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A global assessment of the social and conservation outcomes of protected areas

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TLDR
Whereas the strictest regimes of PA management attempted to exclude anthropogenic influences to achieve biological conservation objectives, PAs that explicitly integrated local people as stakeholders tended to be more effective at achieving joint biological conservation and socioeconomic development outcomes.
Abstract
Protected areas (PAs) are a key strategy for protecting biological resources, but they vary considerably in their effectiveness and are frequently reported as having negative impacts on local people. This has contributed to a divisive and unresolved debate concerning the compatibility of environmental and socioeconomic development goals. Elucidating the relationship between positive and negative social impacts and conservation outcomes of PAs is key for the development of more effective and socially just conservation. We conducted a global meta-analysis on 165 PAs using data from 171 published studies. We assessed how PAs affect the well-being of local people, the factors associated with these impacts, and crucially the relationship between PAs' conservation and socioeconomic outcomes. Protected areas associated with positive socioeconomic outcomes were more likely to report positive conservation outcomes. Positive conservation and socioeconomic outcomes were more likely to occur when PAs adopted comanagement regimes, empowered local people, reduced economic inequalities, and maintained cultural and livelihood benefits. Whereas the strictest regimes of PA management attempted to exclude anthropogenic influences to achieve biological conservation objectives, PAs that explicitly integrated local people as stakeholders tended to be more effective at achieving joint biological conservation and socioeconomic development outcomes. Strict protection may be needed in some circumstances, yet our results demonstrate that conservation and development objectives can be synergistic and highlight management strategies that increase the probability of maximizing both conservation performance and development outcomes of PAs.

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A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures.

TL;DR: This analysis of protected areas across 152 countries finds that compared with matched unprotected areas, PAs have on average not reduced a compound index of pressure change over the past 15 y, and cautions against the rapid establishment of new PAs without simultaneously addressing the conditions needed to enable their success.
Journal ArticleDOI

Farming approaches for greater biodiversity, livelihoods, and food security

TL;DR: Evidence is urgently needed to direct science-policy initiatives, such as the United Nations 2030 Agenda for Sustainable Development, based on quantitative evidence of the impacts of conventional intensification on biodiversity loss and greenhouse gas emissions.
References
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Journal ArticleDOI

Predictive habitat distribution models in ecology

TL;DR: A review of predictive habitat distribution modeling is presented, which shows that a wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management.
Journal ArticleDOI

Property rights regimes and natural resources: A conceptual analysis

Edella Schlager, +1 more
- 01 Jan 1992 - 
TL;DR: In this paper, a conceptual schema for arraying property-rights regimes that distinguishes among diverse bundles of rights ranging from authorized user, to claimant, to proprietor, and to owner is developed.
BookDOI

Guidelines for applying protected area management categories

Nigel Dudley
TL;DR: IUCN's Protected Areas Management Categories (PAMC) are recognized by international bodies such as the United Nations as well as many national governments as mentioned in this paper as the benchmark for defining, recording and classifying protected areas.
Journal ArticleDOI

An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests

TL;DR: The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application.
Journal ArticleDOI

Variable selection using random forests

TL;DR: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection, and proposes a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.
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