Search

Foraging animals commonly choose whether to find new food (as 'producers') or scavenge from others (as 'scroungers'), and this decision has ecological and evolutionary consequences. Understanding these tactic decisions is particularly vital for naturally occurring producer-scrounger systems of economic importance, because they determine the system's productivity and resilience. Here, we investigate how individuals' traits predict tactic decisions, and the consistency and pay-offs of these decisions, in the remarkable mutualism between humans ( Homo sapiens ) and greater honeyguides ( Indicator indicator ). Honeyguides can either guide people to bees' nests and eat the resulting beeswax (producing), or scavenge beeswax (scrounging). Our results suggest that honeyguides flexibly switched tactics, and that guiding yielded greater access to the beeswax. Birds with longer tarsi scrounged more, perhaps because they are more competitive. The lightest females rarely guided, possibly to avoid aggression, or because genetic matrilines may affect female body mass and behaviour in this species. Overall, aspects of this producer-scrounger system probably increase the productivity and resilience of the associated human-honeyguide mutualism, because the pay-offs incentivize producing, and tactic-switching increases the pool of potential producers. Broadly, our findings suggest that even where tactic-switching is prevalent and producing yields greater pay-offs, certain phenotypes may be predisposed to one tactic.

Many mutualisms are exploited by third-party species, which benefit without providing anything in return. Exploitation can either destabilize or promote mutualisms, via mechanisms that are highly dependent on the ecological context. Here we study a remarkable bird-human mutualism, in which wax-eating greater honeyguides ( Indicator indicator ) guide humans ( Homo sapiens ) to wild bees' nests, in an exchange of knowledge about the location of nests for access to the wax combs inside. We test whether the depletion of wax by mammalian and avian exploiter species either threatens or stabilizes the mutualism. Using camera traps, we monitored feeding visits to wax comb made available following honey harvests. We found that greater honeyguides face competition for wax from conspecifics and nine exploiter species, five of which were not previously known to consume wax. Our results support the hypothesis that heterospecific exploiters stabilize the mutualism, because wax depletion by these competitors probably limits feeding opportunities for conspecific exploiters, favouring the early-arriving individual that guided humans to the bees' nest. These findings highlight the importance of the ecological context of species interactions and provide further evidence for how mutualisms can persist because of, and not in spite of, exploitation by third-party species.

Claire Spottiswoode and colleagues introduce the ancient partnership between wild honeyguides and human honey hunters., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Human-wildlife cooperation occurs when humans and free-living wild animals actively coordinate their behavior to achieve a mutually beneficial outcome. These interactions provide important benefits to both the human and wildlife communities involved, have wider impacts on the local ecosystem, and represent a unique intersection of human and animal cultures. The remaining active forms are human-honeyguide and human-dolphin cooperation, but these are at risk of joining several inactive forms (including human-wolf and human-orca cooperation). Human-wildlife cooperation faces a unique set of conservation challenges, as it requires multiple components-a motivated human and wildlife partner, a suitable environment, and compatible interspecies knowledge-which face threats from ecological and cultural changes. To safeguard human-wildlife cooperation, we recommend: (i) establishing ethically sound conservation strategies together with the participating human communities; (ii) conserving opportunities for human and wildlife participation; (iii) protecting suitable environments; (iv) facilitating cultural transmission of traditional knowledge; (v) accessibly archiving Indigenous and scientific knowledge; and (vi) conducting long-term empirical studies to better understand these interactions and identify threats. Tailored safeguarding plans are therefore necessary to protect these diverse and irreplaceable interactions. Broadly, our review highlights that efforts to conserve biological and cultural diversity should carefully consider interactions between human and animal cultures. Please see AfricanHoneyguides.com/abstract-translations for Kiswahili and Portuguese translations of the abstract., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Conservation Letters published by Wiley Periodicals LLC.)

Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future.