A multispecies assessment of wildlife impacts on local community livelihoods

Pozo, Rocío Alfaro; LeFlore, Eric; Duthie, A. Bradley; Jones, Isabel L.; Minderman, Jeroen; Rakotonarivo, O. Sarobidy; Cusack, Jeremy J.

doi:10.1111/cobi.13565

Cited by 16 publications

(15 citation statements)

References 47 publications

(134 reference statements)

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“…There may be various consequences associated with the trend of increasing population densities with higher human footprint. For example, higher population density can lead to changes in disease dynamics (Pongsiri et al 2009), increased human–wildlife interactions (Soulsbury and White 2016, König et al 2020, Pozo et al 2020), habitat changes (Wallach et al 2010) and declines of other species (e.g. native species; Hollings et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Mammal population densities at a global scale are higher in human‐modified areas

et al. 2020

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Global landscapes are changing due to human activities with consequences for both biodiversity and ecosystems. For single species, terrestrial mammal population densities have shown mixed responses to human pressure, with both increasing and decreasing densities reported in the literature. How the impacts of human activities on mammal populations translates into altered global density patterns remains unclear. Here we aim to disentangle the effect of human impacts on large‐scale patterns of mammal population densities using a global dataset of 6729 population density estimates for 468 mammal species (representing 59% and 44% of mammalian orders and families). We fitted a mixed effect model to explain the variation in density based on a 1‐degree resolution as a function of the human footprint index (HFI), a global proxy of direct and indirect human disturbances, while accounting for body mass, trophic level and primary productivity (normalized vegetation index; NDVI). We found a significant positive relationship between population density and HFI, where population densities were higher in areas with a higher HFI (e.g. agricultural or suburban areas – no populations were located in very high HFI urban areas) compared to areas with a low HFI (e.g. wilderness areas). We also tested the effect of the individual components of the HFI and still found a consistent positive effect. The relationships remained positive even across populations of the same species, although variability among species was high. Our results indicate shifts in mammal population densities in human modified landscapes, which is due to the combined effect of species filtering, increased resources and a possible reduction in competition and predation. Our study provides further evidence that macroecological patterns are being altered by human activities, where some species will benefit from these activities, while others will be negatively impacted or even extirpated.

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Section: Discussionmentioning

confidence: 99%

Mammal population densities at a global scale are higher in human‐modified areas

et al. 2020

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“…The influence of seasonality on carnivore depredation of livestock has been examined extensively across sub-Saharan Africa (Patterson et al, 2004;Woodroffe and Frank, 2005;Kolowski and Holekamp, 2006;Valeix et al, 2009;Mukeka et al, 2019;Robertson et al, 2019). Consistently, rates of carnivore depredation of livestock have been shown to increase during rainy seasons (but see Pozo et al, 2020). This seasonality has been attributed to many potential drivers, including herding practices, wild prey migration, and prey switching driven by reduced predation success on wild prey (Patterson et al, 2004;Kolowski and Holekamp, 2006;Valeix et al, 2012;Mponzi et al, 2014;Kuiper et al, 2015;Loveridge et al, 2017;Kissui et al, 2019;Mukeka et al, 2019).…”

Section: Discussionmentioning

confidence: 96%

The Integral Nature of Encounter Rate in Predicting Livestock Depredation Risk

Hoffmann

Pilfold

Ruppert

et al. 2022

Front. Conserv. Sci.

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Carnivore depredation of livestock is one of the primary drivers of human-carnivore conflict globally, threatening the well-being of livestock owners, and fueling large carnivore population declines. Interventions designed to reduce carnivore depredation typically center around predictions of depredation risk. However, these spatial risk models tend to be informed by data depicting the number of livestock attacked by carnivores. Importantly, such models omit key stages in the predation sequence which are required to predict predation risk, or in this case depredation risk. Applying the classic predation risk model defined by Lima and Dill demonstrates that depredation risk is dependent upon quantifying the rates at which carnivores encounter livestock before attacking. However, encounter rate is challenging to estimate, necessitating novel data collection systems. We developed and applied such a system to quantify carnivore-livestock encounters at livestock corrals (i.e., bomas) across a 9-month period in Central Kenya. Concurrently, we monitored the number of livestock attacked by carnivores at these bomas. We calculated carnivore-livestock encounter rates, attack rates, and depredation risk at the boma. We detected 1,383 instances in which carnivores encountered livestock at the bomas. However, we only recorded seven attacks. We found that the encounter rate and attack rate for spotted hyenas were almost six and three times higher than that for any other species, respectively. Consequently, spotted hyenas posed the greatest depredation risk for livestock at the boma. We argue that better understanding of carnivore-livestock encounter rates is necessary for effective prediction and mitigation of carnivore depredation of livestock.

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“…This is relevant because human, financial and time resources are limited in conservation and there is a constant competition for their allocation to cases (Hughey et al 2003, Jachowski and Kesler 2008, McDonald-Madden et al 2008, Ruiz-Miranda et al 2020). It is also increasingly recognized that different species can impact human livelihood in different ways and at different times within the same geographical area, which should be considered in management (Pozo et al 2020, 2021). Intervention in one conflict could thus be a priority for a time, and then deprioritized when another requires intervention more urgently.…”

Section: Discussionmentioning

confidence: 99%

Intervene or wait? Modelling the timing of intervention in conservation conflicts adaptive management under uncertainty

Bach

Minderman

Mill

et al. 2021

Preprint

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The timing of biodiversity managers' interventions can be critical to the success of conservation, especially in situations of conflict between conservation objectives and human livelihood, i.e., conservation conflicts. Given the uncertainty associated with complex social-ecological systems and the potentially irreversible consequences of delayed action for biodiversity and livelihoods, managers tend to simply intervene as soon as possible by precaution. However, refraining from intervening when the situation allows it can be beneficial, notably by saving critical management resources. Here, we introduce a strategy for managers to decide, based on monitoring, whether intervention is required or if waiting is possible. This study evaluates the performance of this waiting strategy compared to a strategy of unconditional intervention at every opportunity. We built an individual-based model of conservation conflict between a manager aiming to conserve an animal population and farmers aiming to maximize yield by protecting their crop from wildlife damage. We then simulated a budget-constrained adaptive management over time applying each strategy, while accounting for uncertainty around population dynamics and around decision-making of managers and farmers. Our results showed that when the decision for the manager to intervene was based on a prediction of population trajectory, the waiting strategy performed at least as well as unconditional intervention while also allowing managers to save resources by avoiding unnecessary interventions. Under difficult budgetary constraints on managers, this waiting strategy ensured as high yields as unconditional intervention while significantly improving conservation outcomes by compensating managers' lack of resources with the benefits accrued over waiting periods. This suggests that waiting strategies are worth considering, as they can facilitate successful management with a more efficient use of management resources, which are often limiting in biodiversity conservation.

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A multispecies assessment of wildlife impacts on local community livelihoods

Cited by 16 publications

References 47 publications

Mammal population densities at a global scale are higher in human‐modified areas

Mammal population densities at a global scale are higher in human‐modified areas

The Integral Nature of Encounter Rate in Predicting Livestock Depredation Risk

Intervene or wait? Modelling the timing of intervention in conservation conflicts adaptive management under uncertainty

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