A framework for assessing conservation and development in a Congo Basin Forest Landscape

An integrated framework for assessing conservation and development changes at the scale of a large forest landscape in the Congo Basin is described. The framework allows stakeholders to assess progress in achieving the often conflicting objectives of alleviating poverty and conserving global environmental values. The study shows that there was little change in either livelihood or conservation indicators over the period 2006 to 2008, and that the activities of conservation organizations had only modest impacts on either. The global economic down-turn in 2008 had immediate negative consequences for both local livelihoods and for biodiversity as people lost their employment in the cash economy and reverted to illegal harvesting of forest products. Weakness of institutions, and corruption were the major obstacles to achieving either conservation or development objectives. External economic changes had more impact on this forest landscape than either the negative or positive interventions of local actors

Exploring the effectiveness of integrated conservation and development interventions in a Central African forest landscape

Integrated conservation and development projects (ICDPs) have had limited success in addressing the often conflicting objectives of conservation and development. We developed a model with local participants to explore the trade-offs between conservation and development in southeastern Cameroon, where illegal hunting is regarded as the greatest challenge to conservation. We simulated the effects of different ICDP strategies by varying the degree of focus on antipoaching activities, anticorruption measures and direct development investments, and by varying the overall budget for such activities. Our outcome variables were numbers of selected wildlife species and household incomes. The model outcomes from the different scenarios were used to stimulate debate among stakeholders. Contributing to poverty alleviation while maintaining current animal population sizes will be extremely difficult and will require long-term external financial support. Devoting greater attention to improving local environmental governance emerged as the highest priority for this investment. We used the model outputs to inform some of the major policy makers in the region. Participatory modeling is a valuable means of capturing the complexities of achieving conservation at landscape scales and of stimulating innovative solutions to entrenched problems

Assessing environment and development outcomes in conservation landscapes

An approach to assessing the environmental outcomes and changes in peoples' livelihoods resulting from landscape-scale conservation interventions was developed for three locations in Africa. Simple sets of performance indicators were developed through participatory processes that included a variety of stakeholders. The selection of indicators was designed to reflect wider landscape processes, conservation objectives and as local peoples' preferred scenarios. This framework, combined with the use of social learning techniques, helped stakeholders develop greater understandings of landscape system dynamics and the linkages between livelihood and conservation objectives. Large scale conservation and development interventions should use these approaches to explore linkages and improve shared understanding of tradeoffs and synergies between livelihood and conservation initiatives. Such approaches provide the basis for negotiating and measuring the outcomes of conservation initiatives and for adapting these to changing perspectives and circumstances

Range-wide indicators of African great ape density distribution

Species distributions are influenced by processes occurring at multiple spatial scales. It is therefore insufficient to model species distribution at a single geographic scale, as this does not provide the necessary understanding of determining factors. Instead, multiple approaches are needed, each differing in spatial extent, grain, and research objective. Here, we present the first attempt to model continent-wide great ape density distribution. We used site-level estimates of African great ape abundance to (1) identify socioeconomic and environmental factors that drive densities at the continental scale, and (2) predict range-wide great ape density. We collated great ape abundance estimates from 156 sites and defined 134 pseudo-absence sites to represent additional absence locations. The latter were based on locations of unsuitable environmental conditions for great apes, and on existing literature. We compiled seven socioeconomic and environmental covariate layers and fitted a generalized linear model to investigate their influence on great ape abundance. We used an Akaike-weighted average of full and subset models to predict the range-wide density distribution of African great apes for the year 2015. Great ape densities were lowest where there were high Human Footprint and Gross Domestic Product values; the highest predicted densities were in Central Africa, and the lowest in West Africa. Only 10.7% of the total predicted population was found in the International Union for Conservation of Nature Category I and II protected areas. For 16 out of 20 countries, our estimated abundances were largely in line with those from previous studies. For four countries, Central African Republic, Democratic Republic of the Congo, Liberia, and South Sudan, the estimated populations were excessively high. We propose further improvements to the model to overcome survey and predictor data limitations, which would enable a temporally dynamic approach for monitoring great apes across their range based on key indicators

Influence of tourism activities and PA size on threat level in 83 PAs.

<p>In bold are highlighted significant values (p <i><0.05</i>). See abbreviations in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114154#pone-0114154-t002" target="_blank">Tab 2</a>. AIC, Akaike's Information Criterion; AICw, Akaike Information Criterion weight; Rank, model rank from the smallest to the largest AIC value; k, number of variables including the intercept.</p><p>Influence of tourism activities and PA size on threat level in 83 PAs.</p

Proportion of protected areas with conservation activities between 1990 and 1999 across different African regions.

<p>The number of protected areas with available information on presence and absence of any conservation activity (research, tourism and law enforcement guards) over the considered period were in total 105.</p

Regional distribution of the protected areas (PAs) in tropical Africa considered in the analyses.

<p>The regions are coloured in different grey scale colours. Light grey represents West Africa, including 54 protected areas; medium grey represents Central Africa, including 31 protected areas; dark grey represents East Africa, including 14 protected areas. On the left-side bottom corner a MODIS NDVI image of Africa, with a red quadrant highlighting the tropical area considered in the study.</p

Influence of law enforcement activities and PA size on threat levels in 90 PAs.

<p>In bold are highlighted significant values (p <i><0.05</i>). See abbreviations in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114154#pone-0114154-t002" target="_blank">Tab 2</a>. AIC, Akaike's Information Criterion; AICw, Akaike Information Criterion weight; Rank, model rank from the smallest to the largest AIC value; k, number of variables including the intercept.</p><p>Influence of law enforcement activities and PA size on threat levels in 90 PAs.</p

Influence of research activities and PA size on threat level in 92 PAs.

<p>In bold are highlighted significant values (p <i><0.05</i>). See abbreviations in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114154#pone-0114154-t002" target="_blank">Tab 2</a>. AIC, Akaike's Information Criterion; AICw, Akaike Information Criterion weight; Rank, model rank from the smallest to the largest AIC value; k, number of variables including the intercept.</p><p>Influence of research activities and PA size on threat level in 92 PAs.</p

Symmetric matrix with Spearman's correlation between all threat impact levels recorded in 98 protected areas.

<p>In bold are highlighted significant correlations (<i>p</i><0.0001) following post hoc test Bonferroni correction (<i>p</i> = 0.05/78). Abbreviations: coh, commercial hunting; suh, subsistence hunting; agr, agriculture; fuw, fuel wood; inf, infrastructure; has, human settlement around; his, human settlement inside; war, war; dis, disease; fir, fire; min, mining; log, logging.</p><p>Symmetric matrix with Spearman's correlation between all threat impact levels recorded in 98 protected areas.</p