Determining baselines for human-elephant conflict: A matter of time

Pozo, Rocío Alfaro; Coulson, Tim; McCulloch, Graham; Stronza, Amanda; Songhurst, Anna

doi:10.1371/journal.pone.0178840

Cited by 41 publications

(60 citation statements)

References 33 publications

(40 reference statements)

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“…Crop raiding is the most prevalent type of HEC in Africa and Asia, and is increasing sharply with the spread of farms into elephant range areas such as Amboseli (Graham, Notter, Adams, Lee, & Ochieng, ; Pozo, Coulson, McCulloch, Stronza, & Songhurst, ). This conflict has led to an increase in human and elephant fatalities across much of Africa (Gadd, ; Lindsey et al., ; Okello, ; Western & Waithaka, ).…”

Section: Discussionmentioning

confidence: 99%

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Fencing solves human‐wildlife conflict locally but shifts problems elsewhere: A case study using functional connectivity modelling of the African elephant

Osipova

Okello

Njumbi³

et al. 2018

Journal of Applied Ecology

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Fencing is one of the most common methods of mitigating human‐wildlife conflicts. At the same time, fencing is considered one of the most pressing threats emerging in conservation globally. Although fences act as barriers and can cause population isolation and fragmentation over time, it is difficult to quantitatively predict the consequences fences have for wildlife. Here, we model how fencing designed to mitigate human‐elephant conflict (HEC) on the Borderlands between Kenya and Tanzania will affect functional connectivity and movement corridors for African elephants. Specifically, we (a) model functional landscape connectivity integrating natural and anthropogenic factors; (b) predict seasonal movement corridors used by elephants in non‐protected areas; and (c) evaluate whether fencing in one area can potentially intensify human‐wildlife conflicts elsewhere. We used GPS movement and remote sensing data to develop monthly step‐selection functions to model functional connectivity. For future scenarios, we used an ongoing fencing project designed for HEC mitigation within the study area. We modelled movement corridors using least‐cost path and circuit theory methods, evaluated their predictive power and quantified connectivity changes resulting from the planned fencing. Our results suggest that fencing will not cause landscape fragmentation and will not change functional landscape connectivity dramatically. However, fencing will lead to a loss of connectivity locally and will increase the potential for HEC in new areas. We estimate that wetlands, important for movement corridors, will be more intensively used by the elephants, which may also cause problems of overgrazing. Seasonal analysis highlights an increasing usage of non‐protected lands in the dry season and equal importance of the pinch point wetlands for preserving overall function connectivity. Synthesis and applications. Fencing is a solution to small‐scale human‐elephant conflict problems but will not solve the issue at a broader scale. Moreover, our results highlight that it may intensify the conflicts and overuse of habitat patches in other areas, thereby negating conservation benefits. If fencing is employed on a broader scale, then it is imperative that corridors are integrated within protected area networks to ensure local connectivity of affected species.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Fencing solves human‐wildlife conflict locally but shifts problems elsewhere: A case study using functional connectivity modelling of the African elephant

Osipova

Okello

Njumbi³

et al. 2018

Journal of Applied Ecology

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“…This trade‐off decision between exploring risky habitat versus attractive forage becomes evident in situations like elephant crop consumption, as elephants risk travelling in human‐dominated landscapes in order to consume highly attractive agricultural crops or access other in need resources (Mumby & Plotnik, ). Elephant crop consumption is both an issue of conservation and of farmer livelihood concern, and is especially evident in areas of high elephant and high human densities, such as northern Botswana (Gupta, ; Mackenzie & Ahabyona, ; Osborn, ; Pozo, Coulson, McCulloch, Stronza, & Songhurst, ; Songhurst, ; Songhurst & Coulson, ). Here agricultural fields tend to coincide in space with other attractive forage and water sources (Jackson, Mosojane, Ferreira, & van Aarde, ; Songhurst, McCulloch, & Coulson, ), which complicates efforts to tease apart the influence of vegetation and water on elephant habitat selection and movement patterns (Chamaillé‐Jammes, Mtare, Makuwe, & Fritz, ; Duffy, Dai, Shannon, Slotow, & Page, ; Harris, Russell, Van Aarde, & Pimm, ).…”

