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Linking Anthropogenic Resources to Wildlifeã¢â‚¬â€œpathogen Dynamics a Review and Meta-analysis

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doi: 10.1111/ele.12428. Epub 2015 Mar 21.

Linking anthropogenic resources to wild animals-pathogen dynamics: a review and meta-assay

Affiliations

  • PMID: 25808224
  • PMCID: PMC4403965
  • DOI: ten.1111/ele.12428

Gratis PMC article

Review

Linking anthropogenic resources to wildlife-pathogen dynamics: a review and meta-analysis

Daniel J Becker  et al. Ecol Lett. 2015 May .

Free PMC commodity

Abstract

Urbanisation and agriculture cause declines for many wild animals, but some species benefit from novel resource, especially food, provided in human-dominated habitats. Resulting shifts in wildlife ecology can modify communicable diseases dynamics and create opportunities for cross-species transmission, yet predicting host-pathogen responses to resource provisioning is challenging. Factors enhancing transmission, such every bit increased aggregation, could exist offset past better host amnesty due to improved nutrition. Here, nosotros conduct a review and meta-analysis to show that nutrient provisioning results in highly heterogeneous infection outcomes that depend on pathogen type and anthropogenic nutrient source. We also notice empirical support for behavioural and immune mechanisms through which human-provided resources alter host exposure and tolerance to pathogens. A review of recent theoretical models of resource provisioning and infection dynamics shows that changes in host contact rates and immunity produce strong non-linear responses in pathogen invasion and prevalence. By integrating results of our meta-analysis dorsum into a theoretical framework, we detect provisioning amplifies pathogen invasion under increased host aggregation and tolerance, but reduces transmission if provisioned food decreases dietary exposure to parasites. These results carry implications for wild fauna affliction management and highlight areas for futurity piece of work, such as how resource shifts might affect virulence evolution.

Keywords: Aggregation; agriculture; foraging environmental; host-parasite interactions; immune defense; infectious disease ecology; mathematical models; supplemental feeding; urbanisation.

Figures

Figure 1
Figure i

Predicted relationships between provisioning and R 0 (where R 0 = 1 is the pathogen invasion threshold). Aggregation around resources could increase host contact rates and infectious stage build-upward in the environment (a; orange), an effect illustrated by increased flocking of house finches at bird feeders and associated increases in conjunctivitis prevalence (b; Altizer et al. 2004). Provisioning can too meliorate host vital rates and increase host population sizes (a; green), which was suggested to explicate higher pathogen prevalence among bumblebees in urban versus rural gardens (c; Goulson et al. 2012). Positive effects of provisioning on R 0 could be countered by improved host condition and immune defence (a; purple). Such an result is suggested by kit foxes showing lower nutritional stress, higher body condition, and improved immune role in urban areas where nutrient and water was more plentiful (d; Goose egg & Frost 1999). Images are provided by Wikimedia Commons.

Figure 2
Figure 2

Distribution of effect sizes for observed relationships between provisioning and infection outcomes (points ± 95% confidence intervals) aslope the mean upshot size gauge (diamond) from the bias-corrected REM (a). Each point is a item host–pathogen interaction. Points in a higher place the horizontal line demonstrate cases where provisioning increased infection prevalence, intensity or seroprevalence; points beneath the horizontal line demonstrate reduced infection outcomes. (b) Estimated mean event size of predictors on infection outcomes, denoted through diamonds alongside 95% confidence intervals. Sample size (n) refers to the number of host–pathogen interactions corresponding to each level. Positive consequence sizes indicate increases in infection outcomes (measures of prevalence, seroprevalence and intensity are pooled).

Figure 3
Figure iii

Visualisation of the MEM explaining the near variation in infection outcomes from the meta-analysis. Information points represent the predicted effect of provisioning for each combination of food source (see fable) and pathogen type, where the horizontal line represents no influence of supplemental feeding on infection. Asterisks correspond means significantly different from cypher after adjusting for multiple comparisons (*P < 0.05, **P < 0.01). Effects based on agricultural food and fungal pathogens are not shown owing to limited information.

Figure 4
Effigy iv

Full general modelling framework for how provisioning affects infectious affliction dynamics of a microparasite (Box ane). In this compartmental framework (a–b), provisioning causes cardinal parameters to increase (shown in bluish) or decrease (shown in red). Varying the response of immune parameters to provisioning generates a range of outcomes on R 0 (c). An increasingly saturating outcome of provisioning is shown through line width (dashed indicates no effect on amnesty), and this arroyo tin generate outcomes ranging from amplifying prevalence to driving R 0 below the invasion threshold (grey line). Figure is adjusted from Becker & Hall (2014), and farther model details and parameter definitions are provided in Box one.

Figure 5
Figure 5

Meta-analysis-guided re-assessment of provisioning effects on pathogen invasion via mathematical models. Simulations examine net effects of resource-mediated processes on R 0 by considering 2 independent behavioural mechanisms supported by our assay, in which provisioning either elevates contact rates (a) or decreases dietary exposure to pathogens (b). Forth with incorporating the above effects and those of resources-altered resistance and tolerance, the model includes potential influence of resource-altered demography, where line width indicates how strongly birth and bloodshed parameters answer to provisioning (shown in the fable). Simulations follow the parameterisation given in Becker & Hall (2014), and the analytic expression for R 0 is provided in the Supplemental Material.

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Source: https://pubmed.ncbi.nlm.nih.gov/25808224/

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