DETERMINING CONSERVATION PRIORITIES AND PARTICIPATIVE LAND USE PLANNING STRATEGIES IN THE MARINGA-LOPORI-WAMBA LANDSCAPE, DEMOCRATIC REPUBLIC OF THE CONGO

Deforestation and forest degradation driven largely by agricultural expansion are key drivers of biodiversity loss in the tropics. Achieving sustainable and equitable management of land and resources and determining priority areas for conservation activities are important in the face of these advancing pressures. The Congo Basin of Central Africa contains approximately 20% of the world's remaining tropical forest area and serves as important habitat for over half of Africa's flora and fauna. The Government of the Democratic Republic of the Congo (DRC) is currently laying the foundation for a national land use plan for conservation and sustainable use of its forests. Since 2004, the African Wildlife Foundation (AWF) has led efforts to develop a participatory land use plan for the Maringa-Lopori-Wamba (MLW) Landscape located in northern DRC. The landscape was recognized in 2002 as one of twelve priority landscapes in the Congo Basin targeted for the establishment of sustainable management plans. This dissertation focuses on the development of geospatial methods and tools for determining conservation priorities and assisting land use planning efforts in the MLW Landscape. The spatio-temporal patterns of recent primary forest loss are analyzed and complemented by the development of spatial models that identify the locations of 42 forest blocks and 32 potential wildlife corridors where conservation actions will be most important to promote future viability of landscape-wide terrestrial biodiversity such as the bonobo (Pan paniscus). In addition, the research explores three scenarios of potential agricultural expansion by 2050 and provides spatially-explicit information to show how trade-offs between biological conservation and human agricultural livelihoods might be balanced in land use planning processes. The research also describes a methodological approach for integrating spatial tools into participatory mapping processes with local communities and demonstrates how the resulting spatial data can be used to inform village-level agricultural land use for resource planning and management. Conclusions from the work demonstrate that primary forest loss is intensifying around agricultural complexes and that wildlife corridors connecting least-disturbed forest blocks are most vulnerable to future forest conversion. Conservation of these areas is possible with the development of land use plans in collaboration with local communities

Building capacity in remote sensing for conservation: present and future challenges

Remote sensing (RS) has made significant contributions to conservation and ecology; however, direct use of RS-based information for conservation decision making is currently very limited. In this paper, we discuss the reasons and challenges associated with using RS technology by conservationists and suggest how training in RS for conservationists can be improved. We present the results from a survey organized by the Conservation Remote Sensing Network to understand the RS expertise and training needs of various categories of professionals involved in conservation research and implementation. The results of the survey highlight the main gaps and priorities in the current RS data and technology among conservation practitioners from academia, institutions, NGOs and industry. We suggest training to be focused around conservation questions that can be addressed using RS-derived information rather than training pure RS methods which are beyond the interest of conservation practitioners. We highlight the importance of developing essential biodiversity variables (EBVs) and how this can be achieved by increasing the RS capacity of the conservation community. Moreover, we suggest that open-source software is adopted more widely in the training modules to facilitate access to RS data and products in developing countries, and that online platforms providing mapping tools should also be more widely distributed. We believe that improved RS capacity among conservation scientists will be essential to improve conservation efforts on the ground and will make the conservation community a key player in the definition of future RS-based products that serve conservation and ecological needs

Pan paniscus (errata version published in 2016)

Due to high levels of illegal hunting, and habitat destruction and degradation,Pan paniscusis estimated to have experienced a significant population reduction in the last 15–20 years and it is thought that this reduction will continue for the next 60 years. Currently, by far the greatest threat to the Bonobo's survival is poaching for the commercial bushmeat trade. It has been estimated that nine tons of bushmeat are extracted daily from a 50,000-km² conservation landscape within the Bonobo’s range. Not only is there is a massive demand for bushmeat stemming from the cities, but rebel factions and poorly-paid government soldiers add to that demand, at the same time facilitating the flow of guns and ammunition (Fruthet al. 2013). In some areas, local taboos against eating Bonobo meat still exist, but in others, these traditions are disintegrating due to changing cultural values and population movements. Stricter enforcement of wildlife laws and more effective management are urgently needed. Habitat loss through deforestation and fragmentation ranks second. Much of the forest loss in this region is caused by slash-and-burn subsistence agriculture, which is most intense where human densities are high or growing. Logging and mining do not yet occur on an industrial scale in the Bonobo’s range, but in future, industrial agriculture is very likely to become a serious threat. Minimising the conversion of intact forest to human-dominated land uses, will be critical for the future survival of Bonobos. Countrywide factors contributing to the decline include the mobility of growing human populations, opening markets, commercial exploitation of natural resources and road construction. As in the past, the survival of Bonobos will be determined by the levels of poaching and forest loss—threats that have been shown to accompany rapid growth in human populations and political instability (Nackoneyet al. 2014). Due to their slow life history and a generation time estimated to be 25 years, Bonobo populations cannot withstand high levels of offtake. The population decline over a three-generation (75 year) period from 2003 to 2078 is likely to exceed 50%, hence qualifying this taxon as Endangered under criterion A

Landsat ETM+ and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes) Habitats in Africa

