Figures

Figure 1.1

Taxonomy of 230 barriers and enablers to using scientific evidence in conservation management and planning decisions.

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Figure 1.2

The role of evidence in evidence-based conservation, where values incorporate ethical, social, political and economic concerns.

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Figure 1.3

How the various evidence crises are likely to create demands for change that the enablers described in this book could help deliver, resulting in a series of improvements and a markedly better planet.

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Figure 1.4

The Policy Hexagon.

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Figure 1.5

How the book sections and chapters (numbered) link together.

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Figure 2.1

Assessing the weight of evidence according to information reliability, source reliability, and relevance (ISR).

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Figure 2.2

The links between general and specific information for conservation actions.

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Figure 2.3

A means of visualising the evidence behind an assumption.

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Figure 2.4

A summary of the main six broad types of study designs.

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Figure 3.1

Typical stages of a systematic mapping process.

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Figure 3.2

Categories of effectiveness based on a combination of effectiveness (the extent of the benefit and harm) and certainty (the strength of the evidence).

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Figure 3.3

Effects of retention cuts (mean effect size 95% CI) on species richness and abundance of forest, generalist and open-habitat species when using (a) clearcut, or (b) unharvested forest as the control.

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Figure 3.4

The process of adjusting analyses using Metadataset.

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Figure 4.1

Cuboids of different strengths of evidence can range in weight from 0 (no weight) to 125 (5³).

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Figure 4.2

A means of visualising the balance of evidence behind an assumption.

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Figure 4.3

A ziggurat plot in which each study is a horizontal bar whose width is the information reliability, source reliability, and relevance (ISR) score (up to 125).

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Figure 4.4

A range of possible outcomes of ziggurat plots.

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Figure 4.5

A weighted histogram plot in which each piece of evidence is represented by a vertical bar, whose height is the information reliability, source reliability, and relevance (ISR) score.

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Figure 4.6

An example of an argument map to decide whether or not to introduce a plant to new locations.

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Figure 4.7

A mind map of possible means of managing lowland grassland habitat to improve the common snipe Gallinago gallinago population.

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Figure 4.8

A theory of change pathway showing analytical questions and assumptions.

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Figure 4.9

A causal diagram of the relationship between the three nodes in the system.

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Figure 4.10

A Bayesian network of the lawn and sprinkler system shown in Figure 4.9 including the expected weather conditions, with the resulting consequences for sprinkler use.

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Figure 4.11

The Bayesian network shown in Figure 4.10 but with the lawn set to wet (as observed).

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Figure 4.12

Inference diagram of the factors influencing pot-fishing activity along the Northumbrian Coast, UK.

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Figure 4.13

Bayesian networks are used to contextualise evidence for decision making.

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Figure 5.1

Example feedback plot of anonymised expert estimates of the number of individuals of a given species present at three different sites, elicited using the IDEA protocol.

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Figure 6.1

Appropriate type of community involvement according to the likely impact caused by the proposal and the extent of community engagement in the site.

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Figure 6.2

Stakeholder mapping classifying them into four groups depending on their interest and power.

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Figure 7.1

Scenarios to support decision making at different phases of the policy phase.

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Figure 7.2

Excerpt from a situation model on protecting a seabird colony from invasive rodents.

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Figure 7.3

Example of a theory of change pathway diagram.

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Figure 7.4

Examples of analytical questions and assumptions related to a situation model.

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Figure 7.5

Examples of analytical questions and assumptions related to a theory of change.

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Figure 8.1

The stages in a structured decision process are represented by coloured circles, alongside text suggesting questions to consider during the process.

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Figure 8.2

An illustration of how 10,000 decisions may be resolved.

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Figure 8.3

A values hierarchy for a catchment management example.

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Figure 8.4

A generalised decision tree to consider options for controlling the invasive plant Crassula helmsii.

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Figure 8.5

Steps in model development for decision making, summarising (A) processes typically deployed in most model developments, and (B) emerging approaches.

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Figure 9.1

The percentage of ASN competitive funding awarded to projects with moderate or strong levels of funding.

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Figure 9.2

A ziggurat plot showing strong support for the assumption that organisations use flexible funding to invest in organisational development and/or maturity.

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Figure 9.3

Decision tree for using evidence in assessing a potential conservation action.

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Figure 10.1

The flow of evidence, from appropriately designed data collection to a wider evidence base to inform internal and external decision making.

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Figure 10.2

Options for collecting data along the causal chain.

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Figure 10.3

How a combination of skills, the need for results, and the existence of opportunities determines whether an experiment can usefully be included in conservation management.

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Figure 11.1

Monitoring a test of adding hay (right) against a control (left) of no hay added.

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Figure 11.2

The number of interventions carried out by Kent Wildlife Trust classified by categories of effectiveness as then summarised by Conservation Evidence.

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Figure 12.1

The World Health Organization checklist for surgeries (adapted for England and Wales).

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