Spatial Conservation Prioritization in Data-Poor Areas of the World

Moilanen, Atte

doi:10.4322/natcon.2012.003

Cited by 14 publications

(7 citation statements)

References 52 publications

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“…Further, ongoing efforts to resolve the biases and deficiencies of taxonomic data sets are unlikely to occur quickly enough to allow improved conservation decision making 8 . Therefore, a major goal in ecology is to identify consistent biodiversity patterns that can be used to guide conservation actions despite this considerable uncertainty [9][10][11] .…”

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confidence: 99%

Global meta-analysis reveals low consistency of biodiversity congruence relationships

et al. 2014

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Knowledge of the number and distribution of species is fundamental to biodiversity conservation efforts, but this information is lacking for the majority of species on earth. Consequently, subsets of taxa are often used as proxies for biodiversity; but this assumes that different taxa display congruent distribution patterns. Here we use a global meta-analysis to show that studies of cross-taxon congruence rarely give consistent results. Instead, species richness congruence is highest at extreme spatial scales and close to the equator, while congruence in species composition is highest at large extents and grain sizes. Studies display highest variance in cross-taxon congruence when conducted in areas with dissimilar areal extents (for species richness) or latitudes (for species composition). These results undermine the assumption that a subset of taxa can be representative of biodiversity. Therefore, researchers whose goal is to prioritize locations or actions for conservation should use data from a range of taxa.

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confidence: 99%

Global meta-analysis reveals low consistency of biodiversity congruence relationships

et al. 2014

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“…Protected areas (PAs) represent a core response strategy, currently covering about 15% of the global land area [6]. However, PAs are often bedevilled by lack of resources and trained personnel, isolation, and poor management [4,[7][8][9]. They are declared on expert opinion or on aesthetic value, neither of which is likely to ensure biodiversity persistence [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Spatial conservation prioritisation (SCP) uses computational tools to prioritise areas for conservation actions [13]. It requires comprehensive, high-resolution, up-todate data on the distribution of biodiversity, but such data are usually incomplete or do not exist at all, making it challenging to develop reliable assessments in data-poor countries [1,8]. Such analyses are frequently performed using biodiversity surrogates, i.e.…”

Section: Introductionmentioning

confidence: 99%

Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa

2020

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Background: Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion. Results: Focal taxon (butterflies, reptiles, and mammals), sampling bias, connectivity and the choice of SDM algorithm were the most sensitive parameters; collectively these reflect data quality issues. In contrast, cell removal rule and species weights contributed much less to overall variability. Using currently available species data, we found the current effectiveness of Egypt's protected areas for conserving fauna was low. Conclusions: For SCP to be useful, there is a lower limit on data quality, requiring data-poor countries to improve sampling strategies and data quality to obtain unbiased data for as many taxa as possible. Since our sensitivity analysis may not generalise, conservation planners should use sensitivity analyses more routinely, particularly relying on more than one combination of SDM algorithm and surrogate group, consider correction for sampling bias, and compare the spatial patterns of predicted priority sites using a variety of settings. The sensitivity of SCP to connectivity parameters means that the responses of each species to habitat loss are important knowledge gaps.

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“…Remotely sensed data is increasingly used for conservation planning in data-poor regions, for example, to model habitat type or species richness to prioritize conservation interventions (Buchanan & Leidner, 2018;Moilanen, 2012). However, at a regional level, between continent-wide and site level analyses, methods to estimate abundance from the field and to model distributions represent key techniques to provide evidence for conservation measures.…”

Section: Introductionmentioning

confidence: 99%

Estimating ecological metrics for holistic conservation management in a biodiverse but information‐poor tropical region

Devenish

Cortez²,

Buchanan

et al. 2019

Conservat Sci and Prac

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Conservation ecologists face the dual challenge of working with difficult-tostudy species and providing ecological metrics that support conservation management at global, regional, and local levels. We present metrics identifying distributions, site-level and global abundance, site-contextualized habitat requirements, and threats for seven dry forest endemic birds (two threatened, one Near Threatened) in the globally important Tumbes region of Peru.Extents of occurrence ranged from 36,000 to 152,000 km 2 , and while broad distributions were generally congruent, nearly half of species overlapped <50% of their range with other species. Population sizes ranged from the low thousands in the threatened/Near Threatened taxa to >150,000. Site-level population estimates varied hugely, reflecting size of site and extreme variation in local abundances. Large tree girths and dense low cover generally promoted bird abundance, but stem density acted in opposite directions for different species, implying the need for site-and species-specific habitat management. Habitat quality varied across sites, further complicating management options at the local level (e.g., reduced grazing). We highlight the suitability of our methods in providing useful conservation metrics for data-poor regions, and demonstrate their application. Importantly, we propose key sites and priority actions for the region, including extensions of existing protected areas.

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Spatial Conservation Prioritization in Data-Poor Areas of the World

Cited by 14 publications

References 52 publications

Global meta-analysis reveals low consistency of biodiversity congruence relationships

Global meta-analysis reveals low consistency of biodiversity congruence relationships

Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa

Estimating ecological metrics for holistic conservation management in a biodiverse but information‐poor tropical region

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