Reconsidering environmental diversity (ED) as a biodiversity surrogacy strategy

Engelbrecht, Ian; Robertson, Mark P.; Stoltz, M.; Joubert, Johan W.

doi:10.1016/j.biocon.2016.02.028

Cited by 21 publications

(37 citation statements)

References 57 publications

(79 reference statements)

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“…To select sites with ED, we used the modified maximum diversity algorithm, which was developed to implement ED across a variety of taxa and ecosystems (Engelbrecht et al 2016). This approach uses a greedy algorithm to iteratively add sites to a set (starting with a small set chosen at random) so that the amount of environmental heterogeneity represented in the set is maximized, which is done by maximizing the distance (in environmental space) between a set of sites and new sites to be added to the set.…”

Section: Site Selectionmentioning

confidence: 99%

“…), but recently refined algorithms perform well across many taxa and locations (Beier & Albuquerque ; Engelbrecht et al. ).…”

Section: Introductionmentioning

confidence: 99%

“…The goal of site complementarity mirrors how conservation sites are commonly selected based on biodiversity information (as opposed to environmental information) with popular planning software such as Marxan (Ball et al 2009). The ED approach has had variable success in selecting areas of high current biodiversity (Araújo et al 2001;Hortal et al 2009), but recently refined algorithms perform well across many taxa and locations Engelbrecht et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Using environmental heterogeneity to plan for sea‐level rise

Hunter

Nibbelink

2017

Conservation Biology

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Environmental heterogeneity is increasingly being used to select conservation areas that will provide for future biodiversity under a variety of climate scenarios. This approach, termed conserving nature's stage (CNS), assumes environmental features respond to climate change more slowly than biological communities, but will CNS be effective if the stage were to change as rapidly as the climate? We tested the effectiveness of using CNS to select sites in salt marshes for conservation in coastal Georgia (U.S.A.), where environmental features will change rapidly as sea level rises. We calculated species diversity based on distributions of 7 bird species with a variety of niches in Georgia salt marshes. Environmental heterogeneity was assessed across six landscape gradients (e.g., elevation, salinity, and patch area). We used 2 approaches to select sites with high environmental heterogeneity: site complementarity (environmental diversity [ED]) and local environmental heterogeneity (environmental richness [ER]). Sites selected based on ER predicted present-day species diversity better than randomly selected sites (up to an 8.1% improvement), were resilient to areal loss from SLR (1.0% average areal loss by 2050 compared with 0.9% loss of randomly selected sites), and provided habitat to a threatened species (0.63 average occupancy compared with 0.6 average occupancy of randomly selected sites). Sites selected based on ED predicted species diversity no better or worse than random and were not resilient to SLR (2.9% average areal loss by 2050). Despite the discrepancy between the 2 approaches, CNS is a viable strategy for conservation site selection in salt marshes because the ER approach was successful. It has potential for application in other coastal areas where SLR will affect environmental features, but its performance may depend on the magnitude of geological changes caused by SLR. Our results indicate that conservation planners that had heretofore excluded low-lying coasts from CNS planning could include coastal ecosystems in regional conservation strategies.

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Section: Site Selectionmentioning

confidence: 99%

“…), but recently refined algorithms perform well across many taxa and locations (Beier & Albuquerque ; Engelbrecht et al. ).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using environmental heterogeneity to plan for sea‐level rise

Hunter

Nibbelink

2017

Conservation Biology

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“…The usefulness of some links have been recognised, e.g. seasonal calving of wildebeest and the risk of malignant catarrhal fever for cattle [9], or earth satellite images used in identifying vegetation as a proxy for predicting the distribution of dung beetles and herbivores [6]. The opportunities for such integrated approaches have increased with the availability of computer technology for storing vast amounts of data as well as algorithms to search and link data from different sources and in different formats the so called Big Data science.…”

Section: Integrated Approach To Disease Studies At the Interfacementioning

confidence: 99%

“…The integrative approach promoted in this paper has resulted in a number of initiatives such as an informal weekly meeting under the name of "Biomath Coffee" which facilitates the early involvement of mathematics in biological research. Within this forum presentation of student projects still at the stage of formulation of research question are encouraged, with presenters benefitting from the comments and advice of biologists and mathematicians present and, at least in some cases, fruitful collaboration was established [3], [5], [6].…”

Section: Mammal Research At University Of Pretoriamentioning

confidence: 99%

Can Mathematics be Biology's next microscope in disease research at the interface?

Anguelov

Bastos²

2017

BIOMATH

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Improving the use of environmental diversity as a surrogate for species representation

Albuquerque

Beier

2017

Ecology and Evolution

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The continuous p‐median approach to environmental diversity (ED) is a reliable way to identify sites that efficiently represent species. A recently developed maximum dispersion (maxdisp) approach to ED is computationally simpler, does not require the user to reduce environmental space to two dimensions, and performed better than continuous p‐median for datasets of South African animals. We tested whether maxdisp performs as well as continuous p‐median for 12 datasets that included plants and other continents, and whether particular types of environmental variables produced consistently better models of ED. We selected 12 species inventories and atlases to span a broad range of taxa (plants, birds, mammals, reptiles, and amphibians), spatial extents, and resolutions. For each dataset, we used continuous p‐median ED and maxdisp ED in combination with five sets of environmental variables (five combinations of temperature, precipitation, insolation, NDVI, and topographic variables) to select environmentally diverse sites. We used the species accumulation index (SAI) to evaluate the efficiency of ED in representing species for each approach and set of environmental variables. Maxdisp ED represented species better than continuous p‐median ED in five of 12 biodiversity datasets, and about the same for the other seven biodiversity datasets. Efficiency of ED also varied with type of variables used to define environmental space, but no particular combination of variables consistently performed best. We conclude that maxdisp ED performs at least as well as continuous p‐median ED, and has the advantage of faster and simpler computation. Surprisingly, using all 38 environmental variables was not consistently better than using subsets of variables, nor did any subset emerge as consistently best or worst; further work is needed to identify the best variables to define environmental space. Results can help ecologists and conservationists select sites for species representation and assist in conservation planning.

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Reconsidering environmental diversity (ED) as a biodiversity surrogacy strategy

Cited by 21 publications

References 57 publications

Using environmental heterogeneity to plan for sea‐level rise

Using environmental heterogeneity to plan for sea‐level rise

Can Mathematics be Biology's next microscope in disease research at the interface?

Improving the use of environmental diversity as a surrogate for species representation

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