Section: Introductionmentioning

confidence: 99%

“…Elephant crop consumption is both an issue of conservation and of farmer livelihood concern, and is especially evident in areas of high elephant and high human densities, such as northern Botswana (Gupta, 2013;Mackenzie & Ahabyona, 2012;Osborn, 2004;Pozo, Coulson, McCulloch, Stronza, & Songhurst, 2017;Songhurst, 2012;Songhurst & Coulson, 2014). Here agricultural fields tend to coincide in space with other attractive forage and water sources (Jackson, Mosojane, Ferreira, & van Aarde, 2008;Songhurst, McCulloch, & Coulson, 2015), which complicates efforts to tease apart the influence of vegetation and water on elephant habitat selection and movement patterns (Chamaillé-Jammes, Mtare, Makuwe, & Fritz, 2013;Duffy, Dai, Shannon, Slotow, & Page, 2011;Harris, Russell, Van Aarde, & Pimm, 2008).…”

Section: Introductionmentioning

confidence: 99%

Exploring movement decisions: Can Bayesian movement‐state models explain crop consumption behaviour in elephants (Loxodonta africana)?

Vogel

Lambert

Songhurst

et al. 2020

Journal of Animal Ecology

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Animal movements towards goals or targets are based upon either maximization of resource acquisition or risk avoidance, and the way animals move can reveal information about their motivation. We use hidden Markov models (HMMs) fitted in a Bayesian framework and hourly Global Positioning System fixes to distinguish animal movements into distinct states and analyse the influence of environmental variables on being in, and switching to, a particular state. Specifically, we apply our models to understand elephant movement decisions around agricultural fields, and crop consumption. As it is unclear what the role of habitat features are on this complex process, we analyse whether elephants target agricultural crops for consumption, or simply pass through them in search of water. Our HMMs separate elephant movements into two states: exploratory movements that are fast and directional, and encamped movements that are slow and meandering. For each elephant, we ran 16 models with each possible combination of selected habitat features (river, elephant corridor, agricultural field, trees), and repeated these analyses including interaction effects with both season and time of day. We used cross‐validation to select the best model. In corridors, exploratory movements are dominant. Elephants mainly showed encamped movements at the river during the dry season, when temporary water sources have dried out and elephants relied on this permanent water source. In fields, males most often exhibited exploratory movements to and from the river, while females showed an increase in the frequency of encamped behaviour during the dry season and at night—the times when most crop consumption and movements through fields occur. Adaptation to risk could explain this behaviour, since foraging in fields is likely less risky under the cover of darkness and during the dry season when farmers are absent. This sex segregation in elephant movement decisions highlights the importance of predation risk in shaping movement patterns, which can result in sex segregation in responses to mitigation methods. The increase in encamped movements in the dry season suggests the importance of agricultural timing, and shows the potential for early ploughing and early‐harvest crop types in order to reduce elephant crop consumption. Taking this into account could increase efficiency of elephant crop consumption mitigation.

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“…can thereby inform management decisions, even given competing management objectives caused by conservation conflict (e.g. Fox & Madsen, ; Pozo, Coulson, Mcculloch, & Stronza, ; Redpath et al., ; Strand, Nilsen, Solberg, & Linnell, ; Sundt‐Hansen, Huisman, Skoglund, & Hindar, ). In addition, GMSE can be used to explore general questions concerning management theory such as the following: How is population persistence affected by management frequency or observation intensity?…”

Section: Introductionmentioning

confidence: 99%

GMSE: An r package for generalised management strategy evaluation

et al. 2018

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Management strategy evaluation (MSE) is a powerful tool for simulating all key aspects of natural resource management under conditions of uncertainty. We present the r package generalised management strategy evaluation (GMSE), which applies genetic algorithms to provide a generalised tool for simulating adaptive decision‐making management scenarios between stakeholders with competing objectives under complex social‐ecological interactions and uncertainty. GMSE models can be agent‐based and spatially explicit, incorporating a high degree of realism through mechanistic modelling of links and feedbacks among stakeholders and with the ecosystem; additionally, user‐defined sub‐models can also be incorporated as functions into the broader GMSE framework. We show how GMSE simulates a social‐ecological system using the example of an adaptively managed waterfowl population on an agricultural landscape; simulated waterfowl exploit agricultural land, causing conflict between conservation interests and the interest of food producers maximising their crop yield. The r package GMSE is open source under GNU Public License; source code and documents are freely available on GitHub.

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Determining baselines for human-elephant conflict: A matter of time

Cited by 41 publications

References 33 publications

Fencing solves human‐wildlife conflict locally but shifts problems elsewhere: A case study using functional connectivity modelling of the African elephant

Fencing solves human‐wildlife conflict locally but shifts problems elsewhere: A case study using functional connectivity modelling of the African elephant

Exploring movement decisions: Can Bayesian movement‐state models explain crop consumption behaviour in elephants (Loxodonta africana)?

GMSE: An r package for generalised management strategy evaluation

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