Edited by: Laura Kehoe, University of Victoria, Canada
Reviewed by: Bruce Alexander Schulte, Western Kentucky University, United States; Samrat Mondol, Wildlife Institute of India, India
This article was submitted to Conservation, a section of the journal Frontiers in Ecology and Evolution
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In social-ecological systems around the world, human-wildlife interactions are on the rise, often with negative consequences. This problem is particularly salient in areas where populations of humans and wildlife are increasing and share limited space and resources. However, few studies look at how both people and wildlife navigate shared spaces. To better examine people and wildlife within the same environment, we used methods from social science and spatial ecology to investigate how humans and elephants in Botswana utilize trees, a shared natural resource. Trees provide an opportunity to study shared resource use because they are important for people as firewood and for elephants as food and habitat. We compared tree species gathered on 49 firewood collections with the species damaged by elephants in 83 vegetation plots. We found that many tree species were damaged by elephants in ways that would generate firewood. There was also a strong overlap in the tree species that people collected and the species that elephants browsed and/or damaged. We compared spatially-explicit firewood collection locations and movement data from elephant GPS collars to model resource selection by people and elephants. Proximity to settlements was a strong driving factor for people in firewood collection, while various factors including vegetation characteristics played a role in predicting elephant movement. We found that areas where people collect firewood were negatively correlated with daytime elephant movement and positively correlated with nighttime elephant movement. We further compared the times that people collected firewood with the times when elephants were near the villages and found that people collected firewood during daylight hours when elephants were not nearby, providing further evidence of temporal partitioning. People and elephants utilized the same species of trees, and also had correlated spatial patterns of resource selection. Therefore, elephant foraging of trees provides a previously unrecognized utility to people in the form of firewood creation, and temporal partitioning allows this to occur without direct human-elephant interaction.
In many parts of the world, humans and wildlife increasingly share land and resources outside of protected areas. Studies on human-wildlife interactions within social-ecological systems (SES) often focus on competition for resources or direct loss caused to one species by the other. These interactions are typically examined through the lens of human-wildlife conflict. Studies that focus solely on conflict between wildlife and humans may overlook other possible kinds of interactions, including benefits that one species may provide to the other. As proposed frameworks for coexistence focus on balancing inputs of costs and benefits (Kansky et al.,
There is no species that exemplifies the challenges of human-wildlife conflict like the elephant. Where populations of people and elephants overlap, interactions increase and so does the potential for conflict situations (Sitati et al.,
In places where people live alongside elephants, efforts to reduce impacts of interactions have relied on a variety of mitigation measures that bear significant costs. As examples, farmers may relocate to guard their fields against elephant foraging during the growing season, may change their daily activities to avoid encounters, forgo gathering resources in areas frequented by elephants, or devote money and time to building fences and implementing deterrents (Mayberry et al.,
Trees provide an opportunity to study the demands for shared natural resources by elephants and people. Elephants depend on trees for protection, shade for thermoregulation, and are an important source of food. People depend on the wood from trees for firewood and construction materials for canoes and homes, as well as fruits that can help buffer food insecurity (Mmopelwa et al.,
We integrated approaches from landscape ecology and anthropology to investigate how people and elephants use tree resources in a shared landscape. We hypothesized that because elephants damage trees, which creates downed wood we would find a pattern of resource overlap among the tree species damaged by elephants and the tree species that people rely on for firewood. We also hypothesized that we would find positively correlated spatial patterns of resource use by people and elephants, but temporal resource use would differ.
For most of human history, people have relied on woodfuel, including firewood and charcoal, for energy (Goren-Inbar et al.,
Beginning in the mid-1970s, scholars sounded the alarm over concerns of the “fuelwood gap” (Eckholm,
Studies that observed local or regional-level forest degradation linked to fuelwood harvest have concluded that degradation is due to the intersection of diverse factors, including socio-economic factors like rapid population growth, changing labor supplies, and global markets, as well as environmental factors like low tree density and climactic conditions (Arnold et al.,
Elephants are generalist megaherbivores, and shrubs and trees make up a significant portion of their diet depending on availability (Cerling et al.,
As a consequence of elephants seeking out wooded habitat for foraging, shade, and refuge, they in turn influence that habitat. Extensive research has been done on the effects of elephants on trees; their influence on physical structure, growth, and community composition has been documented throughout Africa (Laws,
A significant by-product of elephant damage to trees is in the generation of downed wood. As mentioned above, this coarse woody debris serves an ecosystem function of their own (Jonsson et al.,
The study site is in the Eastern Panhandle of the Okavango Delta (Panhandle), Botswana, by one of the largest intact wetlands in the world (
Map of the study region in the Eastern Okavango Panhandle, Botswana.
