Rules of habitat use by elephants Loxodonta africana in southern Africa: insights for regional management

Harris, Grant; Russell, G. B.; Aarde, Rudi I. van; Pimm, Stuart L.

doi:10.1017/s0030605308000483

Cited by 90 publications

(116 citation statements)

References 15 publications

(25 reference statements)

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“…In general, the wet season habitat selection patterns of male and female elephants were similar and supported the relevance of previously reported habitat covariates (see Harris et al 2008;Hoare and Du Toit 1999;Loarie et al 2009a;Roever et al 2012;Wall et al 2006). Both sexes had a nonlinear relationship with water, selecting for areas both near and far from water relative to availability (Table 2).…”

Section: Discussionsupporting

confidence: 85%

“…These included surface water (de Beer et al 2006;Harris et al 2008), slope (Wall et al 2006), vegetation cover (Harris et al 2008;Loarie et al 2009a), and human presence (Harris et al 2008;Hoare and Du Toit 1999;Jackson et al 2008). Wet season surface water was located using data from Tracks4Africa (2010) and man-made watering point data provided by conservation authorities.…”

Section: Habitat Covariatesmentioning

confidence: 99%

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Functional connectivity within conservation networks: Delineating corridors for African elephants

Roever

Aarde

Leggett

2013

Biological Conservation

100

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Managing multiple parks, reserves, and conservation areas collectively as conservation networks is a recent, yet growing trend. But in order for these networks to be ecologically viable, the functional connectivity of the landscape must be ensured. We assessed the connectivity between six African savanna elephant populations in southern Africa to test whether existing conservation networks were functioning and to identify other areas that could benefit from being managed as conservation networks. We used resource selection function models to create an index of habitat selection by males and female elephants. We employed this habitat use index as a resistance surface, and applied circuit theory to assess connectivity between adjacent elephant populations within six clusters of protected areas across southern Africa. Circuit theory current flow maps predicted a high likelihood of connectivity in the central portion of our study area (i.e. between the Chobe, Kafue, Luangwa, and Zambezi cluster). Main factors limiting connectivity across the study area were high human density in the east and a lack of surface water in the west. These factors effectively isolate elephants in the Etosha cluster in Namibia and Niassa clusters in Mozambique from the central region. Our models further identified two clusters where elephants might benefit from being managed as part of a conservation network, 1) northern Zambia and Malawi and 2) northern Mozambique.We conclude that using habitat selection and circuit theory models to identify conservation networks is a data-based method that can be applied to other focal species to identify and conserve functional connectivity.

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Section: Discussionsupporting

confidence: 85%

Section: Habitat Covariatesmentioning

confidence: 99%

Functional connectivity within conservation networks: Delineating corridors for African elephants

Roever

Aarde

Leggett

2013

Biological Conservation

100

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“…The dry season is the limiting season for this mega-herbivore (Illius, 2006;Trimble et al, 2009), so we consider the dry season best reflects limitations in habitat suitability and ultimately best relates to fitness. Habitat selection patterns are likely to vary in the core wet season (Young et al, 2009), when elephants shift their diet to consume more grasses (Cerling et al, 2006;Codron et al, 2011) and are less dependent on permanent water sources (Harris et al, 2008;Young et al, 2009). Based on our knowledge of elephant use patterns in the area from GPS collar data, we suspect that areas designated as "non-habitat" will be most influenced, particularly in NG-north.…”

Section: Discussionmentioning

confidence: 99%

Incorporating mortality into habitat selection to identify secure and risky habitats for savannah elephants

