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A conceptual diagram of the spatially-explicit threat-based model developed for identifying the spatial distribution of human influence in the MLW Landscape. The major components of the model include factors relating to potential hunting pressure and habitat degradation in the landscape.
Source publication
Determining priority areas for conservation activities in the forests of the Congo Basin is increasingly
important in the face of advancing human pressures and deforestation. Since 2004, the African Wildlife Foundation (AWF) has led conservation and land-use planning activities in the Maringa-Lopori-Wamba (MLW) Landscape located in northern Democr...
Contexts in source publication
Context 1
... conceptual diagram of the model developed is shown in Figure 2. The model considered 1.) Hunting pressure (including human accessibility and relative population demand for bushmeat), as well as 2.) Habitat degradation (including the influence of agricultural and urban areas as well as large-scale plantations). ...
Context 2
... we added together the hunting accessibility and habitat degradation surfaces. Because we agreed that hunting poses a greater immediate threat to terrestrial biodiversity in MLW (especially to the bonobo, cited in IUCN 2010), we assigned it a weight of 60%, versus 40% for habitat degradation (Figure 2). ...
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Citations
... Using a Geographic Information System (GIS), we also delineated (c) a buffer zone located within the PFZs that extended 3 km away from the outermost boundary of the AMZ. Since tree cover loss in this area is all caused by agricultural expansion and its geographic pattern generally extends outward from the roads and agricultural areas [3,31], such a buffer zone might be required to manage agricultural activities that expand outside of the AMZs and could be appropriate for implementing less intensively extractive practices to support local livelihoods, such as agro-forestry. ...
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This study aims to analyse the spatial pattern of deforestation and fragmentation in forests of Kinnerasani wildlife sanctuary and surroundings, Telangana, India. This study has found the annual deforestation rates between 2005 and 2015 as 1.38% and 1.50% inside and surroundings of the study area respectively. The fragmentation analysis reveals the high reduction in large core areas over the period of 2005–2015. Temporal forest cover change analysis was linked with predictive modelling to generate future forest cover scenario. The multi-layer perceptron neural network modelling was used for forecasting the deforestation for 2025. This study evaluates the nature of changes in deforestation and its affecting explanatory drivers such as slope (in degrees), elevation (in m), and the shortest distance to roads (in m), the shortest distance to nearest settlements (in m) and the distance from water bodies (in m). Spatial modelling has forecasted the annual change rate of forest as 0.19% in Kinnerasani wildlife sanctuary which indicates decreasing trend of forest loss in near future. The results of the study are useful as spatial input for conservation of forests in Kinnerasani wildlife sanctuary, Telangana, India.
... The model's measure of habitat degradation considered the relative influence of a variety of factors influencing quality of bonobo habitat, including the influence of agricultural and urban areas, logging roads and industrial plantations. Both sub-models are described in further detail in Nackoney and Williams (2012). The two sub-models were combined in an additive weighting process. ...
... Les composants majeurs du modèle incluent des facteurs en relation à la pression potentielle de chasse et la dégradation dans le paysage.) detail in Nackoney and Williams (2012), the output of the human influence model was used to depict the relative conditions promoting or discouraging bonobo movement and to identify the most permeable travel routes between blocks. Because bonobos do not cross major rivers, locations of rivers spanning at least 30 metres across were mapped using Landsat satellite imagery and applied in the model as a prohibitive constraint to connectivity. ...
The endangered bonobo (Pan paniscus), endemic to the Democratic Republic of Congo (DRC), is threatened by hunting and habitat loss. Two recent wars and ongoing conflicts in the DRC greatly challenge conservation efforts. This chapter demonstrates how spatial data and maps are used for monitoring threats and prioritizing locations to safeguard bonobo habitat, including identifying areas of highest conservation value to bonobos and collaboratively mapping community-based natural resource management (CBNRM) zones for reducing deforestation in key corridor areas. We also highlight the development of a range-wide model that analysed a variety of biotic and abiotic variables in conjunction with bonobo nest data to map suitable habitat. Approximately 28 per cent of the range was predicted suitable; of that, about 27.5 per cent was located in official protected areas. These examples highlight the importance of employing spatial data and models to support the development of dynamic conservation strategies that will help strengthen bonobo protection.
... Previous studies have only focused on biodiversity when planning conservation strategies [4,5]. However, systematic conservation planning should take into account landscape pattern, ecological vulnerability and anthropogenic threat [6]. As biodiversity is a determinant of ecosystem process and plays a key role in many ecosystem services [7,8], both biodiversity and ecosystem services (BES) should be modeled to identify hotspots through comparing their spatial patterns [9]. ...
