ORCID Profile
0000-0003-4671-2225
Current Organisations
University of Southampton
,
University of Reading
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Publisher: Wiley
Date: 11-07-2021
Abstract: The stress‐gradient hypothesis (SGH) provides a conceptual framework for explaining how environmental context determines the nature of biotic interactions. It may be also useful for predicting geographic variability in the effect of management interventions on biological invasions. We aimed to test hypotheses consistent with the SGH to explain context dependency in bamboo invasion of secondary forests in Japan, and establish a predictive understanding of forest management impacts on invasion. We use a priori physiological knowledge of invasive giant bamboo, Phyllostachys bambusoides , to generate hypotheses consistent with the SGH. We modelled variation in giant bamboo occupancy within 810 secondary forest plots across the broad environmental gradients of Japan using a national vegetation database. Consistent with the SGH, we find that the effect of tree canopy cover on bamboo occupancy depends on interactions between solar radiation and mean annual temperature. In cool regions with high solar radiation—stressful conditions for bamboo—shade cast by dense canopies facilitates invasion. However, in warmer regions that are more benign, dense canopies tend to inhibit spread via competition for light, space and other resources. Synthesis and applications . We provide evidence that the stress‐gradient hypothesis can inform practical recommendations for invasive species control. We characterised geographic variability in the effect of forest thinning, a widespread management intervention used to enhance forest bio ersity, on the risk of bamboo spread into secondary forests in Japan. Thinning forest canopies to increase understorey light radiation should limit bamboo spread in cooler regions, while tree planting to increase canopy shade should limit bamboo spread in warmer regions.
Publisher: Springer Science and Business Media LLC
Date: 03-11-2022
DOI: 10.1038/S41559-022-01891-Z
Abstract: Synthesis of primary ecological data is often assumed to achieve a notion of 'generality', through the quantification of overall effect sizes and consistency among studies, and has become a dominant research approach in ecology. Unfortunately, ecologists rarely define either the generality of their findings, their estimand (the target of estimation) or the population of interest. Given that generality is fundamental to science, and the urgent need for scientific understanding to curb global scale ecological breakdown, loose usage of the term 'generality' is problematic. In other disciplines, generality is defined as comprising both generalizability-extending an inference about an estimand from the s le to the population-and transferability-the validity of estimand predictions in a different s ling unit or population. We review current practice in ecological synthesis and demonstrate that, when researchers fail to define the assumptions underpinning generalizations and transfers of effect sizes, generality often misses its target. We provide guidance for communicating nuanced inferences and maximizing the impact of syntheses both within and beyond academia. We propose pathways to generality applicable to ecological syntheses, including the development of quantitative and qualitative criteria with which to license the transfer of estimands from both primary and synthetic studies.
Publisher: California Digital Library (CDL)
Date: 19-05-2022
Publisher: Springer Science and Business Media LLC
Date: 08-01-2020
DOI: 10.1038/S41597-019-0344-7
Abstract: The use of functional information in the form of species traits plays an important role in explaining bio ersity patterns and responses to environmental changes. Although relationships between species composition, their traits, and the environment have been extensively studied on a case-by-case basis, results are variable, and it remains unclear how generalizable these relationships are across ecosystems, taxa and spatial scales. To address this gap, we collated 80 datasets from trait-based studies into a global database for metaCommunity Ecology: Species, Traits, Environment and Space “CESTES”. Each dataset includes four matrices: species community abundances or presences/absences across multiple sites, species trait information, environmental variables and spatial coordinates of the s ling sites. The CESTES database is a live database: it will be maintained and expanded in the future as new datasets become available. By its harmonized structure, and the ersity of ecosystem types, taxonomic groups, and spatial scales it covers, the CESTES database provides an important opportunity for synthetic trait-based research in community ecology.
Publisher: Wiley
Date: 26-12-2023
DOI: 10.1111/ELE.14144
Abstract: The log response ratio, lnRR, is the most frequently used effect size statistic for meta‐analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the s ling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate s ling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its in idual study‐specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the s ling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta‐analyses of lnRR, regardless of ‘missingness’.
Publisher: Springer Science and Business Media LLC
Date: 24-04-2023
DOI: 10.1186/S13750-023-00301-6
Abstract: Meta-analysis is a quantitative way of synthesizing results from multiple studies to obtain reliable evidence of an intervention or phenomenon. Indeed, an increasing number of meta-analyses are conducted in environmental sciences, and resulting meta-analytic evidence is often used in environmental policies and decision-making. We conducted a survey of recent meta-analyses in environmental sciences and found poor standards of current meta-analytic practice and reporting. For ex le, only ~ 40% of the 73 reviewed meta-analyses reported heterogeneity (variation among effect sizes beyond s ling error), and publication bias was assessed in fewer than half. Furthermore, although almost all the meta-analyses had multiple effect sizes originating from the same studies, non-independence among effect sizes was considered in only half of the meta-analyses. To improve the implementation of meta-analysis in environmental sciences, we here outline practical guidance for conducting a meta-analysis in environmental sciences. We describe the key concepts of effect size and meta-analysis and detail procedures for fitting multilevel meta-analysis and meta-regression models and performing associated publication bias tests. We demonstrate a clear need for environmental scientists to embrace multilevel meta-analytic models, which explicitly model dependence among effect sizes, rather than the commonly used random-effects models. Further, we discuss how reporting and visual presentations of meta-analytic results can be much improved by following reporting guidelines such as PRISMA-EcoEvo (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Ecology and Evolutionary Biology). This paper, along with the accompanying online tutorial, serves as a practical guide on conducting a complete set of meta-analytic procedures (i.e., meta-analysis, heterogeneity quantification, meta-regression, publication bias tests and sensitivity analysis) and also as a gateway to more advanced, yet appropriate, methods.
