Analysis and Management of Animal Populations by Byron K. Williams

By Byron K. Williams

Analysis and administration of Animal Populations bargains with the procedures keen on making proficient judgements in regards to the administration of animal populations. It covers the modeling of inhabitants responses to administration activities, the estimation of amounts wanted within the modeling attempt, and the appliance of those estimates and types to the advance of sound administration judgements. The publication synthesizes and integrates in one quantity the tools linked to those issues, as they follow to ecological overview and conservation of animal populations.

  • Integrates inhabitants modeling, parameter estimation and decision-theoretic techniques to administration in one, cohesive framework
  • Provides authoritative, state of the art descriptions of quantitative ways to modeling, estimation and decision-making
  • Emphasizes the function of mathematical modeling within the behavior of technology and management
  • Utilizes a unifying organic context, constant mathematical notation, and various organic examples

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Extra resources for Analysis and Management of Animal Populations

Example text

In many circumstances nonhunting mortality rate after the hunting season should be positively related to population size at the end of the hunting season. The additive mortality hypothesis leads to a prediction that there is no relationship between nonhunting mortality rate and population size. These predictions differ considerably in the degree to which they represent explanatory causes of population dynamics, and the difficulty with which data can be collected and used informatively for testing (Conroy and Krementz, 1990).

Under these conditions the hypothesis then is accepted as provisionally true, in that we view it as our best approximation of reality (subject, of course, to subsequent investigation and possible refinement). 2. 1. Single-Hypothesis Approach This approach frequently is associated with the writing of Popper (1959, 1963, 1972) and the influential paper by Platt (1964) on strong inference. 3: 1. Develop or identify a hypothesis (typically from existing theory). 2. With the help of the associated model, deduce testable predictions.

H In these formulations the first premise asserts that prediction P is a consequent of an amended theory, as described above. The second essentially asserts that P is disconfirmed by observation (argument 1) or that P is confirmed by observation (argument 2). The third premise asserts the truth of theory {T}, and the fourth represents the observed data O. A horizontal line separates the argument's premises and evidence from its conclusion, which is stated on the last line. , the observation indicates that the prediction is incorrect).

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