Grassroots Vol 21 No 4 | Page 32

NEWS

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offers great promise to meet these challenges . This discipline uses growing datasets and innovative analytical methods to tackle important questions in biodiversity science and management . Statistical ecology offers opportunities for African researchers to develop local solutions to the continent ’ s ecological challenges . It is currently a fast-developing field , even in Africa where it is led mostly by active research groups in South Africa .
Our aim at the centre for Statistics in Ecology , Environment and Conservation at the University of Cape Town is
to answer important ecological questions using cutting edge statistical methods . The case studies below , in
which researchers at the centre are involved , illustrate the potential of this
exciting field .
Case studies of statistical ecology in Africa
The South African Biodiversity Data Pipeline for Wetlands and Waterbirds is a clear example of a project that can make an impact on conservation . This collaborative project led by the South African National Biodiversity Institute collates data from citizen science bird monitoring programmes to determine the state of waterbird populations and wetlands . Information about population trends and species distribution is critical for conservation managers . The project will transform raw data into usable indicators and display the results online for anyone to see . It has the potential to inform decisions and policies .
Statistical ecology can also help limit poaching . From rhinos and elephants to abalone and cycads , wildlife trade is a threat to African biodiversity .
A recent study by researchers analysed data collected by rangers to identify elephant poaching hotspots . Across the African continent , tens of thousands of wildlife rangers patrol wide areas every day , helping track biodiversity and threats to it . The challenge is that the locations of elephant carcasses
they detect may reflect patrol patterns rather than true poaching patterns . The researchers used tailored statistical techniques to correct this bias and show where poaching was actually
concentrated within their Zimbabwean study site .
Sometimes , researchers need to use refined techniques to gather reliable data , particularly when the species is difficult to detect . For instance , acoustic monitoring was used to keep track of the population of the Cape Peninsula moss frog . Researchers placed microphones at the study sites to record sounds from the environment . Then , they used automated sound recognition software to distinguish calls from the moss frogs . Frog abundance could be estimated from the frequency and location of calls using innovative statistical models . These imaginative procedures allowed them to monitor the population of this threatened endemic species without the need for specialist field staff .
Challenges and the way forward
Despite these promising examples , statistical ecology has yet to reach its potential in Africa . Large gaps remain in African biodiversity data , linked to limited local research funding and government support in many countries . Citizen science and remote sensing are exciting options for addressing these limitations at a relatively low cost , yet specialised skills are needed to analyse these data .
There is a promising trend of growing research and training in statistical ecology in Africa , but many institutions lack capacity and resources .
Researchers from the Global-North working on African systems should try to collaborate more meaningfully with African institutions to help address these gaps . This is critical to enriching the way data informs decisions in African biodiversity management and policy .
There ’ s a unique opportunity next year to share knowledge , build capacity , and create a long-term collaboration network . Our centre in Cape Town is hosting the International Statistical Ecology Conference , a flagship event in the field . We encourage Africans working in this space to submit an abstract .
Figure 2 . The recent development of the field of statistical ecology as compiled from Web of Science ( a ) per publications worldwide , and ( b ) per institutions working on African data . African institutions are shown in orange , although others have delegations in Africa . ( by Henintsoa Onivola Minoarivelo )
31 Grassroots Vol 21 No 4 December 2021