed easy detection of substantive declines, had any occurred. The yearly effects we detected for some richness and abundance variables may not have led to cumulative declines because individuals displaced one year may have been replaced in subsequent years, and some individuals each year may have habituated to or learned to tolerate the intrusions. For the avian communities and intrusion levels we studied, managers should focus on trying to preclude or ameliorate short‐term impacts. Attempts to identify the types and intensities of intrusion that actually cause cumulative declines in richness and abundance should continue. Data about intrusions that do not generate cumulative declines, such as those presented here, are just as important as data about intrusions that do cause cumulative declines; managers need both to define the scope of intrusion disturbances that can lead to cumulative impacts in avian communities. Information about the cumulative effects of intrusion should be used by conservation biologists, wildlife managers, and land‐use planners to decide whether or how to control intrusion.
Wildland recreation that does not involve animal harvests (non‐consumptive recreation) often influences various components of natural systems, including soils, water, air, soundscapes, vegetation, and wildlife. The effects of non‐consumptive recreation on wildlife have typically been assessed at spatial scales that are not only much smaller than the overall distributions of this disturbance but also much smaller than the areas that species use during a season or year. This disparity in scales has prevented effective assessment and management of broad‐scale recreation disturbance for many species, especially wildlife. We applied three software systems (ArcGIS, FRAGSTATS, and Conefor) to demonstrate how metrics commonly measured by landscape ecologists can be used to quantify broad‐scale patterns of non‐consumptive recreation. Analysts can employ such metrics to develop predictive models of how recreation disturbance – by itself and in additive or interactive combinations with other landscape characteristics – may affect wildlife responses across large areas. In turn, these models can inform decision making in broad‐scale recreation management.
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