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Moving through the mosaic: identifying critical linkage zones for large herbivores across a multiple‐use African landscape

Abstract

Context

Reduced connectivity across grassland ecosystems can impair their functional heterogeneity and negatively impact large herbivore populations. Maintaining landscape connectivity across human-dominated rangelands is therefore a key conservation priority.

Objective

Integrate data on large herbivore occurrence and species richness with analyses of functional landscape connectivity to identify important areas for maintaining or restoring connectivity for large herbivores.

Methods

The study was conducted on a landscape with a mosaic of multiple land uses in Laikipia County, Kenya. We used occupancy estimates for four herbivore species [African elephant (Loxodonta africana), reticulated giraffe (Giraffa reticulata), plains zebra (Equus quagga), and Grevy’s zebra (Equus grevyi)] and species richness estimates derived from aerial surveys to create resistance surfaces to movement for single species and a multi-species assemblage, respectively. We validated single-species resistance surfaces using telemetry data. We used circuit theory and least cost-path analyses to model linkage zones across the landscape and prioritize areas for connectivity restoration.

Results

Resistance layers approximated the movements of our focal species. Results for single-species and multi-species connectivity models were highly correlated (rp > 0.9), indicating similar spatial patterns of functional connectivity between individual species and the larger herbivore assemblage. We identified critical linkage zones that may improve permeability to large-herbivore movements.

Conclusion

Our analysis highlights the utility of aerial surveys in modeling landscape connectivity and informing conservation management when animal movement data are scarce. Our results can guide management decisions, providing valuable information to evaluate the trade-offs between improving landscape connectivity and safeguarding livelihoods with electrified fences across rangelands.

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Data availability

The datasets and R code used to generate the resistance layers, including occupancy and species richness data, fence dataset, anthropogenic areas and core areas, as well as the GPS data for plains zebra and Grevy’s zebra are available at GitHub repository: https://github.com/Smithsonian/Moving-through-the-mosaic-Identifying-critical-linkage-zones-for-large-herbivores-across-Laikipia.git

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Acknowledgements

Support for this work comes from Breakefield Critical Conservation Fund, Saudi-Aramco, the Mpala Wildlife Foundation, National Geographic Society and Princeton University and the German Research Foundation (DFG, Grant #257734638). We thank all the ranches, conservancies, and wider communities in Laikipia for their engagement and willingness to provide data, access and share information. Specifically, we thank Cosmas Nzomo (Mpala), Sheila Njoroge (Mpala), Samuel Mutisya (Ol Pejeta), Jaimie Gaymer (Ol Jogi), Thomas Silvester (Loisaba), Dido Dima (ADC Mutara), Florence Chepkorir (ADC Mutara) and Sampeke Ole Meshemi (Ol Jogi) for their hospitality, time, logistical support and willingness to share information. We also thank Bernard Gituku for providing shapefiles for Ol Pejeta Conservancy, Ann Wambui for providing information on fences and fence gaps for Mugie Conservancy, Benaiah Odhiambo for providing information on Segera fences and Timothy Kaaria, Research Officer in Lewa Conservancy, for providing maps for Borana and Lewa Conservancies. Finally, we thank all the people that answered our questions on assessing fence permeability.

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Correspondence to Ramiro D. Crego.

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Crego, R.D., Wells, H.B.M., Ndung’u, K.S. et al. Moving through the mosaic: identifying critical linkage zones for large herbivores across a multiple‐use African landscape. Landscape Ecol 36, 1325–1340 (2021). https://doi.org/10.1007/s10980-021-01232-8

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Keywords

  • Aerial surveys
  • Barrier mapper
  • Circuit theory
  • Conservation planning
  • Functional connectivity
  • Least-cost path