Integrating biodiversity and ecosystem services (BES) has been viewed as an appropriate approach to identifying conservation priorities. Taking Xishuangbanna tropical region in Southwest China, different BESs (habitat quality [used as a proxy for biodiversity], carbon storage, and water yield) were quantified using the InVEST model and conservation hotspots from 1976, 1990, and 2010 were identified by overlapping and ranking the service layers. Results showed that BESs areas were unevenly distributed. High habitat quality and carbon storage areas located in the eastern part of the region were mainly occupied by broad-leaved forest, while high water yield areas were covered by grassland and tropical forests. Recognized hotspots were primarily composed of the broad-leaved forest and shrub grassland. However, these habitat types declined by nearly 50% from 1.25×10⁵ ha to 0.63×10⁵ ha and became more fragmented during the study period. We also found that the sub-watersheds which decreased in BES had fewer hotspots distributed and suffered greater landscape fragmentation. Our study further explored the impacts of land-use conversion on BES, and illustrated the necessity and feasibility of BESs in identifying potential conservation areas.
... Moreover, remote sensing and GIS techniques widely used in the assessment of forest disturbance, development of forest fragmentation and prioritization maps for the conservation of forests (Roy and Tomar, 2000;Martínez Ramos et al., 2016;). Several studies are taken up on forest disturbance, geospatial modeling and in the construction of conservation prioritization maps (Pressey and Bottrill, 2008;Nackoney and Williams, 2012). ...
Conservation of forestlands in West Singhbhum
District of, Jharkhand State, is increasingly
important because of enhanced pressure and
deforestation. The present study was attempted
to generate an accurate and reliable
conservation prioritization map for restoration/
management activities by analyzing
spatiotemporal patterns of landuse and land
cover (LULC) change occurred in the part of
Porahat forest division over the past three
decades (1975 - 2015). Patterns of forest
disturbance and the level of fragmentation were
geospatially analyzed on the distance from
roads, settlements, topography (slope, aspect,
elevation).The results revealed that the forest
cover was decreased from 60.5% (59,616 ha)
in 1975 to 52% (51,257 ha) during 2015.
However, there was an increase in non-forest
and water bodies by 8.1% (8,045 ha) and
0.49% (314 ha), respectively compared to 1975
statistics. The prioritized conservation map was
generated with an accuracy of 82 percent
employing the GIS modeling. It was observed
that about 22% of existing forest lands was
highly disturbed. These disturbed patches need
special attention/prioritization for conservation
and management. The generated spatial pattern
of conservation prioritization map of this study
can be utilized in the regeneration of natural
species and the sustainable development of
forest lands.
... Deforestation hotspots have been used to synthesize information regarding forest cover changes on a global scale (Lepers et al. 2005) and regional scale in tropical forests (Achard et al. 2002) through a combination of coarse to medium resolution images or national statistics. A critical component of the systematic conservation planning targets and areas of high conservation priority includes an accounting of vulnerability and anthropogenic threat (Nackoney and Williams, 2012). Hotspot analysis allows identification of local areas with high concentration of a phenomenon within the landscape (Asubonteng, 2007). ...
This study has assessed forest cover changes, rate of deforestation, levels of fragmentation and modeled the spatial dynamics of deforestation and forest fragmentation in Assam, India. The analysis of the forest change shows that between 1975 and 2015, the area coverage of forest has reduced from 23749 km2 (30.3% of the state geographical area) to 21145 km2 (27% of the state geographical area). The total predicted forest area of Assam is 20189 km2 (25.7%) for 2030. Forest and non-forest classes were evaluated in order to assess changes and to study influence of five physical factors—distance from road and settlement areas, elevation, slope and distance from the water bodies on the deforestation. The Multi-Layer Perceptron neural network modeling was used for predicting the spatial distribution of deforestation. According to the Cramer coefficients, the elevation was strongly associated with deforestation in Assam followed by the slope, distance to water bodies and the distance to roads. The forest areas undergoing high concentration of negative changes have been modelled in the form of deforestation hotspots by using optimised hotspot tool. The results of fragmentation analysis have indicated the reduction of core forests throughout the study period. The spatial database generated in the study is a useful input for understanding of the impact of deforestation and fragmentation on biodiversity and land use change.
