Co‐creation of individual‐based models by practitioners and modellers to inform environmental decision‐making

Wood, Kevin A.; Stillman, Richard A.; Goss‐Custard, J. D.

doi:10.1111/1365-2664.12419

Cited by 50 publications

(55 citation statements)

References 19 publications

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“…Despite this enhanced capacity, an important exercise often neglected by modellers during model development is practitioner engagement (Fulton et al . ; Wood, Stillman & Goss‐Custard ). Practitioner engagement can benefit conservation decision‐making by: (i) reducing conflicts between modellers and practitioners (Elston et al .…”

Section: Introductionmentioning

confidence: 99%

Cost and feasibility of a barrier to halt the spread of invasive cane toads in arid Australia: incorporating expert knowledge into model‐based decision‐making

Southwell

Tingley

Bode

et al. 2016

Journal of Applied Ecology

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Summary Active engagement with practitioners is a crucial component of model‐based decision‐making in conservation management; it can assist with data acquisition, improve models and help narrow the ‘knowing–doing’ gap. We worked with practitioners of one of the worst invasive species in Australia, the cane toad Rhinella marina, to revise a model that estimates the effectiveness of landscape barriers to contain spread. The original model predicted that the invasion could be contained by managing artificial watering points on pastoral properties, but was initially met with scepticism by practitioners, in part due to a lack of engagement during model development. We held a workshop with practitioners and experts in cane toad biology. Using structured decision‐making, we elicited concerns about the original model, revised its structure, updated relevant input data, added an economic component and found the most cost‐effective location for a barrier across a range of fixed budgets and management scenarios. We then conducted scenario analyses to test the sensitivity of management decisions to model revisions. We found that toad spread could be contained for all of the scenarios tested. Our modelling suggests a barrier could cost $4·5 M (2015 AUD) over 50 years for the most likely landscape scenario. The incorporation of practitioner knowledge into the model was crucial. As well as improving engagement, when we incorporated practitioner concerns (particularly regarding the effects of irrigation and dwellings on toad spread), we found a different location for the optimal barrier compared to a previously published study (Tingley et al. 2013). Synthesis and applications. Through engagement with practitioners, we turned an academic modelling exercise into a decision‐support tool that integrated local information, and considered more realistic scenarios and constraints. Active engagement with practitioners led to productive revisions of a model that estimates the effectiveness of a landscape barrier to contain spread of the invasive cane toad R. marina. Benefits also include greater confidence in model predictions, improving our assessment of the cost and feasibility of containing the spread of toads.

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Section: Introductionmentioning

confidence: 99%

Cost and feasibility of a barrier to halt the spread of invasive cane toads in arid Australia: incorporating expert knowledge into model‐based decision‐making

Southwell

Tingley

Bode

et al. 2016

Journal of Applied Ecology

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“…The model will be most impactful if it is framed to address a real-world conservation problem. We observe that consultation and collaboration in developing a model do not need to rely on co-development (Addison et al, 2013;Wood, Stillman, & Goss-Custard, 2015). This will also help foster a meaningful conversation and engagement with end-users (see next recommendation).…”

Section: Model Designmentioning

confidence: 92%

“…Answers to conservation questions are more likely to result in actions, such as the optimal strategy to allocate resources to achieve conservation objectives (Carwardine et al, 2012;Conroy & Peterson, 2013;McCarthy et al, 2010;Schmolke et al, 2010). Modelers, however, will always benefit from end-users' knowledge of the system, and stakeholders who are consulted throughout the model development phase are more likely to adopt the conclusions drawn from modeling for conservation management (Addison et al, 2013;Parrott, 2017;Schmolke et al, 2010;Wood et al, 2015). Models addressing conservation issues commonly include ecological aspects, but it is not a pre-requisite.…”

Section: Model Designmentioning

confidence: 99%

“…The histograms were obtained by plotting 1,000 random values drawn from a Poisson distribution with a mean of five (left panel) and 1,000 hand-picked values (right panel). This can be achieved if quantitative modelers conceptualize their models with the ultimate aim of informing conservation management (recommendation 1) and communicate with potential stakeholders (recommendation 2) from the outset of the research project (Addison et al, 2013;Parrott, 2017;Wood et al, 2015). com/pablogarciadiaz/0ea50ffd31bb33263572dcfbcd3658ff 2017; Regan, McCarthy, Baxter, Panetta, & Possingham, 2006;Rout, Kirkwood, Sutherland, Murphy, & McCarthy, 2014).…”

