Anthropogenic risk increases night-time activities and associations in African elephants (Loxodonta africana) in the Ruaha-Rungwa ecosystem, Tanzania

Elephants face diverse threats from human activities and use temporal and social strategies to reduce human-induced mortality risk. We used data from camera trap surveys in 2018–2019 (n= 1625 independent detection events from 11,751 sampling days) to investigate elephant responses to anthropogenic risk in the Ruaha-Rungwa ecosystem, Tanzania. The study was conducted in one low- risk and three high- risk sites using 26–40 paired camera trap stations per site. Risk influenced the active pe-riods, use of roads and water sources, social associations and behaviour of elephants. Elephants demonstrated significantly more night-time and reduced daytime activ-ity in the high- risk sites relative to the low- risk site. This higher night-time activity in the high- risk sites was observed for both males and females, though it was more pronounced for cow–calf groups than lone males. Foraging events and use of water sources were more frequent at night in the high- risk sites. Elephants used roads as movement routes in the low- risk site but avoided roads in the high- risk sites. Males were significantly more likely to associate with other males and cow–calf groups in the high- risk sites. Fewer occurrences of relaxed behaviours were observed in the high- risk sites compared to the low- risk site. We discuss the potential implications of our findings for elephant survival and reproduction.Output Status: Forthcoming/Available Onlin

Large herbivores may alter vegetation structure of semi-arid savannas through soil nutrient mediation

In savannas, the tree–grass balance is governed by water, nutrients, fire and herbivory, and their interactions. We studied the hypothesis that herbivores indirectly affect vegetation structure by changing the availability of soil nutrients, which, in turn, alters the competition between trees and grasses. Nine abandoned livestock holding-pen areas (kraals), enriched by dung and urine, were contrasted with nearby control sites in a semi-arid savanna. About 40 years after abandonment, kraal sites still showed high soil concentrations of inorganic N, extractable P, K, Ca and Mg compared to controls. Kraals also had a high plant production potential and offered high quality forage. The intense grazing and high herbivore dung and urine deposition rates in kraals fit the accelerated nutrient cycling model described for fertile systems elsewhere. Data of a concurrent experiment also showed that bush-cleared patches resulted in an increase in impala dung deposition, probably because impala preferred open sites to avoid predation. Kraal sites had very low tree densities compared to control sites, thus the high impala dung deposition rates here may be in part driven by the open structure of kraal sites, which may explain the persistence of nutrients in kraals. Experiments indicated that tree seedlings were increasingly constrained when competing with grasses under fertile conditions, which might explain the low tree recruitment observed in kraals. In conclusion, large herbivores may indirectly keep existing nutrient hotspots such as abandoned kraals structurally open by maintaining a high local soil fertility, which, in turn, constrains woody recruitment in a negative feedback loop. The maintenance of nutrient hotspots such as abandoned kraals by herbivores contributes to the structural heterogeneity of nutrient-poor savanna vegetation

Estimating the abundance of a group-living species using multi-latent spatial models

Statistical models use observations of animals to make inferences about the abundance and distribution of species. However, the spatial distribution of animals is a complex function of many factors, including landscape and environmental features, and intra- and interspecific interactions. Modelling approaches often have to make significant simplifying assumptions about these factors, which can result in poor model performance and inaccurate predictions. Here, we explore the implications of complex spatial structure for modelling the abundance of the Serengeti wildebeest, a gregarious migratory species. The social behaviour of wildebeest leads to a highly aggregated distribution, and we examine the consequences of omitting this spatial complexity when modelling species abundance. To account for this distribution, we introduce a multi-latent framework that uses two random fields to capture the clustered distribution of wildebeest. Our results show that simplifying assumptions that are often made in spatial models can dramatically impair performance. However, by allowing for mixtures of spatial models accurate predictions can be made. Furthermore, there can be a non-monotonic relationship between model complexity and model performance; complex, flexible models that rely on unfounded assumptions can potentially make highly inaccurate predictions, whereas simpler more traditional approaches involve fewer assumptions and are less sensitive to these issues. We demonstrate how to develop flexible spatial models that can accommodate the complex processes driving animal distributions. Our findings highlight the importance of robust model checking protocols, and we illustrate how realistic assumptions can be incorporated into models using random fields

Spatial distribution of lion kills determined by the water dependency of prey species

Predation risk from lions (Panthera leo) has been linked to habitat characteristics and availability and traits of prey. We separated the effects of vegetation density and the presence of drinking water by analyzing locations of lion kills in relation to rivers with dense vegetation, which offer good lion stalking opportunities, and artificial water points with low vegetation density. The spatial distribution of lion kills was studied at the Klaserie Private Nature Reserve, South Africa. The distance between 215 lion kills and the nearest water source was analyzed using generalized linear models. Lions selected medium-sized prey species. Lion kills were closer to rivers and to artificial water points than expected by random distribution of the kills. Water that attracted prey, and not the vegetation density in riverine areas, increased predation risk, with kills of buffalo (Syncerus caffer), kudu (Tragelaphus strepsiceros), and wildebeest (Connochaetes taurinus) as water-dependent prey species. Traits of prey species, including feeding type (food habits), digestion type (ruminant or nonruminant), or body size, did not explain locations of lion kills, and no seasonal patterns in lion kills were apparent. We argue that the cascading impact of lions on local mammal assemblages is spatially heterogeneous