A stochastic approach to marine reserve design: Incorporating data uncertainty

Beech, Talia; Dowd, Michael K.; Field, Chris; Hatcher, Bruce G.; Andréfouët, Serge

doi:10.1016/j.ecoinf.2008.09.001

Cited by 21 publications

(10 citation statements)

References 28 publications

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“…Conservation planning methods that include uncertainty associated with habitat-mapping accuracy can therefore increase reliability and robustness of final conservation solutions by helping us achieve conservation goals efficiently (Moilanen et al 2006). Despite this, a paucity of research exists that accounts for uncertainty in habitat distributions in reserve design (but see Beech et al 2008). This may be because habitat mapping accuracy information is often not readily available or accessible to conservation planners.…”

Section: Introductionmentioning

confidence: 99%

Incorporating uncertainty associated with habitat data in marine reserve design

Tulloch

Possingham

Jupiter

et al. 2013

Biological Conservation

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One of the most pervasive forms of uncertainty in data used to make conservation decisions is error associated with mapping of conservation features. Whilst conservation planners should consider uncertainty associated with ecological data to make informed decisions, mapping error is rarely, if ever, accommodated in the planning process. Here, we develop a spatial conservation prioritization approach that accounts for the uncertainty inherent in coral reef habitat maps and apply it in the Kubulau District fisheries management area, Fiji. We use accuracy information describing the probability of occurrence of each habitat type, derived from remote sensing data validated by field surveys, to design a marine reserve network that has a high probability of protecting a fixed percentage (10-90%) of every habitat type. We compare the outcomes of our approach to those of standard reserve design approaches, where habitat-mapping errors are not known or ignored. We show that the locations of priority areas change between the standard and probabilistic approaches, with errors of omission and commission likely to occur if reserve design does not accommodate mapping accuracy. Although consideration of habitat mapping accuracy leads to bigger reserve networks, they are unlikely to miss habitat conservation targets. We explore the trade-off between conservation feature representation and reserve network area, with smaller reserve networks possible if we give up on trying to meet targets for habitats mapped with a low accuracy. The approach can be used with any habitat type at any scale to inform more robust and defensible conservation decisions in marine or terrestrial environments.

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Section: Introductionmentioning

confidence: 99%

Incorporating uncertainty associated with habitat data in marine reserve design

Tulloch

Possingham

Jupiter

et al. 2013

Biological Conservation

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“…Planners often have to use remotely sensed habitat maps to design reserve systems because species distribution data are scarce (Mumby & Edwards 2002), but these maps can be highly inaccurate. Despite this, only limited work has been done to explore issues of habitat mapping errors in marine conservation planning (Beech et al 2008, Tulloch et al 2013. Our findings demonstrate possible inadequacies in, and risks of, spatial prioritization analyses that do not consider habitat map accuracy, particularly when using remotely-sensed habitat maps with high thematic complexity, where detection and misclassification errors are more likely than with coarse-classifications (Roelfsema & Phinn 2013).…”

Section: Discussionmentioning

confidence: 88%

“…One recent example is the Great Barrier Reef Marine Park Rezoning (Fernandes et al 2005), which used bioregional maps and assumed these were representative of a range of coral reef habitats without any accuracy information. Management decisions can be prone to errors of omission (when a feature is mistakenly thought to be absent) or commission (when a feature is mistakenly thought to be present) if inaccurate spatial data are used (Rondinini et al 2006, Beech et al 2008.…”

Section: Introductionmentioning

confidence: 99%

Trade-offs between data resolution, accuracy, and cost when choosing information to plan reserves for coral reef ecosystems

