Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation

Merow, Cory; Latimer, Andrew M.; Silander, John A.

doi:10.1890/10-1174.1

Cited by 21 publications

(19 citation statements)

References 42 publications

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“…In the montane and subalpine forests of Arizona, stem traits, height and flowering phenology were most important . In the fynbos of South Africa, leaf traits, stem traits and flowering phenology were most important (Merow, Latimer & Silander 2011). In the tussock grasslands of New Zealand, leaf traits, root traits, height and life-history traits were most important (Lalibert e et al 2012).…”

Section: Plant Strategies and Trait Dimensionsmentioning

confidence: 99%

The intrinsic dimensionality of plant traits and its relevance to community assembly

2013

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Summary1. Plants are multifaceted organisms that have evolved numerous solutions to the problem of establishing, growing and reproducing with limited resources. The intrinsic dimensionality of plant traits is the minimum number of independent axes of variation that adequately describes the functional variation among plants and is therefore a fundamental quantity in comparative plant ecology. Given the large number of functional traits that are measured on plants, the dimensionality of plant form and function is potentially vast. 2. A variety of linear and nonlinear methods were used to estimate the intrinsic dimensionality of three large trait data sets. The results of these analyses indicate that while the dimensionality of plant traits is generally larger than we have admitted in the past, it does not exceed six in the most comprehensive data set. 3. The dimensionality of plant form and function is a blessing, not a curse. The higher the intrinsic dimension of traits in an analysis, the more easily our models will be able to accurately discriminate species in trait space and therefore be able to predict species distributions and abundances. Recent analyses indicate that the ability to predict community composition increases rapidly with additional traits, but reaches a plateau after four to eight traits. 4. Synthesis. There appears to be a tractable upper limit to the dimensionality of plant traits. To optimize research efficiency for advancing our understanding of trait-based community assembly, ecologists should minimize the number of traits while maximizing the number of dimensions, because including multiple correlated traits does not yield dividends and including more than eight traits leads to diminishing returns. It is recommended to measure traits from multiple organs whenever possible, especially leaf, stem, root and flowering traits, given their consistent performance in explaining community assembly across different ecosystems.

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Section: Plant Strategies and Trait Dimensionsmentioning

confidence: 99%

The intrinsic dimensionality of plant traits and its relevance to community assembly

2013

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“…It uses a matrix of species trait means and a vector of predicted communityweighted mean traits as input to obtain the most even probability distribution of species relative abundance. It has been shown in several studies how significant variation in species abundances can be explained (Sonnier et al 2010;Merow et al 2011;Laliberté et al 2012). The second model, entitled "Traitspace", has been proposed by Laughlin (Laughlin et al 2012;Laughlin and Laughlin 2013;Laughlin 2014).…”

Section: From Empirical To Predictive Approachmentioning

confidence: 99%

Grass strategies and grassland community responses to environmental drivers: a review

Pontes

Maire

Schellberg

et al. 2015

Agron. Sustain. Dev.

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Grassland covers about one quarter of the Earth's land area and is currently estimated to contribute to the livelihoods of over 800 million people. Grassland provides ecosystem goods and services, mainly through the provisioning of milk and meat. Therefore, the proper use of grasslands will be essential for feeding the nine billion people that will inhabit planet Earth by 2050. In the context of a changing climate, we should better understand the interactions of environment, management and grass crop at individual, community and ecosystem levels. Functional ecology focuses on the roles and functions that species play in the community or ecosystem in which they occur. Functional ecology thus aims to understand how plant species adapt to environmental conditions and how management can alter this adaptation. Here, we review the latest advances in plant functional traits research and on species strategies to the main environmental factors occurring in grassland ecosystems: nutrient availability, grazing, cutting and shading. Functional ecology also provides a framework to better understand how species strategies interact with the species composition at the community level. Therefore, the literature on community assembling theories in relation to ecosystem processes most relevant to grassland management and services is also reviewed. Finally, future research questions and some new orientations for grassland experts are offered in order to meet the challenge of maintaining productivity and preservation of these semi-natural environments in the face of global change.

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“…The CATS model has shown potential to become a powerful predictive tool (Sonnier, Shipley & Navas ; Laughlin et al . ; Merow, Latimer & Silander ; Shipley et al . ; Laliberté et al .…”

Section: Introductionmentioning

confidence: 99%

Trait‐based climate change predictions of plant community structure in arid steppes

et al. 2012

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Summary1. Global climate change is possibly one of the most important challenges for current and future human populations due to its wide-ranging effects on ecosystems. Global prediction models suggest that in some areas of the world (e.g. Northern Africa, Central America) an increase in aridity might strongly disturb agricultural production and affect food security. To counterbalance these negative effects, reliable predictive models are needed to anticipate ecosystem changes. 2. We tested the ability of the Community Assembly by Trait-based Selection (CATS) model that is based on the principle of maximum entropy and trait-based environmental filtering, to predict actual and future plant community composition in the arid steppes of eastern Morocco. Specifically, we asked whether this model was adequate for predicting actual community composition, based on predicted community-weighted mean (CWM) traits, and what would be the changes in community composition under various scenarios of climate change for the period 2080-2099. 3. The CATS model could predict > 90% of actual community composition if the actual CWM traits were known but only~40% if the CWM values were predicted from estimated aridity and grazing values. The predictions of community composition for 2080-2099 suggested that, regardless of the climate change scenario considered, the dominant group in grazed and ungrazed sites would shift from ruderal species to stress-tolerant sub-shrubs, which would constitute up over 80% of total community composition in some cases. 4. Synthesis. Our findings suggest that effects of climate change will strongly modify plant community structure in arid steppes, possibly accentuating the process of desertification, and reducing the pastoral value of the vegetation. Future research efforts should concentrate on the identification of strong trait-environment relationships to improve model predictions.

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Can entropy maximization use functional traits to explain species abundances? A comprehensive evaluation

Cited by 21 publications

References 42 publications

The intrinsic dimensionality of plant traits and its relevance to community assembly

The intrinsic dimensionality of plant traits and its relevance to community assembly

Grass strategies and grassland community responses to environmental drivers: a review

Trait‐based climate change predictions of plant community structure in arid steppes

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