People in the Panhandle live across 14 designated villages and many unofficial settlements. Villages range from a population of 475 people in Tobera to a population of 3,716 people in Seronga, the sub-district capital and the largest village in the study site (Botswana Central Statistics Office,
Firewood collection is labor intensive and depends on factors such as availability, household size, and season, as people use more firewood during cold winters (Gaye,
Mokgacha, a village in the Panhandle, was the main site for our ethnographic and vegetation fieldwork. Mokgacha is situated between two major elephant pathways, time-worn paths that elephants use to move between the savanna and the Delta's waters (Songhurst et al.,
We selected households from across Mokgacha where individuals were recruited for repeated firewood harvest focal follows (hereafter referred to as focal follows) (
Following verbal consent with each participant, we arranged focal follows up to a week prior to actual harvest. We met participants at their home at their preferred time and date. On some occasions participants were unavailable at the agreed upon time and focal follows were postponed to a later time and/or date. During focal follows, we used a handheld GPS unit to record both the primary collection site as well as the track traveled from the residence to reach that point. We recorded starting locations and times as well as the time that firewood harvest began and ended. We identified and listed all firewood species harvested during each focal follow. Species of firewood harvested were identified with the help of a research assistant from the locality with extensive experience collecting and identifying firewood. We recorded names in Setswana or other local languages when necessary and translated local tree names into Latin names with the assistance of Vogel (unpublished data) and Okavango Research Institute Herbarium.
We collected data on trees and assessed elephant-related damage in vegetation plots around Mokgacha. Because we wanted a representative sample which included the variety of habitat types in the area, we chose not to assign points within a grid but instead to stratify sampling by habitat type and distance to the village. We first identified eight broad habitat types based on an existing vegetation classification (GeoTerra Image Ltd,
For each of the assigned points, we assessed a 5-meter radius plot. We recorded each tree or shrub >1 meter in height within the plot and assessed elephant damage to each individual plant. Types of elephant damage recorded included: presence/absence of browse (leaves and twigs < 2 cm diameter), presence/absence of small branch damage (2–10 cm diameter), presence/absence of large branch damage (> 10 cm diameter), presence/absence of main stem damage of any size, mortality, and percentage of uprooting and debarking around the circumference. Elephant damage was distinguished from human or livestock damage by assessing height of browsing, type of branch break or browse, manually broken tree trunks, visual hatchet marks, or low, clipped vegetation associated with cattle and goats.
We quantified how firewood would be generated by elephant damage to trees based on an acceptance-availability calculation for each species. We based our firewood generation index on a plant selection index described by Owen-Smith and Chafota (
We used location data from 10 male and 10 female elephants in the Panhandle to estimate spatial and temporal use of tree resources by elephants. The elephants were fitted with Vectronic GPS collars which recorded hourly location fixes (
We tabulated the tree species collected on each of the focal follows with Mokgacha households and calculated the frequency that each species was collected. We compared this frequency with the firewood generation index value calculated for each tree species that was found within the vegetation plots. We excluded plots on Delta islands for the comparison, as people were unable to reach those islands to collect firewood during our study period.
We extracted the times that people harvested firewood from the focal follows. We calculated temporal patterns of elephant proximity to settlements by selecting all points that were within 250 m of the edge of a settlement (7,009 points). We calculated the proportion of those points that occurred during each hour of the day and compared them to the proportion of times that people collected firewood.
We chose to use a resource-selection approach to estimate spatially explicit probabilities of use for areas where people collect firewood. The point-based resource selection function compares resources for sampled used and available points (Boyce et al.,
We choose the covariates below to include in our spatial models.