Tulloch

Klein

Jupiter

et al. 2017

Journal of Environmental Management

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Conservation planners must reconcile trade-offs associated with using biodiversity data of differing qualities to make decisions. Coarse habitat classifications are commonly used as surrogates to design marine reserve networks when fine-scale biodiversity data are incomplete or unavailable. Although finely-classified habitat maps provide more detail, they may have more misclassification errors, a common problem when remotely-sensed imagery is used. Despite these issues, planners rarely consider the effects of errors when choosing data for spatially explicit conservation prioritizations. Here we evaluate trade-offs between accuracy and resolution of hierarchical coral reef habitat data (geomorphology and benthic substrate) derived from remote sensing, in spatial planning for Kubulau District, Fiji. For both, we use accuracy information describing the probability that a mapped habitat classification is correct to design marine reserve networks that achieve habitat conservation targets, and demonstrate inadequacies of using habitat maps without accuracy data. We show that using more detailed habitat information ensures better representation of biogenic habitats (i.e. coral and seagrass), but leads to larger and more costly reserves, because these data have more misclassification errors, and are also more expensive to obtain. Reduced impacts on fishers are possible using coarsely-classified data, which are also more cost-effective for planning reserves if we account for data collection costs, but using these data may under-represent reef habitats that are important for fisheries and biodiversity, due to the maps low thematic resolution. Finally, we show that explicitly accounting for accuracy information in decisions maximizes the chance of successful conservation outcomes by reducing the risk of missing conservation representation targets, particularly when using finely classified data.

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“…Lautenbach et al, 2012), thus hiding spatial variation and detail. Only a few studies have investigated the impact of uncertain or varying input data on the actual spatial result of spatial optimisation (Aerts et al, 2003;Murray, 2003;Beech et al, 2008;Wei and Murray, 2012). In this paper, we investigate the sensitivity of optimal land-use maps generated by optimisation algorithms to variation of input data and optimisation constraints.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of land-use pattern optimisation to variation in input data and constraints

2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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Spatial optimisation has been widely used in scientific studies for land-use pattern optimisation and resource allocation, often to maximise ecosystem services and/or land use performances. Such use implies its applicability in real-world spatial planning and policy development. But how suitable is spatial optimisation, especially in the context of uncertain input data and stakeholder expectations? For example, what is the impact of uncertainty associated with modelled nitrate leaching rates on land-use pattern optimisation with the objective to minimise nitrate leaching? Since spatial optimisation problems are usually constrained by stakeholder expectations or preferences, it is equally important to know how sensitive optimal land-use pattern are to changes in those expectations and preferences.In this paper, we investigate how sensitive optimal land-use patterns are to variation in input data (e.g. nitrate leaching rates) and optimisation constraints, such as stakeholder expectations in terms of agricultural production outputs. Our analysis was based on a spatial optimisation study in the Hawke's Bay area of New Zealand's North Island. The objective was to explore the landscape's limits in terms of the potential reduction in nitrate leaching. The optimisation problem was constrained by expectations for agricultural production outputs of the main agricultural land uses in this area. The spatial optimisation was based on nitrate leaching rates for each land use-land parcel combination of the case study area. Since no information was available on the uncertainties associated with the given nitrate leaching rates, we computed seven different optimisation scenarios, assuming seven different levels of uncertainties ranging from 5% to 50%. For each of those seven scenarios, we computed 500 optimisation runs and added to each run a uniformly distributed random error to the given nitrate leaching values. We then determined the allocation frequency of each land use to each land parcel for each uncertainty level. Based on the allocation frequencies, we determined the maximum allocation probability for each land parcel and uncertainty level, which represents the most likely allocated land use over 500 optimisation runs. The distributions of allocation probabilities across land parcels as well as across uncertainty levels were then used to characterise the sensitivity of the optimal land-use pattern to variation in the underlying nitrate leaching rates. To compare the landscape's potential to reduce nitrate leaching across the different uncertainty levels, we calculated the mean total nitrate leaching for the case study area for each uncertainty level (i.e. across 500 optimisation runs) and associated it with the mean maximum allocation probability for the particular uncertainty level. To analyse the impact of variation in the optimisation constraints on the optimal land-use pattern, we followed a similar approach. But instead of a random perturbation like with the nitrate leaching rates, we systematically varied the optimi...

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A stochastic approach to marine reserve design: Incorporating data uncertainty

Cited by 21 publications

References 28 publications

Incorporating uncertainty associated with habitat data in marine reserve design

Incorporating uncertainty associated with habitat data in marine reserve design

Trade-offs between data resolution, accuracy, and cost when choosing information to plan reserves for coral reef ecosystems

Sensitivity of land-use pattern optimisation to variation in input data and constraints

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