Evaluation Options for Wildlife Management and Strengthening of Causal Inference

Hone, Jim; Drake, V. A.

doi:10.1093/biosci/biac105

Cited by 4 publications

(5 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Annual updating is already established in the management of mallards and their harvest in North America [ 26 ]. The utility of validating predictions both for statistical and causal inference has been emphasised in wildlife studies and management [ 11 , 12 ]. The present study demonstrates that this can be extended to livestock production and disease control.…”

Section: Discussionmentioning

confidence: 99%

“…Mechanistic modelling focuses on evidence of a mechanism(s) generating a pattern. A mechanism is an important aspect of evidence supporting a cause-and-effect relationship [ 11 , 12 , 14 , 15 ]. The comparison of predictions not using and those using updating of predictions examines if updating of predictions influences their bias and precision.…”

Section: Methodsmentioning

confidence: 99%

“…The validation of predictions in wildlife management studies is encouraged as part of both causal and statistical inference. Many examples have been highlighted in the literature [ 11 , 12 ]. This study distinguishes between causal inference, which focuses on using logic and evaluating the strength of evidence to determine whether a proposed cause has the observed effect, and statistical inference, which focuses on parameter estimation and inferring population parameter values from a random sample.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Are predictions of bovine tuberculosis-infected herds unbiased and precise?

Hone

2023

Epidemiol. Infect.

Self Cite

View full text Add to dashboard Cite

Bovine tuberculosis (bTB) is prevalent among livestock and wildlife in many countries including New Zealand (NZ), a country which aims to eradicate bTB by 2055. This study evaluates predictions related to the numbers of livestock herds with bTB in NZ from 2012 to 2021 inclusive using both statistical and mechanistic (causal) modelling. Additionally, this study made predictions for the numbers of infected herds between 2022 and 2059. This study introduces a new graphical method representing the causal criteria of strength of association, such as R2, and the consistency of predictions, such as mean squared error. Mechanistic modelling predictions were, on average, more frequently (3 of 4) unbiased than statistical modelling predictions (1 of 4). Additionally, power model predictions were, on average, more frequently (3 of 4) unbiased than exponential model predictions (1 of 4). The mechanistic power model, along with annual updating, had the highest R2 and the lowest mean squared error of predictions. It also exhibited the closest approximation to unbiased predictions. Notably, significantly biased predictions were all underestimates. Based on the mechanistic power model, the biological eradication of bTB from New Zealand is predicted to occur after 2055. Disease eradication planning will benefit from annual updating of future predictions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Are predictions of bovine tuberculosis-infected herds unbiased and precise?

Hone

2023

Epidemiol. Infect.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Greater use in wildlife management of explicit causal criteria to complement adaptive management has been proposed to increase the strength of causal inference (Hone et al 2023). So how do studies rank the strength of causal inference?…”

Section: Causalitymentioning

confidence: 99%

“…Wildlife management resources (e.g., money, time, personnel) are limited, so there is a strong incentive to predict outcomes accurately, and to avoid taking actions that have negative effects. The effort-outcome principle in wildlife management was stated as a cause and effect relationship between management efforts (inputs or cause) and outcomes (effects; Hone et al 2017Hone et al , 2023. Conservation scientists use the declining population paradigm (Caughley 1994) to identify the cause(s) of population decline and reduce them to reverse the decline.…”

mentioning

confidence: 99%

Causality and wildlife management

Hone

2023

J Wildl Manag

Self Cite

View full text Add to dashboard Cite

Establishing cause and effect (i.e., causality) is a fundamental aim in science and important for wildlife management, as we need to know the cause of an event if we seek to reproduce, enhance, or diminish it. We review 13 alternative approaches for applying 12 criteria to establish causality. Strength of causal inference is greater when more causal criteria are applied so we propose a scaffolding set of criteria to clearly establish causality. We recommend validating predicted outcomes of wildlife management efforts when predictions are based on a unique mechanism and temporality, especially when manipulative experimental studies are not feasible. We use 3 case studies, namely of lamb predation by feral pigs (Sus scrofa), causes of trends in northern spotted owls (Strix occidentalis caurina), and causes of trends in mallards (Anas platyrhynchos), to illustrate these topics, which are of wide relevance in wildlife management. We recommend greater use of causality and relative strength of causal inference to improve adaptive wildlife management.

show abstract

An integrative paradigm for building causal knowledge

Grace

2024

Ecological Monographs

View full text Add to dashboard Cite

A core aspiration of the ecological sciences is to determine how systems work, which implies the challenge of developing a causal understanding. Causal inference has long been approached from a statistical perspective, which can be limited and restrictive for a variety of reasons. Ecologists and other natural scientists have historically pursued mechanistic knowledge as an alternative approach to causal understanding, though without explicit reference to the requirements of causal statistics. In this paper, I describe the premises of an expanded paradigm for causal studies, the Integrative Causal Investigation Paradigm, that subsumes causal statistics and mechanistic investigation into a multi‐evidence approach. This paradigm is distinct from the one articulated by causal statistics in that it (1) focuses its attention on the long‐term goal of building causal knowledge across multiple studies and (2) recognizes the essential role of mechanistic investigations in establishing a causal understanding. The Integrative Paradigm, consequentially, proposes that there are multiple methodological routes to building causal knowledge and thus represents a pluralistic perspective. This paper begins by describing the crux of the problem faced by causal statistics. To understand this problem, it should be recognized that the word causal has multiple meanings and a variety of evidential standards. An expanded vocabulary is developed so as to reduce ambiguities and clarify critical issues. I further show by example that there is an important ingredient typically omitted from consideration in causal statistics, which is the known information related to the mechanisms underlying relationships being evaluated. To address this issue, I describe a procedure, Causal Knowledge Analysis, that involves an evaluation and compilation of existing evidence indicative of causal content and the features of mechanisms. Causal Knowledge Analysis is applied to three example situations to illustrate the process and its potential for contributing to the development of causal knowledge. The implications of adopting the proposed paradigm and associated procedures are discussed and include the potential for advancing ecology, the potential for clarifying causal methodology, and the potential for contributing to predictive forecasting.

show abstract

Evaluation Options for Wildlife Management and Strengthening of Causal Inference

Cited by 4 publications

References 63 publications

Are predictions of bovine tuberculosis-infected herds unbiased and precise?

Are predictions of bovine tuberculosis-infected herds unbiased and precise?

Causality and wildlife management

An integrative paradigm for building causal knowledge

Contact Info

Product

Resources

About