Towards Integrated Decision Making for Elephant Management

Herbivory by megaherbivores on woody vegetation in general is well documented; however studies focusing on the individual browsing effects of both mega- and mesoherbivore species on recruitment are scarce. We determined these effects for elephant Loxodonta africana and nyala Tragelaphus angasii in the critically endangered Sand Forest, which is restricted to east southern Africa, and is conserved mainly in small reserves with high herbivore densities. Replicated experimental treatments (400 m2) in a single forest patch were used to exclude elephant, or both elephant and nyala. In each treatment, all woody individuals were identified to species and number of stems, diameter and height were recorded. Results of changes after two years are presented. Individual tree and stem densities had increased in absence of nyala and elephant. Seedling recruitment (based on height and diameter) was inhibited by nyala, and by elephant and nyala in combination, thereby preventing recruitment into the sapling stage. Neither nyala or elephant significantly reduced sapling densities. Excluding both elephant and nyala in combination enhanced recruitment of woody species, as seedling densities increased, indicating that forest regeneration is impacted by both mega- and mesoherbivores. The Sand Forest tree community approached an inverse J-shaped curve, with the highest abundance in the smaller size classes. However, the larger characteristic tree species in particular, such as Newtonia hildebrandtii, were missing cohorts in the middle size classes. When setting management goals to conserve habitats of key importance, conservation management plans need to consider the total herbivore assemblage present and the resulting browsing effects on vegetation. Especially in Africa, where the broadest suite of megaherbivores still persists, and which is currently dealing with the ‘elephant problem’, the individual effects of different herbivore species on recruitment and dynamics of forests and woodlands are important issues which need conclusive answers.

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“…Woodland populations are believed to benefit from a release from browsing pressure by megaherbivores [23], [36], [60]. Our findings (cf.…”

Section: Discussionsupporting

confidence: 50%

The Effects of Herbivory by a Mega- and Mesoherbivore on Tree Recruitment in Sand Forest, South Africa

et al. 2011

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“…Previously, MaxEnt has been used to predict the distributions of entire species rather than for modeling the distribution in a small part of the total range (Murray-Smith et al 2008, Benito et al 2009), but we believe that there is no reason why it cannot be used at a finer scale, provided a sufficient breadth of the determining niche axes are sampled. Increasingly, protected area managers are setting targets for population size (Parrish et al 2003), or establishing population 'thresholds of concern' that trigger management actions (Biggs et al 2008). Niche models not only facilitate better prediction of where a species may occur both within and outside of protected areas, thereby enabling more efficient censusing and monitoring (van Wilgen et al 1998, Gillson andDuffin 2007), but also enhance predictions of where optimal habitats occur, thus enabling management to implement actions that facilitate higher recruitment and survival of species threatened with extirpation.…”

Section: Introductionmentioning

confidence: 99%

Using Maximum Entropy modeling to predict the potential distributions of large trees for conservation planning

et al. 2012

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Citation: Smith, A., B. Page, K. Duffy, and R. Slotow. 2012. Using Maximum Entropy modeling to predict the potential distributions of large trees for conservation planning. Ecosphere 3(6):56. http://dx.doi.org/10.1890/ES12-00053.1Abstract. Large trees, as keystone structures, are functionally important in savanna ecosystems, and low recruitment and slow growth makes their conservation important. Understanding factors influencing their distribution is essential for mitigation of excessive mortality, for example from management fires or large herbivores. We recorded the locations of large trees in Hluhluwe-Imfolozi Park (HiP) using GPS to record trees along 43 km of 10 m-wide transects. Maximum entropy modeling (MaxEnt) uses niche modeling to predict the distribution of a species from the probability of finding it within raster squares, based on environmental variables and recorded locations. MaxEnt is typically applied at a regional spatial scale, and here we assessed its usefulness when predicting the distribution of species at a small (local) scale. HiP has variable topography, heterogeneous soils, and a strong rainfall gradient, resulting in a wide variety of habitat types. We used locations of 179 Acacia nigrescens and 106 Sclerocarya birrea (large trees ! 5m), and raster environmental layers for: aspect, elevation, geology, annual rainfall, slope, soil and vegetation. A. nigrescens was largely restricted to the Imfolozi section, while S. birrea had a wider distribution across the reserve. Understanding the interaction of environmental variables dictating tree distribution may facilitate habitat restoration, and will assist planning decisions for persistence of large trees within reserves, including options to reduce fire frequency or herbivore impacts. Though the AUC (Area Under the Curve) values used to test model predictions were high for both species, the ground truthing test data showed that distribution for A. nigrescens was more accurate than that for S. birrea, highlighting the need for independent test data to assess model accuracy. We emphasize that MaxEnt can be used at finer spatial scales than those typically used for species occurrence, but models must be tested using spatially independent test data.

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“…Like most conservation practitioners (see Cook et al, 2010), elephant managers face a myriad of often competing ecological, economic and social considerations (Biggs et al, 2008). Elephants are desirable in many protected areas and private and communal lands in Africa due to their considerable economic, ecological, cultural and aesthetic value (see Blignaut et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Science and elephant management decisions in South Africa

Young

Aarde

2011

Biological Conservation

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a b s t r a c tMost conservation practitioners rely on experience-based information for management decisions. In South Africa, government policy thus directs managers to base decisions for elephant management on the best available scientific information. The reliance of their management decisions on science, however, is unknown. We interviewed 30 managers from small to large protected areas in South Africa to evaluate whether science underpinned strategic and applied management decisions regarding elephants. We also evaluated their perspectives on limitations to, and opportunities for, greater reliance on science. Twentynine managers valued science as a platform for their decisions. However, most managers based decisions on experience-based information. Only 28%, 30% and 8% of managers respectively developed objectives, identified issues and selected management methods on science-based information. Furthermore, only 30% selected a desired number of elephants, and 5% selected a population control method, according to science-based information. The probability of managers basing decisions on science-based information differed with management area size, tenure, planning timeframes and manager experience. However, except for experience, these relationships were weak, and overall an implementation gap between science and elephant management existed. Limitations for science to inform decisions included lack of relevance and application of science findings to respective management areas, and scientists' attitudes and credibility. Opportunities included more site specific studies, better communication of, and easier access to scientific material. These opportunities could enable scientists to contribute more effectively to elephant management decisions and improve the reliance on science among the wider community of conservation practitioners in Africa.

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Towards Integrated Decision Making for Elephant Management

Cited by 21 publications

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The Effects of Herbivory by a Mega- and Mesoherbivore on Tree Recruitment in Sand Forest, South Africa

The Effects of Herbivory by a Mega- and Mesoherbivore on Tree Recruitment in Sand Forest, South Africa

Using Maximum Entropy modeling to predict the potential distributions of large trees for conservation planning

Science and elephant management decisions in South Africa

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