Using Natural Gradients to Infer a Potential Response to Climate Change: An Example on the Reproductive Performance of Dactylis Glomerata L.

Dainese, Matteo

doi:10.3390/biology1030857

Cited by 5 publications

(4 citation statements)

References 37 publications

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“…Up until now, hierarchical partitioning has been predominantly applied to single models (also called global models because they include all hypothesized explanatory variables; for example in Murray & Conner ; Dainese ). We term the independent effect estimated for j ‐th explanatory variable ( x j ) of the global model

I_{g l o b a l false(x_{j} false)}

.…”

Section: Methodsmentioning

confidence: 99%

Quantifying variable importance in a multimodel inference framework

2015

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Summary The sum of Akaike weights (SW) is often used to quantify relative variable importance (RVI) within the information‐theoretic (IT) multimodel inference framework. A recent study (Galipaud et al. 2014, Methods in Ecology and Evolution 5: 983) questioned the validity of the SW approach. Regrettably, this study is flawed because SW was evaluated with an inappropriate benchmark. Irrespective of this study's methodological issues, RVI metrics based on the relative contribution of explanatory variables in explaining the variance in the response variable (partitioned R2‐based) are lacking in multimodel inference. We re‐evaluated the validity of SW by repeating Galipaud et al.'s experiment but with an appropriate benchmark. When explanatory variables are uncorrelated, the quantity that SW estimates (i.e. the probability that a variable is included in the actual best IT model) is monotonically related to squared zero‐order correlation coefficients (r2) between explanatory variables and the response variable. The degree of correspondence between SW and r2 rankings (not values) of variables in data sets with uncorrelated explanatory variables was therefore used as a benchmark to evaluate the validity of SW as a RVI metric. To address the lack of partitioned R2‐based RVI metrics in multimodel inference, we proposed 2 metrics: (a) Iweighted, the average model probability‐weighted partitioned R2; and (b) Ibest, the partitioned R2 derived from the best IT model. We performed Monte Carlo simulations to evaluate the utility of Iweighted and Ibest versus partitioned R2 derived from the global model (Iglobal). SW rankings matched r2 rankings of variables; therefore, SW is a valid measure of RVI. Among partitioned R2‐based metrics, Iweighted and Iglobal were generally more accurate in estimating the population partitioned R2. Iweighted performed better when explanatory variables were uncorrelated, whereas Iglobal was better in smaller data sets with correlated explanatory variables. To improve the utility of Iweighted in such data sets, we proposed approaches to eliminate or reduce the influence of correlated variables. Despite recent criticisms, our results show that SW is a valid RVI metric. To quantify RVI in terms of the R2 explained by each variable, Iweighted and Iglobal are the preferred RVI metrics.

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I_{g l o b a l false(x_{j} false)}

.…”

Section: Methodsmentioning

confidence: 99%

Quantifying variable importance in a multimodel inference framework

2015

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“…Ideally, observations should be made over decades, which is rarely possible. Elevation gradients are a powerful tool for testing ecological responses of biota to geophysical influences such as temperature [4], and the influence of changing environment experienced along such gradients has been suggested as analogous to climate warming [5,6]. Hodkinson [6] provided a series of examples where parasitism was measured along elevation gradients.…”

Section: Introductionmentioning

confidence: 99%

Response of insect parasitism to elevation depends on host and parasitoid life-history strategies

2013

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How global warming will affect insect parasitoids and their role as natural enemies of insect pests is difficult to assess within a short period of time. Considering that elevation gradients can be used as analogues for global warming, we carried out meta-analyses of 27 correlations between parasitoid richness and elevation and 140 correlations between parasitism rate and elevation in natural and semi-natural environments. We also explored various covariates that may explain the observed responses. Both parasitism rates and parasitoid species richness significantly decreased with increasing elevation. The decrease was greater for ectoparasitoids and parasitoids of ectophagous insects than for endoparasitoids and parasitoids of endophagous hosts, possibly because these latter are better protected from adverse and extreme climatic conditions occurring at higher elevations. Although our results suggest an increase of parasitism with increasing temperature, other factors regulating herbivorous insects have to be considered before concluding that climate warming will lead to a decrease in pest density.

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“…Some predictions of biotic responses to climate rely on envelope models (Pearson and Dawson 2003). While substantial work and debate focuses on bioclimate envelope modeling to predict species distributions (Busby 1991;Pearson and Dawson 2003;Elith and Leathwick 2009;Dainese 2012), few studies explore the issue of compositional responses of ecosystems to climate change, particularly at the floristic level (Baselga and Araújo 2009; Pucko et al 2011;Friggens et al 2012). Halpin (1997) showed that when the envelope of climatic conditions describing the distribution of an ecosystem no longer exists at a location, the ecosystem contracts and eventually disappears.…”

Section: Introductionmentioning

confidence: 99%

Climate change and ecosystem composition across large landscapes

Jennings

Harris

2016

Landscape Ecol

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Context Climate change alters the vegetation composition and functioning of ecosystems. Measuring the magnitude, direction, and rate of changes in vegetation composition induced by climate remains a serious and unmet challenge. Such information is required for a predictive capability of how individual ecosystem will respond to future climates. Objectives Our objectives were to identify the relationships between 20 climate variables and 39 ecosystems across the southwestern USA. We sought to understand the magnitude of relationships between variation in vegetation composition and bioclimatic variables as well as the amount of ecosystem area expected to be affected by future climate changes.Methods Bioclimatic variables best explaining the plant species composition of each ecosystem were identified. The strength of relationships between beta turnover and bioclimate gradients was calculated, the spatial concordance of ecosystem and bioclimate configurations was shown, and the area of suitable climate remaining within the boundaries of contemporary ecosystems under future climate projections was measured.

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Using Natural Gradients to Infer a Potential Response to Climate Change: An Example on the Reproductive Performance of Dactylis Glomerata L.

Cited by 5 publications

References 37 publications

Quantifying variable importance in a multimodel inference framework

Quantifying variable importance in a multimodel inference framework

Response of insect parasitism to elevation depends on host and parasitoid life-history strategies

Climate change and ecosystem composition across large landscapes

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