Effectiveness of protected areas in north-eastern New South Wales: recent trends in six measures

Pressey, Robert L.; Whish, Giselle; Barrett, Thomas; Watts, Matthew

doi:10.1016/s0006-3207(01)00229-4

Cited by 144 publications

(135 citation statements)

References 42 publications

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“…For this particular study this assumption has been advantageous in validating the presence of different driving factors affecting the forest cover pre and post 1992 [52]. Further, the argument that protection goes to less threatened areas and a PA would maintain forest cover irrespective of any policy intervention [43,98] would be redundant in the present study. This is due to the fact that BNP experienced forest cover decline from 1973 to 1992 and is situated just few miles outside the rapidly growing city of Bangalore, in a densely populated area with seven village communities inside the national park.…”

Section: Discussionmentioning

confidence: 92%

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

2012

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Establishment of protected areas (PA) has been one of the leading tools in biodiversity conservation. Globally, these kinds of conservation interventions have given rise to an increase in PAs as well as the need to empirically evaluate the impact of these PAs on forest cover. Few of these empirical evaluations have been geared towards comparison of pre and post policy intervention landscapes. This paper provides a method to empirically evaluate such pre and post policy interventions by using a cellular automata-Markov model. This method is tested using remotely sensed data of Bannerghatta National park (BNP) and its surrounding, which have experienced various national level policy interventions (Indian National Forest Policy of 1988) and rapid land cover change between 1973 and 2007. The model constructs a hypothetical land cover scenario of BNP and its surroundings (1999 and 2007) in the absence of any policy intervention, when in reality there has been a significant potential policy intervention effect. The models predicted a decline in native forest cover and an increase in non forest cover post 1992 whereas the actual observed landscape experienced the reverse trend where after an initial decline from 1973 to 1992, the forest cover in BNP is towards recovery post 1992. Furthermore, the models show a higher deforestation and lower reforestation than the observed deforestation and reforestation patterns for BNP post 1992. Our results not only show the implication of national level policy changes on forest cover but also show the usefulness of our method in evaluating such conservation efforts.

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Section: Discussionmentioning

confidence: 92%

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

2012

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“…Even when biodiversity and humans have different reasons for spatial preferences, the fact that both cost maps and species distributions are spatially autocorrelated means that some ''unlucky'' species are concentrated in regions of high economic value. If these unlucky species have not been mapped, we will fail to conserve them when we exclude high-value locations from the system, essentially replicating historical conservation efforts that tended to concentrate protected areas in high elevation ''rock and ice'' sites and away from agriculturally productive ecosystems (Scott et al 2001, Pressey et al 2002.…”

Section: Discussionmentioning

confidence: 99%

“…Early protected areas were sited at locations with low potential for commercial or subsistence use; these failed to conserve the biodiversity that was most threatened by human activities (Pressey 1994, Pressey et al 2002, Possingham et al 2006, Margules and Sarkar 2007. Design principles (representation, adequacy, comprehensiveness, complementarity, etc.…”

Section: Introductionmentioning

confidence: 99%

The role of scale in designing protected area systems to conserve poorly known species

2015

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Possingham. 2015. The role of scale in designing protected area systems to conserve poorly known species. Ecosphere 6(11):237. http://dx.doi.org/10.1890/ES15-00346.1Abstract. Systematic conservation planning has a substantial theoretical underpinning that allows optimization of tradeoffs between biodiversity conservation and other socioeconomic goals. However, this theory assumes perfect spatial information about the locations of biodiversity features (e.g., species distributions). In practice, planners represent well-known taxa and other biodiversity ''surrogates'' in protected area systems, hoping that unmapped species will also be conserved. However, empirical research finds that surrogates predict species presence imperfectly, and sometimes rather poorly, at scales relevant to planning, and existing theory provides no further guidance. We developed new theory, explicitly incorporating aspects of spatial scale, for the representation problem when the locations of species distributions are unknown. Using probability theory and simulated and real species distributions, we found that the probability of adequately representing an unmapped species in a protected area system will be low unless the total fraction of the region being protected is larger than the species representation target. Furthermore, successful conservation depended critically on the relative sizes of the species distribution and of the individual protected areas; fewer, larger protected areas allowed the entire species distribution to fall into an unprotected gap. This scale-dependence varied with the configuration of the protected area system, with the conservation objective most likely to be attained if the individual protected areas were hyperdispersed (evenly spaced across the planning region). Using these results, we developed three design principles for representing unmapped species in protected areas: (1) The fraction of the region placed in protected areas should be substantially larger than the species-level representation target; (2) Individual protected areas must be at least one to two orders of magnitude smaller than the unmapped species' distribution; and (3) Protected areas should be evenly dispersed over geographic space. We also performed preliminary investigations of the effects of surrogates and socio-economic cost data on the probability of adequately representing unmapped species, finding that the primary effect of surrogates may simply be to promote hyperdispersion of protected areas across the planning region, and that seeking to minimize opportunity costs gives poorer conservation results than random protected area placement.

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“…Australia's reserves therefore tend to protect areas that are steep and/or infertile (Pressey et al 2002). As a consequence, one-third of Australia's bioregions are very poorly represented (less than 5 per cent) in Australia's National Reserve System (Hatton et al 2011).…”

Section: The Theory Of Biodiversity Prioritisationmentioning

confidence: 99%

Learning from agri-environment schemes in Australia: Investing in biodiversity and other ecosystem services on farms

Ansell¹,

Gibson²,

Salt³

2016

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Effectiveness of protected areas in north-eastern New South Wales: recent trends in six measures

Cited by 144 publications

References 42 publications

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

The role of scale in designing protected area systems to conserve poorly known species

Learning from agri-environment schemes in Australia: Investing in biodiversity and other ecosystem services on farms

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