Normalized difference vegetation index(NDVI) | Mean value calculated over study period from MODIS NDVI Vegetation Product (Didan, |
Distance to main road | Distance based on OpenStreetMaps vector with osmplotr package (Padgham, |
Distance to okavango delta | Distance based on Delta vector classified in Google Earth Engine from 20 m resolution Sentinel data (ASF DAAC, |
Distance to settlements | Distance based on point density clouds of buildings and kraals generated from Google Earth basemap imagery in QGIS (QGIS Development Team, |
Vegetation classes: (1) miombo forests; (2) woodlands on Kalahari sand; (3) thornbush savanna; (4) other woodlands; (5) shrub- and grasslands; and (6) wetlands | Land cover classification from The Future Okavango Project (Pröpper et al., |
We used the same point-based resource selection function method to model spatial resource use by elephants. We subsampled the points to a minimum of 4 h interval to reduce autocorrelation since the resource selection function assumes independence between points, and serial animal locations are not independent of each other. To represent the points available to elephants we generated two random points for every used point within the minimum convex polygon for each individual elephant using the amt package (Signer et al.,
We generated spatial prediction rasters based on the models and compared the outputs using Spearman's correlation coefficient and with the spatialEco package (Evans and Ram,
Thirty-five tree species were collected by people for firewood at least once during focal follows, and 15 were harvested on at least five or more focal follows. The vegetation plots we assessed had an average of 6 different tree species and 18 individual trees. On average, 13 trees per plot showed signs of elephant browsing, and 10 were damaged by elephants in some way that would generate firewood. Nineteen tree species occurred in the vegetation plots and elephant spoor was found in all 83 plots surveyed. Elephants damaged all 19 of the tree species to some degree, and 11 were damaged in at least 80% of the plots where they were present (firewood generation index >0.80). Of the top 10 species damaged most frequently by elephants, 8 of those species were also in the top 10 frequently collected for firewood (
Plot of the frequency of firewood collection by people and the elephant firewood generation index value for each tree species. All tree species found in at least five vegetation plots were included. Species are ordered by increasing frequency of firewood harvest by people.
We found contrasting temporal patterns of landscape use by people and elephants (
Contrasting temporal pattern of firewood collection by people and frequency of elephant proximity near settlements (within 250 meters).
Proximity to settlements was the only significant predictor of human resource selection for firewood (
Resource selection functions were modeled using generalized linear models.
Intercept | 123.85 | −0.95*** | −1.00*** |
NDVI | −0.49 | 0.25*** | −0.20*** |
Distance to Delta | – | −0.61*** | −0.23*** |
Distance to main road | 21.02 | 0.18*** | −0.13** |
Distance to settlement | −94.91*** | −0.056* | −0.20*** |
Miombo forests | – | −0.74*** | −0.19** |
Woodlands on Kalahari sands | 16.19 | 0.18* | −0.079 |
Thornbush savanna | 17.01 | −0.34*** | −0.12 |
Other woodlands | – | 0.22** | −0.078 |
Shrub- and grasslands | – | 0.24** | −0.073 |
Wetlands | 16.92 | −0.56*** | 0.580*** |
Predicted surfaces generated from resource selection models. The color ramp scales from negative selection values in blue to positive selection values in orange. Coefficients and covariates used to create these surfaces are reported in
We found that during the day, all vegetation classes were significant predictors for elephant presence. Distance to settlements and the Delta were negatively selected for during the day. NDVI was positively selected for, as well as increasing distance from roads. However, at night, NDVI and distance to roads were negatively selected for, and vegetation classes became less significant predictors of elephant presence (
All firewood collection occurred in the daytime. By comparing firewood collection activity to elephants' daytime and nighttime movements, we can see how the patterns could change if there was no temporal partitioning of resource use. The raster values predicted by these models were negatively correlated when comparing firewood collection and daytime elephant resource selection, and positively correlated when comparing firewood collection and nighttime elephant resource selection (Spearman's correlation coefficient: day = −0.050; night = 0.401). Spatial representation of the correlation highlights that during the daytime, human and elephant patterns of use are more negatively correlated, but that elephants' resource use at night shows more positive spatial correlation with firewood collection patterns (
Interactions between people and wildlife framed only through the lens of conflict may fail to account for instances where there are neutral or beneficial interactions. Much of the research currently available on human-elephant interactions in southern Africa focuses on conflict-mitigation, in particular around farms (O'Connell-Rodwell et al.,
People benefitted from a renewable supply of firewood created by elephants. Potentially dangerous interactions were self-mitigated through differing patterns of spatial and temporal use of the tree resources. We found temporal partitioning facilitated a situation where people could benefit from the availability of firewood generated from elephant-damaged trees by collecting wood when elephants were unlikely to be present. The tree branches that people most frequently collected for firewood were of the same tree species that elephants often damaged. People also collected firewood in the same areas where elephants moved, and some areas of the landscape had high likelihood of selection both by people for firewood and by elephants for foraging. There was a positive correlation between the areas that elephants selected at night as areas people used for firewood collection during the day. By utilizing similar areas at different times, people and elephants likely reduced their direct interactions and potential for conflict.
