Validating movement corridors for African elephants predicted from resistance-based landscape connectivity models
Resistance-based connectivity models are widely used conservation tools for spatial prioritization and corridor planning, but there are no generally accepted methods and recommendations for validating whether these models accurately predict actual movement routes. Hence, despite growing interest and recognition of the importance of protecting landscape connectivity, the practical utility of predictions derived from connectivity models remains unclear.
The difficulties in validations are mainly related to the unavailability of independent data and lack of appropriate, easily applied statistical frameworks. Here, we present a case study where two independently collected datasets were used to validate resistance-based landscape connectivity models and movement corridors identified by these models.
We used annual aerial counts to evaluate the connectivity model, and a field survey to assess the performance of predicted corridors. We applied these two independent datasets to validate a previously developed connectivity model for the African elephant (Loxodonta africana) in the Borderland region between Kenya and Tanzania.
The results of this study confirm that the resistance-based connectivity model is a valid approach for predicting movement corridors for the African elephant. We show that high connectivity values are a strong predictor of the presence of large numbers of the elephants across the years. The probability of observing elephants increased with increasing connectivity values, while accounting for seasonality is an important factor for accurately predicting movements from connectivity models.
Movement corridors derived from resistance-based connectivity models have a strong predictive power and can be successfully used in spatial conservation prioritization.
KeywordsAfrican elephant Conservation planning Resistance surface Landscape connectivity Movement corridors Step-selection function
This research was supported by the European Commission under the Erasmus Mundus Joint Doctorate Programme (FONASO). We would like to thank Alexander Silbersdorff for consulting and comments to the analytical part of this research. We acknowledge Prof. Brendan Wintle for valuable advice on data analysis and interpretation of the results of this study. We would also like to show our gratitude to Edward Masharen Lekina for assistance for data collection and interpretation.
- Bennett AF (2003) Linkages in the landscape: the role of corridors and connectivity in wildlife conservation. IUCN reportGoogle Scholar
- Bolker B, Brooks M, Gardner B, Lennert C, Minami M (2012) Owls example: a zero-inflated, generalized linear mixed model for count data. Departments of Mathematics & Statistics and Biology, McMaster University, HamiltonGoogle Scholar
- Bowman J, Cordes C (2015) Landscape connectivity in the Great Lakes Basin. Wildlife Research and Monitoring Section Ministry of Natural Resources and Forestry, PeterboroughGoogle Scholar
- Buckland ST (2004) Advanced distance sampling. Oxford University Press, OxfordGoogle Scholar
- Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2001) Introduction to distance sampling estimating abundance of biological populations. Oxford University PressGoogle Scholar
- Haddad NM, Tewksbury JJ (2006) Impacts of corridors on populations and communities. Cambridge University Press, Cambridge, pp 390–415Google Scholar
- Hijmans RJ, van Etten J, Cheng J, Mattiuzzi M, Sumner M, Greenberg JA, Lamigueiro OP, Bevan A, Racine EB, Shortridge A, Hijmans MR (2016) Package ‘raster’. R package. ftp://slartibardfast.gtlib.gatech.edu/pub/CRAN/web/packages/raster/raster.pdf. Accessed 17 Feb 2017
- Kioko J, Okello M, Muruthi P (2006) Elephant numbers and distribution in the Tsavo-Amboseli ecosystem, south-western Kenya. Pachyderm 41:53–60Google Scholar
- Manly BFJ, McDonald LL, Thomas DL, McDonald TL, Erickson WP (2002) Resource selection by animals: statistical analysis and design for field studies, 2nd edn. Kluwer, NordrechtGoogle Scholar
- McRae B, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model connectivity in ecology and conservation. Ecology 10:271–272Google Scholar
- McRae BH, Kavanagh DM (2011) Linkage mapper connectivity analysis software. The nature conservancy. Toolbox available at http://www.circuitscape.org/linkagemapper. Accessed 20 Feb 2017
- Meiklejohn K, Ament R, Tabor G (2009) Habitat corridors & landscape connectivity: clarifying the terminology. Center for Large Landscape Conservation, BozemanGoogle Scholar
- Ngene S, Ihwagi F, Nzisa M, Mukeka J, Njumbi S, Omondi P (2011) Total aerial census of elephants and other large mammals in the Tsavo-Mkomazi ecosystem. Nairobi Kenya Kenya Wildl Serv 43:5–56Google Scholar
- Norris D, Peres CA, Michalski F, Hinchsliffe K (2008) Terrestrial mammal responses to edges in Amazonian forest patches: a study based on track stations. Walter de Gruyter, BerlinGoogle Scholar
- Norton-Griffiths M (1978) Handbooks on techniques currently used in African wildlife ecology. AWLF, NairobiGoogle Scholar
- Okello MM, Kenana L, Maliti H, Kiringe JW, Kanga E, Warinwa F, Bakari S, Ndambuki S, Massawe E, Sitati N, Kimutai D, Mwita M, Gichohi N, Muteti D, Ngoru B, Mwangi P (2016) Population density of elephants and other key large herbivores in the Amboseli ecosystem of Kenya in relation to droughts. J Arid Environ 135:64–74CrossRefGoogle Scholar
- R Core Team (2017) R: a language and environment for statistical computing. https://www.R-project.org/. Accessed 18 Dec 2018
- Sanderson J, Da Fonseca GA, Galindo-Leal C, Alger K, Inchausty VH, Morrison K, Rylands A (2006) Escaping the minimalist trap: design and implementation of large-scale biodiversity corridors. Conserv Biol Ser Camb 14:620Google Scholar
- Shah VB, McRae BH (2008) Circuitscape: a tool for landscape ecology. In: Proceedings of the 7th python in science conference, pp 62–66Google Scholar
- Southgate R, Moseby K (2008) Track-based monitoring for the deserts and rangelands of Australia. Prepared for the threatened species network at WWF Australia. Envisage Environmental Services Ecological Horizons, SydneyGoogle Scholar
- Wade AA, McKelvey KS, Schwartz MK (2015) Resistance-surface-based wildlife conservation connectivity modeling: summary of efforts in the United States and guide for practitioners. Gen Tech Rep RMRS-GTR-333 Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station 93, p 333Google Scholar
- Wang Y-H, Yang K-C, Bridgman CL, Lin L-K (2008) Habitat suitability modelling to correlate gene flow with landscape connectivity. Landscape Ecol 23:989–1000Google Scholar
- Western D, FAO R (Italy) eng, Wildlife M of T and, Eng N (Kenya) WC and MD (1976) An aerial method of monitoring large mammals and their environment (with a description of a computer program for survey analyses) - project working document 9Google Scholar
- Zeller KA, McGarigal K, Cushman SA, Beier P, Vickers TW, Boyce WM (2015) Using step and path selection functions for estimating resistance to movement: pumas as a case study. Landscape Ecol 31:1–17Google Scholar