Adaptive Management

Environmental variability and change, changes in social values, and the discovery of new information require that AMLs be adaptable. Perhaps the most fundamental approach in this regard is adaptive management (Holling, 1978; Williams et al., 2009). Adaptive management can be used in a variety of social decision-making settings (see Chapter 8). Herrick et al. (2012) defined adaptive management as an iterative decision-making process that incorporates development of management objectives, actions to address the objectives, monitoring of results, and repeated adaptation of management until desired results are achieved. A key tenet of adaptive management that is relevant to managing free-ranging horses and burros is the treatment of management actions as testable hypotheses. In turn, maximizing long-term knowledge of the system and thereby improving management (balanced with achieving optimal short-term outcomes, given current knowledge; Stankey and Allan, 2009) hinges on several fundamental tenets of research and monitoring design. Those tenets include use of control plots (against which to evaluate the effects of a given management “treatment,” such as erecting exclosures, administering immunocontraception broadly in a population, or removing or transferring animals from a population); use of replication to increase confidence that results are generalizable rather than anomalous; and controlling for variability (such as that due to annual differences in precipitation and thus productivity), for example, through Before-After Control-Impact designs (Underwood, 1992, 1994). Also essential for adaptive management specifically and for applied ecology generally is the explicit incorporation of uncertainty (such as the use of 95-percent confidence intervals, standard errors or standard deviations, and probability density functions) into estimated measures (such as herd size, utilization rate in a site or HMA, and average penetration resistance in a landscape).

Several other approaches to analysis and interpretation of management actions and monitoring data can improve confidence in the results. First, if there is interest in understanding whether or how a particular factor (e.g., average site growing-season precipitation) affects the degree of ecosystem alteration caused by a given density of free-ranging horses and burros, ecosystem attributes mentioned above should be measured at numerous sites with comparable horse and burro density across a broad range of that factor (gradient analyses; Austin, 1985; Gosz, 1992). Such approaches provide quantitative information on the major driving variables, permit the generation of information for extrapolating between sites and across scales, and begin to address mechanistic explanations of phenomena relevant to management (Gosz, 1992). Although ideally other important factors would remain constant in all sites along the gradient, that is rarely the case; for example, soils may differ markedly along the gradient. In those situations, explicitly accounting for this key factor (e.g., soils) can be approached in a manner comparable with complete factorial or blocked designs (e.g., Underwood, 1994, 1997; Sokal and Rohlf, 2012). A related example might be the use of landscape-scale analyses to identify portions of the landscape most likely to be early-warning indicators of deterioration of landscape condition, such as areas of heavy use.

Numerous relatively recent advances in ecological monitoring that can further increase confidence in results are relevant and noteworthy for the Wild Horse and Burro Program. For example, if a particular question is being addressed in terms of testing of the null hypothesis and the null hypothesis fails to be rejected (that is, no effect of a management action or “treatment” was found), a post hoc power analysis can be performed to assess how likely the effort was to detect an existing effect (what power the effort had) given the sample sizes used for and the variability among replicates in the various groups. Over time, however, a priori power analyses have generally come to be regarded more favorably than post



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