Large-mammal populations are ecological linchpins1, and their worldwide decline2 and extinction3 disrupts many ecosystem functions and services4. Reversal of this trend will require an understanding of the determinants of population decline, to enable more accurate predictions of when and where collapses will occur and to guide the development of effective conservation and restoration policies2,5. Many correlates of large-mammal declines are known, including low reproductive rates, overhunting, and habitat destruction2,6,7. However, persistent uncertainty about the effects of one widespread factor—armed conflict—complicates conservation-planning and priority-setting efforts5,8. Case studies have revealed that conflict can have either positive or negative local impacts on wildlife8,9,10, but the direction and magnitude of its net effect over large spatiotemporal scales have not previously been quantified5. Here we show that conflict frequency predicts the occurrence and severity of population declines among wild large herbivores in African protected areas from 1946 to 2010. Conflict was extensive during this period, occurring in 71% of protected areas, and conflict frequency was the single most important predictor of wildlife population trends among the variables that we analysed. Population trajectories were stable in peacetime, fell significantly below replacement with only slight increases in conflict frequency (one conflict-year per two-to-five decades), and were almost invariably negative in high-conflict sites, both in the full 65-year dataset and in an analysis restricted to recent decades (1989–2010). Yet total population collapse was infrequent, indicating that war-torn faunas can often recover. Human population density was also correlated (positively) with wildlife population trajectories in recent years; however, we found no significant effect, in either timespan, of species body mass, protected-area size, conflict intensity (human fatalities), drought frequency, presence of extractable mineral resources, or various metrics of development and governance. Our results suggest that sustained conservation activity in conflict zones—and rapid interventions following ceasefires—may help to save many at-risk populations and species.
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We thank E. Angus, C. Baker, C. Buoncore, J. Castillo Vardaro, B. Lin, A. Tilman, and the Princeton University libraries staff for assistance with data collection and analysis; D. Wilcove, S. Pacala, S. Morris, S. Budischak, J. Socolar, K. Gaynor, J. Edmond, T. Coverdale, R. Long, and U. Srinivasan provided comments. I. Craigie provided access to raw data from ref. 19, and we acknowledge the use of publically available data sources, especially from R. East, the IUCN African Elephant Specialist Group, the World Database of Protected Areas, PRIO-GRID, and GED. This work is a product of US NSF DDIG grant DEB-1501306 to J.H.D. and R.M.P. Additional support was provided by NSF DEB-1355122, DEB-1457697, and the Princeton Environmental Institute.
The authors declare no competing financial interests.
Reviewer Information Nature thanks I. Craigie, O. Schmitz, A. Shortland and the other anonymous reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
Extended Data Figure 1 Distribution and frequency of armed conflict in African protected areas for the restricted interval, 1989–2010.
a, Number of conflict-years in each protected area; colours indicate average value across all grid cells overlapping the protected area. b, Mean conflict-years per protected area in each country. Boxes, inter-quartile ranges; vertical lines, medians; whiskers, 1.5× the inter-quartile range from the median; dots, outlying values. Total number of protected areas included per country, from the World Database of Protected Areas21, is shown on the right; statistical analyses of the correlation between conflict and wildlife population trajectories were conducted using the subset of these protected areas for which adequate wildlife data were obtainable. Sudan and South Sudan are distinguished in a but combined in b; two outlying island nations, Cape Verde and Mauritius, are omitted from a but included in b. Map created in ArcGIS and R using open-access country-border data from the Global Administrative Areas database (https://gadm.org). C.A.R., Central African Republic; D.R. Congo, Democratic Republic of Congo; Equat. Guinea, Equatorial Guinea; West. Sahara, Western Sahara.
Model: λ ~ conflict frequency + body mass + protected-area size. a, Histogram showing approximate normality of regression residuals. b, Plot of residuals versus model fit, showing no clear pattern (thus, no pronounced heteroscedasticity). c, Standardized residuals versus leverage; dashed red lines show Cook’s distance contours, indicating that no points exerted disproportionate influence on the regression outcome (that is, no points had Cook’s distance > 1.0)61. d–f, Plots of residuals versus conflict frequency (d), body mass (e), and protected-area size (f). All show no clear pattern, which validates our decision not to include non-additive interaction terms in the candidate-model set61. They also show no curvilinear relationships with the predictor, which indicates that there is no strong justification for including nonlinear fits68. a–f, Data are from 253 λ estimates.
Model: λ ~ conflict frequency + HPD + percentage of urban area + drought frequency. a, Histogram showing approximate normality of regression residuals. b, Plot of residuals versus model fit, showing no clear pattern (thus, no pronounced heteroscedasticity). c, Standardized residuals versus leverage; dashed red lines show Cook’s distance contours, indicating that just one point may have exerted disproportionate influence on the regression outcome (Cook’s distance >1.0)61. However, excluding this datum did not qualitatively alter the results. d–f, Plots of residuals versus conflict frequency (d), HPD (e), percentage of urban area (f), and SPEI drought index (g). All show no clear pattern, which validates our decision not to include non-additive interaction terms in the candidate-model set61, and also show no curvilinear relationships, which indicates no strong justification for including nonlinear fits68. a–f, Data are from 172 λ estimates.
a, b, Moran’s I (ref. 65) for all pairwise combinations of data points, calculated from the residuals of the best-fitting models69 for 1946–2010 (a) and 1989–2010 (b) and plotted as a function of the geographical distance between the protected areas from which the data were drawn (in 50-km bins). I = 0 indicates a random distribution of values in space, I > 0 indicates spatial clustering, and I < 0 indicates overdispersion. The magnitude of I can be interpreted similarly to a correlation coefficient, with I < 0.20 considered to indicate that little autocorrelation is present at a given distance class66. The absence of a monotonically decreasing trend in I as the distance between sampled locations increases supports our interpretation that λ did not co-vary as a function of some unaccounted-for underlying spatial process that might confer statistical non-independence67.
This file contains Supplementary Tables 1-5. (PDF 1084 kb)
Raw data for the 1946–2010 analyses. These data include for each mammal population: the country, protected area, species name, time interval (starting and ending years), λ estimate, conflict-frequency estimate, species body mass, protected-area size, spatially lagged conflict frequency estimates, national-level conflict-frequency estimate, spatial and temporal dimensions of conflict frequency, grouping of records for the bootstrap test of pseudoreplication, and the literature sources for mammal population data in each year. See Supplementary Table 5 for explanations of each column heading. (XLS 99 kb)
Raw data for the 1989–2010 analyses. These data include for each mammal population: the country, protected area, species name, time interval (starting and ending years), λ estimate, conflict-frequency estimate, species body mass, protected-area size, human population density, Corruption Perceptions Index, conflict intensity, drought frequency, percent urban area, travel time to the nearest urban centre, presence or absence of extractable mineral resources, spatially lagged conflict frequency estimate, national-level conflict-frequency estimate, spatial and temporal dimensions of conflict frequency, grouping of records for the bootstrap test of pseudoreplication, and the literature sources for mammal population data in each year. See Supplementary Information Table 5 for explanations of each column heading. (XLS 94 kb)
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Daskin, J., Pringle, R. Warfare and wildlife declines in Africa’s protected areas. Nature 553, 328–332 (2018). https://doi.org/10.1038/nature25194
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