Application of open source tools for biodiversity conservation and natural resource management in East Africa
Introduction
The application of innovative technologies to biodiversity conservation and natural resource management is fast gaining ground in Africa, with local communities becoming actively involved (Herrick et al., 2013). Innovations such as mobile money transfers have enabled pastoralist communities to bank and transfer funds as well as use their phones to gather and share knowledge on rangelands conditions such as pasture health, disease prevalence and water availability (Herrick et al., 2013). Decisions made to move livestock in search of water, forage and best market prices are usually based on such information (Oba, 2012), now easy to upload and access on mobile platforms.
The availability of user friendly open source software and the increasing broad internet connectivity across Kenya (Macharia, 2014), sometimes powered by solar energy, provides a unique platform that enables the incorporation of scientifically based conservation strategies into traditional conservation methods practiced for millennia. While information dissemination has begun at different levels across Africa's rangelands (Reid et al., 2016), there are few examples of data collection, processing, interpretation and application at a local community level. This is partly due to a lack of user friendly analysis tools and technical knowledge among community members, which point to reduced research output at the local level.
Here we use more than forty years of animal and plant data collected in the Amboseli ecosystem, and more recently the Magadi ecosystem, in Kajiado County, southern Kenya (Fig. 1), to demonstrate the application of open source tools to biodiversity conservation and natural resource management at the community level. We show how massive long-term data collected in the two ecosystems can be rapidly analyzed and presented using a set of customized open source tools. We provide examples on how these tools support quick community decision making on grazing management and provide insights on seasonal wildlife distributions and vegetation biomass (g/m2) shortfalls in relation to local drought conditions (Western et al., 2015b).
The highly interactive tools developed in R (R Core Team, 2016), an open source statistical computing software with spatial analysis capabilities, contain a series of commands that perform various data tasks including: examining the data structure, aggregation, exploratory data analysis, calculation of vegetation biomass (g/m2), percentage grass greenness and grazing pressure estimations, Normalized Difference Vegetation Index (NDVI) extraction, species population estimates and distribution mapping, among other functions. The tools are customized to suit the local community data needs and level of expertise without losing the underlying data richness and integrity.
The open source tools require basic computer knowledge and can be executed by local community trainees using straight forward computer commands. The digital platforms allow biodiversity scenario building, model formulation such as the identification of migratory corridors, habitat utilization by animal species, and application to county spatial planning programs (Mose and Western, 2015).
Section snippets
Study area and methods
The Amboseli and Magadi ecosystems (Fig. 1) have a combined area of 9500 km2 and support a large population of livestock, wildlife and pastoralists (Groom and Western, 2013) freely moving over the rangelands extending into Northern Tanzania. The protected 388 km2 Amboseli National Park is centrally located in the Amboseli ecosystem and acts as refuge for wildlife during droughts (Western and Lindsay, 1984). The pastoralist in the Magadi area have not settled permanently, in contrast to the
The application of the tool to vegetation monitoring and habitat changes
Vegetation monitoring data are processed by the open source platform once collected. The pasture biomass (g/m2) trends and incidences of extreme conditions (below red dotted line) are shown in Fig. 3. The period preceding the 2009 drought was characterized by continuously low biomass (g/m2). The same downward trend characterized the build up to the late 2016 and early 2017 drought conditions.
Fig. 4 shows the vegetation biomass (g/m2) estimates in the Amboseli ecosystem processed by the spatial
Discussion
The results of our study showing examples of outputs from a suite of open source tools that are customized for local communities' use, provide a rapid assessment of socioeconomic and ecological resources in the pastoral rangelands of East Africa. The current slow pace of delivering information and a lack of visualization tools for easy viewing and interpretation by non-technical decision-makers hinders its application to conservation planners and managers (Sarkar et al., 2006). A quick delivery
Conclusion
Decision support in biodiversity conservation requires an effective combination of analysis tools and expertise usually unavailable locally. The formulation of these tools calls for expanded infrastructure and understanding complex ecological process (Sarkar et al., 2006). Once developed, the applications are straight forward. The work presented here and applied to conservation and development planning in the Amboseli and Magadi ecosystems has encouraged other institutions, including the
Acknowledgment
We thank Lucy Waruingi, the executive director of African Conservation Centre, the staff of Amboseli Conservation Program, South Rift Association of Land Owners and Department of Resource Surveys and Remote Sensing, for their support over the years. Two anonymous reviewers provided helpful comments. Liz Claiborne Ortenberg Foundation: www.lcaof.org (Grant Number: LCAOF-ACP2014) funded the data collection.
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