Bayesian Methods in Conservation Biology

By P. Wade. 2000

304 citations recorded by the source corpus.

Abstract: Bayesian statistical inference provides an alternate way to analyze data that is likely to be more appropriate to conservation biology problems than traditional statistical methods. I contrast Bayesian techniques with traditional hypothesis‐testing techniques using examples applicable to conservation. I use a trend analysis of two hypothetical populations to illustrate how easy it is to understand Bayesian results, which are given in terms of probability. Bayesian trend analysis indicated that the two populations had very different chances of declining at biologically important rates. For example, the probability that the first population was declining faster than 5% per year was 0.00, compared to a probability of 0.86 for the second population. The Bayesian results appropriately identified which population was of greater conservation concern. The Bayesian results contrast with those obtained with traditional hypothesis testing. Hypothesis testing indicated that the first population, which the Bayesian analysis indicated had no chance of declining at >5% per year, was declining significantly because it was declining at a slow rate and the abundance estimates were precise. Despite the high probability that the second population was experiencing a serious decline, hypothesis testing failed to reject the null hypothesis of no decline because the abundance estimates were imprecise. Finally, I extended the trend analysis to illustrate Bayesian decision theory, which allows for choice between more than two decisions and allows explicit specification of the consequences of various errors. The Bayesian results again differed from the traditional results: the decision analysis led to the conclusion that the first population was declining slowly and the second population was declining rapidly.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/3454f0864dd6a8042516d94d8777340f0fed127f

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In Ecological Modeling

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Citations in the corpus

A TAXONOMY AND TREATMENT OF UNCERTAINTY FOR ECOLOGY AND CONSERVATION BIOLOGY (2002, background): “This assumption is false for species that are cryptic, or dive, or otherwise might go unnoticed even in the direct path of a survey (Taylor and Wade 2000).”
Emerging Issues in Population Viability Analysis (2002, background): “Wade (2000) argues that Bayesian approaches to decision-making, which-define probabilities of potential results, may be more appropriate than traditional statistical approaches to the hypothesis testing about population viability when uncertainty about population trends is great.”
Clarifying the effect of toe clipping on frogs with Bayesian statistics (2004, methodology): “A Bayesian analysis uses a model to combine prior information with new data to produce updated estimates of the parameters (Hilborn & Mangel 1997; Wade 2000; Link et al. 2002).”
Declining amphibian populations: a global phenomenon in conservation biology (2001, background): “Attempts should be made to calculate the confidence in intervals associated with estimates of power, and furthermore there may be considerable merit in using Bayesian approaches to estimate levels of uncertainty (HILBORN & MANGEL, 1997; WADE, 2000).”
Suggestions for presenting the results of data analyses (2001, methodology): “Bayesian analysis allows inference from a posterior distribution that incorporates information from the observed data, the model, and the prior distribution (Schmitt 1969, Ellison 1996, Barnett 1999, Wade 2000).”
Bayesian data analysis. (2010, methodology): “The advantages of Bayesian analysis have been recognized by many scientific disciplines, including archeology (Litton & Buck, 1995), astrophysics (Loredo, 1992), conservation biology (Wade, 2000), ecology (Ellison, 2004), epidemiology (Dunson, 2001), evolutionary biology (Huelsenbeck, Ronquist, Niel”
Statistical significance and biological relevance: A call for a more cautious interpretation of results in ecology (2008, background): “…write papers with the wrong conclusions if we make a wrong interpretation of our statistical analyses, making the whole costly process pretty much useless, not to say counter-productive, if decision-making is affected by our wrong conclusions (Ellison, 1996; Wade, 2000; Hobbs and Hilborn, 2006).”
A critical review of multi-criteria decision making methods with special reference to forest management and planning (2009, methodology): “More applications of Bayesian analysis can be found in Alho and Kangas (1997), Wade (2000), Kangas et al. (2000a,b), Borsuk et al. (2000) and Prato (2001).”
A theoretical analysis of competitive exclusion in a Trans‐Himalayan large‐herbivore assemblage (2002, methodology): “We are aware of the increasing use of Bayesian or likelihood approaches in non-experimental studies (Wade, 2000), where the probability of the value of a parameter is estimated given the data, in favour of estimating the probability of the observed data given a specific value of the parameter (such…”
The social dynamics of southern resident killer whales and conservation implications for this endangered population (2009, background): “The Bayesian framework enabled uncertainty about both the number and the composition of social clusters to be analysed and it communicated these uncertainties as direct probability statements (e.g. Wade 2000).”
Experimental evidence of homing to site of incubation by mature sockeye salmon, Oncorhynchus nerka (2006, result): “Bayesian methods are ideal for this type of analysis, and the results can be easily interpreted in terms of relative probability, unlike conventional confidence intervals or P values (Hilborn & Mangel 1997; Wade 2000).”