Planning for robust reserve networks using uncertainty analysis

Moilanen, Atte; Runge, Michael C.; Elith, Jane; Tyre, Andrew J.; Carmel, Yohay; Fegraus, Eric; Wintle, Brendan A.; Burgman, Mark A.; Ben‐Haim, Yakov

doi:10.1016/j.ecolmodel.2006.07.004

Cited by 98 publications

(67 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A poor strategy is one that produces a poor outcome with certainty. Robustness analysis is needed when estimated benefits are high, but uncertain (Moilanen et al 2006). If known benefits are low but uncertainty is favorably biased, there is scope for positive surprises -this could be the case of poorly surveyed areas.…”

Section: Robust Conservation Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Conservation Prioritization in Data-Poor Areas of the World

Moilanen¹

2012

Nat. Conserv.

Self Cite

View full text Add to dashboard Cite

Systematic conservation planning and spatial conservation prioritization are closely related fields of conservation science. They are concerned with the spatial allocation of conservation resources into actions such as expansion of reserve networks and allocation of habitat restoration, management or maintenance. Decision analytic techniques including optimization are heavily used, with the implication that sufficient data must be available to allow analyses that have relevance for on-the-ground planning. Most commonly, analyses are based on data about species distributions, costs and threats. However, in many parts of the world, including much of the tropics, comprehensive data about species distributions is not available. Are there feasible and robust methods for the allocation of conservation action when data is limited and species-based systematic conservation planning is not feasible? In this work I discuss spatial conservation prioritization and its application when data is limited -making a contrast to the case where ideal data is available.

show abstract

Section: Robust Conservation Strategiesmentioning

confidence: 99%

“…An ideal conservation strategy produces a highly favorable and cost-effective outcome with high reliability (Moilanen et al 2006). A poor strategy is one that produces a poor outcome with certainty.…”

Section: Robust Conservation Strategiesmentioning

confidence: 99%

Spatial Conservation Prioritization in Data-Poor Areas of the World

Moilanen¹

2012

Nat. Conserv.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In a global and dynamic economic climate, however, such an adjournment entails uncertainty and risks of its own: ecosystems may be degraded and species may go extinct. For this reason, and in line with the precautionary principle, conservation decisions must be made and actions must be initiated in the face of uncertainty (Ludwig et al 1993;Moilanen et al 2006). Conservation planners should therefore identify the errors and uncertainties in the planning process and where necessary, evaluate the sensitivity of conservation planning outcomes to these.…”

Section: Introductionmentioning

confidence: 99%

Dealing with Data Uncertainty in Conservation Planning

Wilson¹

2010

Nat. Conserv.

View full text Add to dashboard Cite

Conservation planning analyses often employ data on biodiversity and sometimes vulnerability and these data are generally assumed to be accurate and correct. Here, different ways of exploring uncertainty associated with typical input data used for conservation planning are illustrated. First the uncertainty associated with predicted species distribution data is measured, summarised, and visualised. Second the uncertainty associated with the choice of vulnerability model is evaluated using Bayesian Model Averaging and the implication of this uncertainty on inference about key relationships associated with native forest conversion is assessed. The approaches used to assess uncertainty are applicable to any conservation planning exercise and such assessments will increase confidence in the products developed and reduce the risk that conservation effort is misdirected.

show abstract

“…After all, identifying management decisions that are robust against an uncertainty so extreme, an uncertainty that is unquantifiable due to a complete lack of data, is an onerous task! Suppose then that one were to find reassurance in the following statement, which not only proposes an ''infogap uncertainty analysis,'' but gives it a ringing endorsement (Moilanen et al 2006:23; emphasis added):…”

Section: Introductionmentioning

confidence: 99%

Fooled by local robustness: an applied ecology perspective

Sniedovich

2012

Ecological Applications

View full text Add to dashboard Cite

Abstract. In this short discussion, we point out that it is apparently as easy to be fooled by robustness as it is to be fooled by randomness. Our objective is to bring to the attention of applied ecologists that radius-of-stability robustness models are models of local robustness. As such, these models are utterly unsuitable for the treatment/management of a severe uncertainty characterized by a vast uncertainty space and a likelihood-free quantification of the uncertainty. This observation is particularly pertinent to applications of info-gap decision theory in ecology, conservation biology, and environmental management, where the objective is to identify decisions that are robust against a severe uncertainty of this type.

show abstract

Planning for robust reserve networks using uncertainty analysis

Cited by 98 publications

References 33 publications

Spatial Conservation Prioritization in Data-Poor Areas of the World

Spatial Conservation Prioritization in Data-Poor Areas of the World

Dealing with Data Uncertainty in Conservation Planning

Fooled by local robustness: an applied ecology perspective

Contact Info

Product

Resources

About