All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic space over time. Previous studies mapping chimpanzee habitat suitability have been limited to small regions or coarse spatial and temporal resolutions. Here, we used Random Forests regression to downscale a coarse resolution habitat suitability calibration dataset to estimate habitat suitability over the entire chimpanzee range at 30-m resolution. Our model predicted habitat suitability well with an r2 of 0.82 (±0.002) based on 50-fold cross validation where 75% of the data was used for model calibration and 25% for model testing; however, there was considerable variation in the predictive capability among the four sub-species modeled individually. We tested the influence of several variables derived from Landsat Enhanced Thematic Mapper Plus (ETM+) that included metrics of forest canopy and structure for four three-year time periods between 2000 and 2012. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. Because the models were sensitive to such temporally based predictors, our results are the first to highlight the value of integrating continuously updated variables derived from satellite remote sensing into temporally dynamic habitat suitability models to support  near real-time monitoring of habitat status and decision support systems

Building capacity in remote sensing for conservation: present and future challenges

Remote sensing (RS) has made significant contributions to conservation and ecology; however, direct use of RS-based information for conservation decision making is currently very limited. In this paper, we discuss the reasons and challenges associated with using RS technology by conservationists and suggest how training in RS for conservationists can be improved. We present the results from a survey organized by the Conservation Remote Sensing Network to understand the RS expertise and training needs of various categories of professionals involved in conservation research and implementation. The results of the survey highlight the main gaps and priorities in the current RS data and technology among conservation practitioners from academia, institutions, NGOs and industry. We suggest training to be focused around conservation questions that can be addressed using RS-derived information rather than training pure RS methods which are beyond the interest of conservation practitioners. We highlight the importance of developing essential biodiversity variables (EBVs) and how this can be achieved by increasing the RS capacity of the conservation community. Moreover, we suggest that open-source software is adopted more widely in the training modules to facilitate access to RS data and products in developing countries, and that online platforms providing mapping tools should also be more widely distributed. We believe that improved RS capacity among conservation scientists will be essential to improve conservation efforts on the ground and will make the conservation community a key player in the definition of future RS-based products that serve conservation and ecological needs

Landsat ETM+ and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes) Habitats in Africa

All four chimpanzee sub-species populations are declining due to multiple factors including human-caused habitat loss. Effective conservation efforts are therefore needed to ensure their long-term survival. Habitat suitability models serve as useful tools for conservation planning by depicting relative environmental suitability in geographic space over time. Previous studies mapping chimpanzee habitat suitability have been limited to small regions or coarse spatial and temporal resolutions. Here, we used Random Forests regression to downscale a coarse resolution habitat suitability calibration dataset to estimate habitat suitability over the entire chimpanzee range at 30-m resolution. Our model predicted habitat suitability well with an r2 of 0.82 (±0.002) based on 50-fold cross validation where 75% of the data was used for model calibration and 25% for model testing; however, there was considerable variation in the predictive capability among the four sub-species modeled individually. We tested the influence of several variables derived from Landsat Enhanced Thematic Mapper Plus (ETM+) that included metrics of forest canopy and structure for four three-year time periods between 2000 and 2012. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. Because the models were sensitive to such temporally based predictors, our results are the first to highlight the value of integrating continuously updated variables derived from satellite remote sensing into temporally dynamic habitat suitability models to support  near real-time monitoring of habitat status and decision support systems

Human proximity and habitat fragmentation are key drivers of the rangewide bonobo distribution

Habitat loss and hunting threaten bonobos (Pan paniscus), Endangered (IUCN) great apes endemic to lowland rainforests of the Democratic Republic of Congo. Conservation planning requires a current, data-driven, rangewide map of probable bonobo distribution and an understanding of key attributes of areas used by bonobos. We present a rangewide suitability model for bonobos based on a maximum entropy algorithm in which data associated with locations of bonobo nests helped predict suitable conditions across the species' entire range. We systematically evaluated available biotic and abiotic factors, including a bonobo-specific forest fragmentation layer (forest edge density), and produced a final model revealing the importance of simple threat-based factors in a data poor environment. We confronted the issue of survey bias in presence-only models and devised a novel evaluation approach applicable to other taxa by comparing models built with data from geographically distinct sub-regions that had higher survey effort. The model's classification accuracy was high (AUC = 0.82). Distance from agriculture and forest edge density best predicted bonobo occurrence with bonobo nests more likely to occur farther from agriculture and in areas of lower edge density. These results suggest that bonobos either avoid areas of higher human activity, fragmented forests, or both, and that humans reduce the effective habitat of bonobos. The model results contribute to an increased understanding of threats to bonobo populations, as well as help identify priority areas for future surveys and determine core bonobo protection areas.Additional co-authors: Omari Ilambu; Bila-Isia Inogwabini; Innocent Liengola; Albert Lotana Lokasola; Alain Lushimba; Joel Masselink; Valentin Mbenzo; Norbert Mbangia Mulavwa; Pascal Naky; Nicolas Mwanza Ndunda; Pele Nkumu; Valentin Omasombo; Gay Edwards Reinartz; Robert Rose; Tetsuya Sakamaki; Samantha Strindberg; Hiroyuki Takemoto; Ashley Vosper; Hjalmar S. Küh