Figures
Figure 1.1 |
Taxonomy of 230 barriers and enablers to using scientific evidence in conservation management and planning decisions. |
|
Figure 1.2 |
The role of evidence in evidence-based conservation, where values incorporate ethical, social, political and economic concerns. |
|
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. |
|
Figure 1.4 |
The Policy Hexagon. |
|
Figure 1.5 |
How the book sections and chapters (numbered) link together. |
|
Figure 2.1 |
Assessing the weight of evidence according to information reliability, source reliability, and relevance (ISR). |
|
Figure 2.2 |
The links between general and specific information for conservation actions. |
|
Figure 2.3 |
A means of visualising the evidence behind an assumption. |
|
Figure 2.4 |
A summary of the main six broad types of study designs. |
|
Figure 3.1 |
Typical stages of a systematic mapping process. |
|
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). |
|
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. |
|
Figure 3.4 |
The process of adjusting analyses using Metadataset. |
|
Figure 4.1 |
Cuboids of different strengths of evidence can range in weight from 0 (no weight) to 125 (53). |
|
Figure 4.2 |
A means of visualising the balance of evidence behind an assumption. |
|
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). |
|
Figure 4.4 |
A range of possible outcomes of ziggurat plots. |
|
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. |
|
Figure 4.6 |
An example of an argument map to decide whether or not to introduce a plant to new locations. |
|
Figure 4.7 |
A mind map of possible means of managing lowland grassland habitat to improve the common snipe Gallinago gallinago population. |
|
Figure 4.8 |
A theory of change pathway showing analytical questions and assumptions. |
|
Figure 4.9 |
A causal diagram of the relationship between the three nodes in the system. |
|
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. |
|
Figure 4.11 |
The Bayesian network shown in Figure 4.10 but with the lawn set to wet (as observed). |
|
Figure 4.12 |
Inference diagram of the factors influencing pot-fishing activity along the Northumbrian Coast, UK. |
|
Figure 4.13 |
Bayesian networks are used to contextualise evidence for decision making. |
|
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. |
|
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. |
|
Figure 6.2 |
Stakeholder mapping classifying them into four groups depending on their interest and power. |
|
Figure 7.1 |
Scenarios to support decision making at different phases of the policy phase. |
|
Figure 7.2 |
Excerpt from a situation model on protecting a seabird colony from invasive rodents. |
|
Figure 7.3 |
Example of a theory of change pathway diagram. |
|
Figure 7.4 |
Examples of analytical questions and assumptions related to a situation model. |
|
Figure 7.5 |
Examples of analytical questions and assumptions related to a theory of change. |
|
Figure 8.1 |
The stages in a structured decision process are represented by coloured circles, alongside text suggesting questions to consider during the process. |
|
Figure 8.2 |
An illustration of how 10,000 decisions may be resolved. |
|
Figure 8.3 |
A values hierarchy for a catchment management example. |
|
Figure 8.4 |
A generalised decision tree to consider options for controlling the invasive plant Crassula helmsii. |
|
Figure 8.5 |
Steps in model development for decision making, summarising (A) processes typically deployed in most model developments, and (B) emerging approaches. |
|
Figure 9.1 |
The percentage of ASN competitive funding awarded to projects with moderate or strong levels of funding. |
|
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. |
|
Figure 9.3 |
Decision tree for using evidence in assessing a potential conservation action. |
|
Figure 10.1 |
The flow of evidence, from appropriately designed data collection to a wider evidence base to inform internal and external decision making. |
|
Figure 10.2 |
Options for collecting data along the causal chain. |
|
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. |
|
Figure 11.1 |
Monitoring a test of adding hay (right) against a control (left) of no hay added. |
|
Figure 11.2 |
The number of interventions carried out by Kent Wildlife Trust classified by categories of effectiveness as then summarised by Conservation Evidence. |
|
Figure 12.1 |
The World Health Organization checklist for surgeries (adapted for England and Wales). |