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Friday, 26 August 2016

Local biodiversity is higher inside than outside terrestrial protected areas worldwide

Published Date
Nature Communications
 
7,
 
Article number:
 
12306
 
doi:10.1038/ncomms12306
Received
 
Accepted
 
Published
 

Data

For each sampling location or site in the PREDICTS database (November 2014 version), we calculated within-sample species richness, total abundance of individuals, rarefied richness (based on the fewest individuals at any site within each study) and community weighted mean log10geographic range size—the inverse of which was then plotted to give our endemicity measure. Each species’ range size was derived from its global occurrence in the Global Biodiversity Information Facility database. We recognise biases in the Global Biodiversity Information Facility data, but these are mitigated to some extent by our hierarchical modelling approach and our estimates compare reasonably well with estimates based on other data sources, listed in full in the Supplementary Information. Land use was classified using the study authors’ description for each site; this method has been shown to be repeatable33. Sites were considered to be protected if their geographical coordinates fell inside protected areas from the World Database on Protected Areas34 (see Supplementary Methods). We then derived two datasets: the first included all studies with sites inside and outside protected areas (all-sites data; Fig. 1b); the second retained only those sites from each study for which land use could be matched across the protected area boundary (matched-sites data 2; Fig. 1d). All sources of biodiversity data are listed in the Supplementary Information.

Analyses

We used generalized linear mixed-effects models to account for differences in response variables due to study-specific methodologies and the spatial structure of sites46. The PREDICTS data present a rare opportunity to compare sites inside and outside protected areas, but do not have the geographic coverage required for a stricter counterfactual approach14, 17 in which sites are individually matched. To reduce the risk that any differences observed between sites inside and outside protected areas were caused by biases in the location of protected areas27, we considered elevation47 and derived slope at c. 1 km2 resolution and agricultural suitability48 at 10 km2 resolution as covariates in all models (see Supplementary Information for further details). To ensure independence of all variables in the model, we intentionally included only these three confounding variables that we considered to be fully independent of the presence of a protected area. For example, distances to roads and markets are affected by the presence of protected areas so are not independent confounding factors (see Supplementary Information for details). We sequentially compared models with and without each fixed effect and at each step dropped the term with the highest P-value, until all terms had P<0.05 (ref. 49).

Assessing protection effects

We tested for biodiversity differences between sites inside and outside protected areas using the all-sites data, treating protection status (inside vs outside a protected area) as a fixed effect. We then tested whether biodiversity measures differed between management category groups by re-coding IUCN category as a four-level factor: unprotected, IUCN category III–VI, IUCN category unknown, and IUCN category I and II.

Assessing protection effects within and among land uses

We used two approaches to test whether biodiversity differences between protected and unprotected sites varied with land use. First, using the all-sites data, we modelled the response of each biodiversity measure to protection status, land use, and their interaction. We also tested for the three-way interaction between land use, protection and either use intensity, latitudinal zone or taxonomic group. Second, using the matched-sites data, we re-ran models with protection status, and then with management category group as a fixed effect. We also split the matched-sites data by latitudinal zone and taxonomic groups to assess whether these factors influenced the effect of protection. Finally, we tested whether the site-level biodiversity response to protection varied with the size/age class of the protected area [four-level factor with all combinations of young (<20 years), old (20–85 years), small (<400 km2) and large (400–12,000 km2); these thresholds between categories were selected to give a similar number of sites in each group].

Estimating global protected area effectiveness

The global effectiveness of protected areas (e) was estimated from e=1−(1−i)/(1−o), where modelled site-level biodiversity inside (i) and outside (o) protected areas are expressed as a proportion of that under ‘pristine’ conditions. We calculated the ratio of i/o from the model estimates for biodiversity inside relative to outside protected areas in each land use (Fig. 3), where each land-use parameter was weighted by the proportion of global terrestrial area within that land-use type. This value of i/ocould then be used to solve an equation expressing the global state of site-level biodiversity: 1−r=ai+(1−a)o, where r is the estimated global average loss of site-level biodiversity relative to pristine46 and a is the fraction of the total land area that is protected50. Solving this equation for i and o allowed us to estimate e. Finally, by using estimates for the effect of protection in IUCN categories I and II (Fig. 2a,b) to give i/o, we estimated e under the more restrictive management scenario. By rearranging the equations we estimated the total protected area (a) needed to obtain the same average local biodiversity outcome (1−r) inferred under this more restrictive management scenario. See Supplementary Information for more details.

Data availability

The biodiversity data that support the findings of this study are available in the Natural History Museum data portal (data.nhm.ac.uk) with the identifier dx.doi.org/10.5519/0095544. R scripts are available at http://github.com/claudialouisegray/PREDICTS_WDPA.

Additional Information 
How to cite this article: Gray, C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7:12306 doi: 10.1038/ncomms12306 (2016).
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We thank the hundreds of data contributors; all PREDICTS project volunteers, masters and PhD students that collated records; and Adriana De Palma, Helen Phillips, Diego Juffe-Bignoli, Neil Burgess, Max Gray, Daniel Ingram, Valerie Kapos, Naomi Kingston, Sarah Luke and the protected areas team at UNEP-WCMC for comments and assistance. We thank the School of Life Sciences at the University of Sussex for support and the Natural History Museum for a GIA travel award. The PREDICTS project is funded by the UK Natural Environment Research Council (NERC, grant number: NE/J011193/2). PREDICTS is endorsed by the Group on Earth Observations Biodiversity Observation Network (GEO BON). This is a contribution from the Imperial College Grand Challenges in Ecosystem and the Environment Initiative, and the Sussex Sustainability Research Programme.

  1. These authors contributed equally to this work.

    • Claudia L. Gray & 
    • Samantha L. L. Hill

  2. Present address: Conservation Programmes, Zoological Society of London, London NW1 4RY, UK

    • Claudia L. Gray

  3. Present address: Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London WC1E 6BT, UK (T.N.).

    • Tim Newbold

Affiliations

  1. School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9QG, UK

    • Claudia L. Gray &
      •  
    • Jörn P. W. Scharlemann

  2. United Nations Environment Programme World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge CB3 0DL, UK

    • Samantha L. L. Hill,
      •  
    • Tim Newbold &
      •  
    • Jörn P. W. Scharlemann

  3. Department of Life Sciences, Natural History Museum, Cromwell Road, London SW7 5BD, UK

    • Samantha L. L. Hill,
      •  
    • Lawrence N. Hudson,
      •  
    • Sara Contu &
      •  
    • Andy Purvis

  4. Department of Biosciences, College of Science, Swansea University, Singleton Park, Swansea SA2 8PP, UK

    • Luca Börger

  5. CSIRO Land and Water, Canberra Australian Capital Territory 2601, Australia

    • Andrew J. Hoskins &
      •  
    • Simon Ferrier

  6. Department of Life Sciences, Imperial College, London, Silwood Park, London SL5 7PY, UK

    • Andy Purvis

Contributions

C.L.G., S.L.L.H., T.N., A.P. and J.P.W.S. designed research; C.L.G., S.L.L.H., T.N., L.B., A.P. and J.P.W.S. performed research; C.L.G., S.L.L.H., T.N., L.N.H., S.C., A.J.H., S.F., A.P. and J.P.W.S. contributed data and analytical tools; C.L.G. and S.L.L.H. analysed data; C.L.G., S.L.L.H., T.N., L.N.H., L.B., A.P. and J.P.W.S. wrote the paper.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors


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