Section: Model Inference and Usementioning

confidence: 99%

See 1 more Smart Citation

A concise guide to developing and using quantitative models in conservation management

García‐Díaz

Prowse

Anderson

et al. 2019

Conservation Science and Practice

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Quantitative models are powerful tools for informing conservation management and decision-making. As applied modeling is increasingly used to address conservation problems, guidelines are required to clarify the scope of modeling applications and to facilitate the impact and acceptance of models by practitioners. We identify three key roles for quantitative models in conservation management: (a) to assess the extent of a conservation problem; (b) to provide insights into the dynamics of complex social and ecological systems; and, (c) to evaluate the efficacy of proposed conservation interventions. We describe 10 recommendations to facilitate the acceptance of quantitative models in conservation management, providing a basis for good practice to guide their development and evaluation in conservation applications. We structure these recommendations within four established phases of model construction, enabling their integration within existing workflows: (a) design (two recommendations); (b) specification (two); (c) evaluation (one); and (d) inference (five). Quantitative modeling can support effective conservation management provided that both managers and modelers understand and agree on the place for models in conservation. Our concise review and recommendations will assist conservation managers and modelers to collaborate in the development of quantitative models that are fit-for-purpose, and to trust and use these models appropriately while understanding key drivers of uncertainty. K E Y W O R D Sapplied conservation, ecological models, prediction, projection, simulation model, statistical model, uncertainty GLOSSARY Conservation management: activities conducted with the primary aim of conserving species and systems to achieve maintenance or restoration of biodiversity features. Correlative or correlational model: a model representing noncausative associations between two or more variables.Differential equation: Mathematical equation that relates a function with one or more of its derivatives (i.e., the instantaneous rate of change of a function). For example, an expression for the rate of change (in space or time) of a population. Estimation: using data to approximate, with some degree of uncertainty, the parameter values of a model. Gene drive: a synthetic, self-replicating genetic element that can propagate one or more focal genes through a population.

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“…These are spatio-temporal dynamical models of individuals [1–4] acting by biology-inspired rules [5–7]. An individual can be a plant or an animal or even a group.…”

Section: Introductionmentioning

confidence: 99%

Individual-based modelling of population growth and diffusion in discrete time

et al. 2017

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Individual-based models (IBMs) of human populations capture spatio-temporal dynamics using rules that govern the birth, behavior, and death of individuals. We explore a stochastic IBM of logistic growth-diffusion with constant time steps and independent, simultaneous actions of birth, death, and movement that approaches the Fisher-Kolmogorov model in the continuum limit. This model is well-suited to parallelization on high-performance computers. We explore its emergent properties with analytical approximations and numerical simulations in parameter ranges relevant to human population dynamics and ecology, and reproduce continuous-time results in the limit of small transition probabilities. Our model prediction indicates that the population density and dispersal speed are affected by fluctuations in the number of individuals. The discrete-time model displays novel properties owing to the binomial character of the fluctuations: in certain regimes of the growth model, a decrease in time step size drives the system away from the continuum limit. These effects are especially important at local population sizes of <50 individuals, which largely correspond to group sizes of hunter-gatherers. As an application scenario, we model the late Pleistocene dispersal of Homo sapiens into the Americas, and discuss the agreement of model-based estimates of first-arrival dates with archaeological dates in dependence of IBM model parameter settings.

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Co‐creation of individual‐based models by practitioners and modellers to inform environmental decision‐making

Cited by 50 publications

References 19 publications

Cost and feasibility of a barrier to halt the spread of invasive cane toads in arid Australia: incorporating expert knowledge into model‐based decision‐making

Cost and feasibility of a barrier to halt the spread of invasive cane toads in arid Australia: incorporating expert knowledge into model‐based decision‐making

A concise guide to developing and using quantitative models in conservation management

Individual-based modelling of population growth and diffusion in discrete time

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