Publisher: Wiley
Date: 10-05-2021
DOI: 10.1111/GEB.13318
Abstract: Contemporary climate change and biological invasions are two main drivers of bio ersity redistribution. Interactive effects between these drivers have been reported in a variety of studies, yet results are conflicting. Some studies find that contemporary climate change facilitates the spread and success of non‐native species, especially those with broad physiological tolerances. Other studies conclude that non‐natives are vulnerable to current and future changes in climatic conditions. Given that most studies have focused on terrestrial species, here we contribute to this debate by analysing responses of marine native and non‐native fauna and flora to key climate‐related stressors, namely increased temperature (warming) and decreased salinity (freshening). Global. 2002–2019. Marine benthic macrophytes and invertebrates. We conducted a meta‐analysis of experiments investigating the performance (e.g. growth, survival and reproduction) of benthic species in response to warming and freshening. We found that non‐native species tended to respond positively to elevated temperature, whereas the performance of native species declined. Similarly, decreased salinity negatively affected the biological processes of native species, but non‐natives showed neutral or negative overall responses to freshening. We find evidence that non‐native species outperform natives under a wide variety of warming and freshening conditions. The growth and reproduction of non‐natives are enhanced by warmer temperatures, and thus ocean warming is expected to facilitate future spread and success of non‐native species. Increased freshening along future coastal areas, however, will likely have a negative impact in both native and non‐native species and thus is expected to be a driver of significant change in coastal marine ecosystems. Our comprehensive analysis highlighted the need to expand our understanding of climate change effects beyond warming and specifically, studies focusing on salinity changes.
Publisher: Wiley
Date: 03-2023
DOI: 10.1111/BRV.12939
Abstract: Ecologists routinely use statistical models to detect and explain interactions among ecological drivers, with a goal to evaluate whether an effect of interest changes in sign or magnitude in different contexts. Two fundamental properties of interactions are often overlooked during the process of hypothesising, visualising and interpreting interactions between drivers: the measurement scale – whether a response is analysed on an additive or multiplicative scale, such as a ratio or logarithmic scale and the symmetry – whether dependencies are considered in both directions. Overlooking these properties can lead to one or more of three inferential errors: misinterpretation of ( i ) the detection and magnitude (Type‐D error), and ( ii ) the sign of effect modification (Type‐S error) and ( iii ) misidentification of the underlying processes (Type‐A error). We illustrate each of these errors with a broad range of ecological questions applied to empirical and simulated data sets. We demonstrate how meta‐analysis, a widely used approach that seeks explicitly to characterise context dependence, is especially prone to all three errors. Based on these insights, we propose guidelines to improve hypothesis generation, testing, visualisation and interpretation of interactions in ecology.
Publisher: Center for Open Science
Date: 14-06-2022
Abstract: The inherent complexity and multi-causality of nature makes attempts to gain understanding and guidance about critical issues including rates of bio ersity loss, or the effectiveness of actions such as tree planting, wickedly difficult and often subject to poorly-substantiated assertions about their generality. Synthesis of primary ecological data is often assumed to achieve a notion of ‘generality’, through the quantification of overall effect sizes and consistency among studies, and has become a dominant research approach in ecology. Assertions about generality should raise the question: what exactly is generality? Ecologists rarely define either the generality of their findings, their estimand (what is estimated, based on the question of interest) or population of interest. Given that generality is fundamental to the philosophy of science, and the urgent need for scientific understanding and applied conservation actions to curb ecological breakdown, the loose use of the term ‘generality’ is problematic. In other disciplines generality is defined as comprising both generalisability: extending an inference about an estimand from the s le to the population, and transferability: the validity of estimand predictions in a different s ling unit or population. We review current practice in ecological synthesis, and demonstrate that by failing to define the assumptions that underpin generalisations and transfers of effect sizes, generality often misses its target. We then provide guidance for communicating nuanced inferences, and maximising the impact of syntheses both within and beyond academia. We finally propose pathways to generality applicable to ecological syntheses, which includes development of quantitative and qualitative criteria with which to license the transfer of estimands from both primary and synthetic studies.
Publisher: Wiley
Date: 29-05-2020
DOI: 10.1002/ECE3.6368
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Rebecca Spake.