Complementary benefits of tourism and hunting to communal conservancies in Namibia

Tourism and hunting both generate substantial revenues for communities and private operators in Africa, but few studies have quantitatively examined the trade‐offs and synergies that may result from these two activities. We evaluated financial and in‐kind benefit streams from tourism and hunting on 77 communal conservancies in Namibia from 1998 to 2013, where community‐based wildlife conservation has been promoted as a land‐use that complements traditional subsistence agriculture. We used data collected annually for all communal conservancies to characterize whether benefits were derived from hunting or tourism. We classified these benefits into 3 broad classes and examined how benefits flowed to stakeholders within communities under the status quo and under a simulated ban on hunting. Across all conservancies, total benefits from hunting and tourism increased at roughly the same rate, although conservancies typically started generating benefits from hunting within 3 years of formation as opposed to after 6 years for tourism. Disaggregation of data revealed that the main benefits from hunting were income for conservancy management and food in the form of meat for the community at large. The majority of tourism benefits were salaried jobs at lodges. A simulated ban on trophy hunting significantly reduced the number of conservancies that could cover their operating costs, whereas eliminating income from tourism did not have as severe an effect. Given that the benefits generated from hunting and tourism typically begin at different times in a conservancy's life‐span (earlier vs. later, respectively) and flow to different segments of local communities, these 2 activities together may provide the greatest incentives for conservation on communal lands in Namibia. A singular focus on either hunting or tourism would reduce the value of wildlife as a competitive land‐use option and have grave repercussions for the viability of community‐based conservation efforts in Namibia, and possibly other parts of Africa.     

Spatial patterns reveal negative density dependence and habitat associations in tropical trees

Understanding how plant species coexist in tropical rainforests is one of the biggest challenges in community ecology. One prominent hypothesis suggests that rare species are at an advantage because trees have lower survival in areas of high conspecific density due to increased attack by natural enemies, a process known as negative density dependence (NDD). A consensus is emerging that NDD is important for plant-species coexistence in tropical forests. Most evidence comes from short-term studies, but testing the prediction that NDD decreases the spatial aggregation of tree populations provides a long-term perspective. While spatial distributions have provided only weak evidence for NDD so far, the opposing effects of environmental heterogeneity might have confounded previous analyses. Here we use a novel statistical technique to control for environmental heterogeneity while testing whether spatial aggregation decreases with tree size in four tropical forests. We provide evidence for NDD in 22% of the 139 tree species analyzed and show that environmental heterogeneity can obscure the spatial signal of NDD. Environmental heterogeneity contributed to aggregation in 84% of species. We conclude that both biotic interactions and environmental heterogeneity play crucial roles in shaping tree dynamics in tropical forests.     

Bivariate geostatistical modelling: a review and an application to spatial variation in radon concentrations

We present a comprehensive review of multivariate geostatistical models, focusing on the bivariate case. We compare in detail three approaches, the linear model of coregionalisation, the common component model and the kernel convolution approach, and discuss similarities between them. We demonstrate the merits of the common component class of models as a flexible means for modelling bivariate geostatistical data of the type that frequently arises in environmental applications. In particular, we show how kernel convolution can be used to approximate the common component model, and demonstrate the method using a data-set of calcium and magnesium concentrations in soil samples. We then apply the model to a study of domestic radon concentrations in the city of Winnipeg, Canada, in which exposure was measured at two sites (bedroom and basement) in each residential location. Our analysis demonstrates that in this study the correlation between the two sites within each house dominates the short-range spatial correlation typical of the distribution of radon.