We found that elephants significantly impacted the vegetation in this area, damaging many trees across the study site. A majority of tree species were damaged with high frequency. When elephants browse or rub on trees, they can break branches and even trunks, and previous research has emphasized how this vegetation damage can change ecosystem structure (Mosugelo et al.,
In this region we found that elephants and humans used the same tree species, which could be driven by a variety of factors. One potential cause is the abundance of wood generated from elephant damage to certain species, and the resulting availability of those species for collection.
We found little overlap in the temporal patterns of people and elephants, which is likely due to mutual avoidance. Our findings support the existing literature on elephant movement outside of protected areas (Douglas-Hamilton et al.,
We also interpreted elephants' nocturnality around the settlements to indicate that elephants modified their behavior to avoid people and the associated risk. Although we expected to find elephants positively selecting for farther distances from settlements during the day, in fact we only found a reduced negative selection based on our model. This indicates that there are potential reasons elephants would not show high avoidance of settlements during the day. There are agricultural fields around many settlements in the region, and crops are raided by elephants. Additionally, most settlements in the Panhandle, including Mokgacha, are close to the Delta. We would expect elephants to pass near the settlements on their way to drink water and for thermoregulation throughout the day, and perhaps slightly more during the hottest part of day. We did find that wetland habitat and distance to the Delta were stronger predictors of elephant presence in the day than at night. This means elephants were selecting areas close to the Delta and wetland habitat. However, according to the patterns of temporal proximity separate from our model, elephants were rarely found near the settlements during hot midday or afternoon. Further research would be required to see if elephants exhibited this sort of pattern near water in areas where settlements are less dense in order to test whether elephants are modifying their behavior specifically due to settlements. Additionally, accounting for seasonal differences in temperature and distribution of available water may play a role in determining elephant proximity to settlements near the Delta.
Further support for temporal partitioning comes from the positive correlation between areas with high probability for firewood collection and areas where elephants were likely to be present. When elephants moved through the natural areas from dusk until dawn, browsing and damaging trees, they created an abundance of downed branches. During the day, people moved through many of the same areas where elephants moved at night. Due to elephant activity in those areas, people were able to meet their firewood harvest demands by picking up downed branches. The spatial patterns of where elephants went during the day was slightly negatively correlated with areas of human firewood collection. At night, elephants select positively for proximity to roads, settlements, and negatively for NDVI, leading to the positive correlation with firewood collection areas. If elephants exhibited the same patterns of movement during the day as they do at night, there would likely be more direct encounters between people and elephants as they would be more spatially proximate to each other.
While we recognize the often-enormous costs for people who share the environment with elephants, our study shows that human-elephant interactions around common pool resources may simultaneously carry benefits for rural, natural-resource dependent communities. Further studies that explore other overlooked aspects of human-elephant interactions can provide more evidence of complex interactions, and may one day be used to build a more complete typology that captures the kinds of interactions around resources with different governance arrangements. As humans and wildlife increasingly come into contact within social-ecological systems, it is important to implement an interdisciplinary approach to support coexistence.
This study was carried out in accordance with the recommendation of the Animal Use Protocol, Texas A&M AgriLife Research Agricultural Animal Care and Use Committee. The protocol was approved by the Agricultural Animal Care and Use Committee, AUP #2014-005A and 2017-010A. Proper veterinary and immobilization permits and procedures were approved by the Government of Botswana Department of Wildlife and National Parks, [EWT8/36/4 XVII(79)]. This study was also approved by the Texas A&M Institutional Review Board for ethnographic research including focal follows under study number #IRB2016-0255D. Verbal consent procedure and assessment of risks were approved by the board.
EB and LR conceived of the research, conducted fieldwork, and co-authored the manuscript. EB analyzed the data. AmS, GM, AnS, and LF provided supervision, advice, and editing of the manuscript. All authors discussed results and contributed to final manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This research would not have been possible without the support of the leaders and residents of Mokgacha Village and Danga, Tinxo, Mawana, Nxininha, and Kavumo cattleposts. The authors would like to acknowledge the contributions of Ipolokeng Katholo, Susanne Vogel, Olorato Ratama, the Okavango Research Institute, Ecoexist Project, and Lethatha Gaborekwe. This study has approval from the Texas A&M Institutional Review Board, study #IRB2016- 0255D.
The Supplementary Material for this article can be found online at: