FUNCTIONAL CONNECTIVITY WITHIN CONSERVATION NETWORKS: DELINEATING CORRIDORS FOR AFRICAN ELEPHANTS

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General Introduction

Wildlife population management is often a central component of both theoretical and applied ecological research. Ecologists are confronted with problems of augmenting small, isolated populations (e.g. Hedrick and Fredrickson 2010; Meretsky et al. 2000) and of reducing largepopulations that are damaging their resource base (e.g. Fortin et al. 2005; McCullough et al.
1997). With African savannah elephants (Loxodonta africana), both of these problems occur;populations that are exposed to poaching can quickly become diminished (Barnes 1999; Barnes and Kapela 1991; Ntumi et al. 2009); whereas, those in protected parks that are given supplemental water can boom and alter woodland structure (Asner et al. 2012; Dunham 1988; Skarpe et al. 2004). Managing elephant population demography then becomes costly and time consuming for managers and can place stress on the animals through culling, relocation, and immunocontraception (i.e. Kerley and Shrader 2007; Pimm and van Aarde 2001; van Aarde et al.
2006). To alleviate some of these management issues, the idea of managing elephants as a metapopulation was first introduced by van Aarde and Jackson (2007). The metapopulation theory was first developed for small, discrete, and isolated populations (Hanski 2004), but has since grown into a potential management scheme to regulate populations and maintain connectivity. For large mammals, the metapopulation concept relies on having discrete local breeding populations with asynchrony in demography and dispersal between populations (Driscoll 2007; Olivier et al. 2009). Source populations supplement the mortality in sink areas, resulting in a net stable population (Pulliam 1988; van Aarde and Jackson 2007). Managing large mammals in this manner is rare due to their large spatial requirements (Olivier et al. 2009), but in southern Africa, the spatial location of parks and the large roaming distances of elephants make this idea feasible (van Aarde and Jackson 2007). However, Armstrong (2005) noted that managing wildlife populations based on metapopulation theory alone could result in a failure to identify positive management strategies and potentially make erroneous management recommendations. For instance, ignoring the quality of habitat patches might cause patches of poor quality to be incorrectly included in the metapopulation network (Armstrong 2005; Battin 2004). Armstrong (2005) therefore recommends a metapopulation concept be integrated with a habitat-based approach, to ensure appropriate management of species across broad spatial scales.

Study Area

The study area comprised a large portion of the distributional range of elephants in southern Africa and included portions of Angola, Botswana, Malawi, Mozambique, Namibia, South Africa, Zambia, and Zimbabwe (Figure 2-1). The region included a desert/grassland mosaic in the west, dry woodlands in the central region, and mesic forest in the east. Seasonal rainfall patterns drive vegetation structure (Sankaran et al. 2005). Mesic regions to the east had greater rainfall and hence greater tree cover compared to western portion of the study area (Table 2-1). Topographic conditions also varied, with more dramatic changes in elevation near the western and eastern coasts and relatively flat, unvarying terrain in the central region. Human density was low over much of the study area, with pockets of increased human densities near major waterways and roadways.

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Elephant Data

Elephants (n=102) were captured and collared with Africa Wildlife Tracking GPS collars (model SM
2000E; Africa Wildlife Tracking, Pretoria, South Africa) between December 2002 and September 2010. Collars were programmed to relocate individuals at varying intervals, ranging from 1 to 24 hours, with most elephants having multiple interval settings during the collaring period. Telemetry locations were divided into two seasons and combined across years. Core wet season was defined as December through March inclusive, and core dry season was defined as June throug September (Young et al. 2009). Locations in the transition months of April, May, October, and November were omitted from the analysis. Only individuals with more than 100 locations per season were retained for the analysis, resulting in 35,167 locations among 86 individuals for the dry season and 43,141 locations among 88 individuals for the wet season analysis.

Habitat Selection Models

Habitat selection was modelled separately for the seven elephant populations and for males and females, with one collared female actually representing a breeding herd with several adult females and their offspring. Elephant locations (1) were compared to randomly generated locations (0) using a mixed effect logistic regression model for location i and individual j, taking the form: w(xij) = exp(β + β1x1ij+ …+ βnxnij + γj), (1) where w(xij) is the resource selection function, βn is the coefficient for the n-th predictor variable xn , and γ is the random intercept for animal j (Gillies et al. 2006; Manly et al. 2002). The random intercept was used to control for the lack of independence of points within individuals and differences in sample size among individuals (Gillies et al. 2006). We implemented a design III approach (Manly et al. 2002; Thomas and Taylor 1990), whereby random locations were generated within the home range of each elephant at a density of 3 points/km2. At this density, contamination (i.e. use and available locations occurring within the same raster pixel) was less than 15% for the habitat covariate mapped at the coarsest resolution (800 x 800 m), and was therefore negligible (Johnson et al. 2006).

Dedication
Abstract
Acknowledgements
CHAPTER 1. GENERAL INTRODUCTION
References
CHAPTER 2. FUNCTIONAL RESPONSES IN THE HABITAT SELECTION OF A GENERALIST MEGA-HERBIVORE, THE AFRICAN SAVANNAH ELEPHANT
Publication Details
Abstract
Introduction
Methods
Results
Discussion
Acknowledgements
References
CHAPTER 3. FUNCTIONAL CONNECTIVITY WITHIN CONSERVATION NETWORKS: DELINEATING CORRIDORS FOR AFRICAN ELEPHANTS
Publication Details
Abstract
Introduction
Methods
Results
Discussion
Acknowledgements
References
CHAPTER 4. INCORPORATING MORTALITY INTO HABITAT SELECTION TO IDENTIFY SECURE AND RISKY HABITATS FOR SAVANNAH ELEPHANTS
Publication Details
Abstract
Introduction
Methods
Results
Discussion
Acknowledgements
References
Appendix A
Appendix B
Appendix C
CHAPTER 5. SIMILARITY IN HABITAT PREFERENCES IMPEDES HUMAN- ELEPHANT COEXISTENCE
Publication Details
Abstract
Introduction
Methods
Results
Discussion
Acknowledgements
References
Appendix A
CHAPTER 6. GENERAL CONCLUSIONS
References