Roever

Aarde

Chase

2013

Biological Conservation

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Empirical models of habitat selection are increasingly used to guide and inform habitat-based management plans for wildlife species. However, habitat selection does not necessarily equate to habitat quality particularly if selection is maladaptive, so incorporating measures of fitness into estimations of occurrence is necessary to increase model robustness. Here, we incorporated spatially explicit mortality events with the habitat selection of elephants to predict secure and risky habitats in northern Botswana. Following a two-step approach, we first predict the relative probability of use and the relative probability of mortality based on landscape features using logistic regression models. 2Combining these two indices, we then identified low mortality and high use (primary habitat) and areas of high mortality and high use (primary risk). We found that mortalities of adult elephants were closely associated with anthropogenic features, with 80% of mortalities occurring within 25 km of people.Conversely, elephant habitat selection was highest at distances of 30 to 50 km from people. Primary habitat for elephants occurred in the central portion of the study area and within the Okavango Delta; whereas risky areas occurred along the periphery near humans. The protected designation of an area had less influence on the proportion of prime habitat therein than did the locations of the area in relation to human development. Elephant management in southern Africa is moving towards a more self-sustaining, habitat-based approach, and information on selection and mortality could serve as a baseline to help identify demographic sources and sinks to stabilize elephant demography.

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“…Walruses (Odobenus rosmarus) aggregate at terrestrial haul-out sites (Lydersen et al, 2008) and elephants congregate at water sources (Harris et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Sampling designs matching species biology produce accurate and affordable abundance indices

Harris

Farley

Russell

et al. 2013

Preprint

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Wildlife biologists often use grid-based designs to sample animals and generate abundance estimates. Although sampling in grids is theoretically sound, in application, the method can be logistically difficult and expensive when sampling elusive species inhabiting extensive areas. These factors make it challenging to sample animals and meet the statistical assumption of all individuals having an equal probability of capture. Violating this assumption biases results. Does an alternative exist? Perhaps by sampling only where resources attract animals (i.e. targeted sampling), it would provide accurate abundance estimates more efficiently and affordably. However, biases from this approach would also arise if individuals have an unequal probability of capture, especially if some failed to visit the sampling area.Since most biological programs are resource limited, and acquiring abundance data drives many conservation and management applications, it becomes imperative to identify economical and informative sampling designs. Therefore, we evaluated abundance estimates generated from grid and targeted sampling designs using simulations based on geographic positioning system (GPS) data from 42 Alaskan brown bears (Ursus arctos). Migratory salmon drew brown bears from the wider landscape, concentrating them at anadromous streams. This provided a scenario for testing the targeted approach. Grid and targeted sampling varied by trap amount, location (traps placed randomly, systematically or by expert opinion), and traps stationary or moved between capture sessions. We began by identifying when to sample, and if bears had equal probability of capture. We compared abundance estimates against seven criteria: bias, precision, accuracy, effort, plus encounter rates, and probabilities of capture and recapture. One grid (49 km2 cells) and one targeted configuration provided the most accurate results. Both placed traps by expert opinion and moved traps between capture sessions, which raised capture probabilities. The grid design was least biased (-10.5%), but imprecise (CV 21.2%), and used most effort (16,100 trap-nights). The targeted configuration was more biased (-17.3%), but most precise (CV 12.3%), with least PeerJ PrePrints | https://peerj.com/preprints/129v1/ | v1 received: 27 Nov 2013, published: 27 Nov 2013, doi: 10.7287/peerj.preprints.129v1PrePrints effort (7,000 trap-nights). Targeted sampling generated encounter rates four times higher, and capture and recapture probabilities 11% and 60% higher than grid sampling, in a sampling frame 88% smaller. Bears had unequal probability of capture with both sampling designs, partly because some bears never had traps available to sample them. Hence, grid and targeted sampling generated abundance indices, not estimates. Overall, targeted sampling provided the most accurate and affordable design to index abundance. Targeted sampling may offer an alternative method to index the abundance of other species inhabiting expansive and inaccessible landscapes elsewhere, provided their attrac...

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Rules of habitat use by elephants Loxodonta africana in southern Africa: insights for regional management

Cited by 90 publications

References 15 publications

Functional connectivity within conservation networks: Delineating corridors for African elephants

Functional connectivity within conservation networks: Delineating corridors for African elephants

Incorporating mortality into habitat selection to identify secure and risky habitats for savannah elephants

Sampling designs matching species biology produce accurate and affordable abundance indices

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