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      Biodiversity inhibits parasites: Broad evidence for the dilution effect

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          Abstract

          Infectious diseases of humans, wildlife, and domesticated species are increasing worldwide, driving the need to understand the mechanisms that shape outbreaks. Simultaneously, human activities are drastically reducing biodiversity. These concurrent patterns have prompted repeated suggestions that biodiversity and disease are linked. For example, the dilution effect hypothesis posits that these patterns are causally related; diverse host communities inhibit the spread of parasites via several mechanisms, such as by regulating populations of susceptible hosts or interfering with parasite transmission. However, the generality of the dilution effect hypothesis remains controversial, especially for zoonotic diseases of humans. Here we provide broad evidence that host diversity inhibits parasite abundance using a meta-analysis of 202 effect sizes on 61 parasite species. The magnitude of these effects was independent of host density, study design, and type and specialization of parasites, indicating that dilution was robust across all ecological contexts examined. However, the magnitude of dilution was more closely related to the frequency, rather than density, of focal host species. Importantly, observational studies overwhelmingly documented dilution effects, and there was also significant evidence for dilution effects of zoonotic parasites of humans. Thus, dilution effects occur commonly in nature, and they may modulate human disease risk. A second analysis identified similar effects of diversity in plant-herbivore systems. Thus, although there can be exceptions, our results indicate that biodiversity generally decreases parasitism and herbivory. Consequently, anthropogenic declines in biodiversity could increase human and wildlife diseases and decrease crop and forest production.

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          Most cited references23

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            The ecology of infectious disease: effects of host diversity and community composition on Lyme disease risk.

            The extent to which the biodiversity and community composition of ecosystems affect their functions is an issue that grows ever more compelling as human impacts on ecosystems increase. We present evidence that supports a novel function of vertebrate biodiversity, the buffering of human risk of exposure to Lyme-disease-bearing ticks. We tested the Dilution Effect model, which predicts that high species diversity in the community of tick hosts reduces vector infection prevalence by diluting the effects of the most competent disease reservoir, the ubiquitous white-footed mouse (Peromyscus leucopus). As habitats are degraded by fragmentation or other anthropogenic forces, some members of the host community disappear. Thus, species-poor communities tend to have mice, but few other hosts, whereas species-rich communities have mice, plus many other potential hosts. We demonstrate that the most common nonmouse hosts are relatively poor reservoirs for the Lyme spirochete and should reduce the prevalence of the disease by feeding, but rarely infecting, ticks. By accounting for nearly every host species' contribution to the number of larval ticks fed and infected, we show that as new host species are added to a depauperate community, the nymphal infection prevalence, a key risk factor, declines. We identify important "dilution hosts" (e.g., squirrels), characterized by high tick burdens, low reservoir competence, and high population density, as well as "rescue hosts" (e.g., shrews), which are capable of maintaining high disease risk when mouse density is low. Our study suggests that the preservation of vertebrate biodiversity and community composition can reduce the incidence of Lyme disease.
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              Associational Resistance and Associational Susceptibility: Having Right or Wrong Neighbors

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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                July 14 2015
                July 14 2015
                July 14 2015
                June 11 2015
                : 112
                : 28
                : 8667-8671
                Article
                10.1073/pnas.1506279112
                26069208
                142048bd-36a3-4c74-b673-0d546c52d3f0
                © 2015

                Free to read

                http://www.pnas.org/site/misc/userlicense.xhtml

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