Biology subjects

Tucker, C. M.

Publications and source records attributed to Tucker, C. M..

4 recordsLinked to original sources

bioRxiv2022

EDGE2: advancing the prioritisation of threatened evolutionary history for conservation action

The global biodiversity crisis threatens the natural world and its capacity to provide benefits to humans into the future. The conservation of evolutionary history, captured by the measure phylogenetic diversity (PD), is linked to the maintenance of these benefits and future options. The Evolutionarily Distinct and Globally Endangered (EDGE) metric has, since 2007, been used to identify species for conservation action that embody large amounts of threatened evolutionary history. In 2017, we convened a workshop to update the EDGE metric to incorporate advances in the field of phylogenetically-informed conservation. Building on that workshop, we devised the metric EDGE2, which we present here. EDGE2 uses a modular, tiered approach to provide priority rankings--and associated measures of uncertainty in both phylogenetic and extinction risk data--for all species in a clade. EDGE2 takes into account the extinction risk of closely-related species to better reflect the contribution a species is expected to make to overall PD in the future. We applied EDGE2 to the worlds mammals to identify an updated list of priority EDGE species and compare the results with the original EDGE approach. Despite similarity in the priority lists produced between EDGE and EDGE2, EDGE2 places greater priority on species with fewer close relatives on the Tree of Life. As we approach a crossroads for global biodiversity policy, EDGE2 exemplifies how academic and applied conservation biologists can collaborate to guide effective priority-setting to conserve the most irreplaceable components of biodiversity upon which humanity depends.

zoology↗

bioRxiv2020

Accounting for underlying complexities identifies simple hierarchy of trait-environment relationships in Wisconsin forest understory communities

Plant species shift in abundance as environmental conditions change because traits adapt species to particular conditions. As a result, trait values shift along environmental gradients--the so-called trait-environment relationships. These relationships are often assessed by regressing community-weighted mean (CWM) traits on environmental gradients. Such regressions (CWMr) assume that local communities exhibit centered optimum trait-abundance relationships and that traits are not independent from one another. However, the shape of trait-abundance relationships can vary widely along environmental gradients--reflecting the interaction between traits and gradients--and traits are usually interrelated. Accounting for these complexities should improve our ability to accurately describe trait-environment relationships. We tested these ideas by analyzing how abundances of 185 herbaceous understory species distributed among 189 forested sites in Wisconsin, USA, varied in response to four functional traits (vegetative height-VH, leaf size-LS, leaf mass per area-LMA, and leaf carbon content) and six soil and climate variables. A generalized linear mixed model (GLMM) allowed us to assess how the shape of trait-abundance relationships changed along environmental gradients for the 24 trait-environment combinations simultaneously. We then compared the resulting trait-environment relationships to those estimated via CWMr. The GLMM identified five significant trait-environment relationships that together explained [~]40% of variation in species abundances across sites. Temperature played important roles with warmer and more seasonal sites favoring taller plants. Soil texture and temperature seasonality affected LS and LMA more modestly; these seasonality effects declined at more seasonal sites. Only some traits under certain conditions showed centered optimum trait- abundance relationships. Concomitantly, CWMr identified 17 significant trait-environment relationships including effects of temperature, precipitation, and soil on LMA as often reported in other studies. Despite this overidentification, CWMr failed to detect significant temperature-seasonality effects found in the GLMM. Modeling the complexity of how traits and environments interact to affect plant abundance allows us to identify and rank key trait- environment relationships. Although the GLMM model was more complex compared to single CWM regressions, it identified a simpler hierarchy of trait-environment relationships that accurately and reliably predicted responses of forest understory species to gradients in environmental conditions.

ecology↗

bioRxiv2020

Trait-environment relationships are predictive, but not general across species

Understanding the relationships between organisms and their environments is increasingly important given human impacts on global conditions. However, predicting how community diversity and composition will change in the future remains challenging (Mouquet et al 2015). One recent approach is to use traits to mechanistically inform how environmental conditions affect performance (i.e., trait-environment relationships), under the assumptions that these measures relate to each other in predictive and general ways. Unfortunately, results have been inconsistent, ignore phenotypic plasticity, and rely heavily on observational data (Shipley et al 2016). We evaluated the predictability and generality of trait-environment relationships in a controlled experimental microcosm system of four daphniid species. We cultured each species along a stressful gradient (conspecific density), measuring performance (fecundity) and traits related to performance (body length, 2nd antenna length, eye diameter, relative growth rate, and age at first reproduction). Using structural equation models, we evaluated the role of traits in mediating changes in individual fecundity in response to conspecific density. We built models for each species separately considering within-species trait variation, and for all species together by considering all trait variation across the four species. Results from this controlled system highlight that the relationship between individual traits and the environment (conspecific density) is strong and predictive of performance (fecundity), both within- and across-species. However, the specific trait-environment relationships which predicted fecundity differed for each species and differed from the relationships observed in the interspecific model, suggesting a lack of generality. These results will inform and improve the use of traits as a tool for predicting how changing environments will impact species abundances and distributions.

ecology↗

bioRxiv2018

The Effect of Phylogenetic Uncertainty and Imputation on EDGE Scores

Faced with the challenge of saving as much diversity as possible given financial and time constraints, conservation biologists are increasingly prioritizing species on the basis of their overall contribution to evolutionary diversity. Metrics such as EDGE (Evolutionary Distinct and Globally Endangered) have been used to set such evolutionarily-based conservation priorities for a number of taxa, such as mammals, birds, corals, amphibians, and sharks. Each application of EDGE has required some form of correction to account for species whose position within the tree of life are unknown. Perhaps the most advanced of these corrections is phylogenetic imputation, but to date there has been no systematic assessment of both the sensitivity of EDGE scores to a phylogeny missing species, and the impact of using imputation to correct for species missing from the tree. Here we perform such an assessment, by simulating phylogenies, removing some species to make the phylogeny incomplete, imputating the position of those species, and measuring (1) how robust ED scores are for the species that are not removed and (2) how accurate the ED scores are for those removed and then imputed. We find that the EDGE ranking for species on a tree is remarkably robust to missing species from that tree, but that phylogenetic imputation for missing species, while unbiased, does not accurately reconstruct species evolutionary distinctiveness. On the basis of these results, we provide clear guidance for EDGE scoring in the face of phylogenetic uncertainty.

zoology↗