Can functional traits account for phylogenetic signal in community composition?

© 2017 The Authors. New Phytologist © 2017 New Phytologist Trust Phylogenetic and functional trait-based analyses inform our understanding of community composition, yet methods for quantifying the overlap in information derived from functional traits and phylogenies remain underdeveloped. Does adding traits to analyses of community composition reduce the phylogenetic signal in the residual variation? If not, then measured functional traits alone may be insufficient to explain community assembly. We propose a general statistical framework to quantify the proportion of phylogenetic pattern in community composition that remains after including measured functional traits. We then illustrate the framework with applications to two empirical data sets. Both data sets showed strong phylogenetic attraction, with related species likely to co-occur in the same communities. In one data set, including traits eliminated all phylogenetic signals in the residual variation of both abundance and presence/absence patterns. In the second data set, including traits reduced phylogenetic signal in residuals by 25% and 98% for abundance and presence/absence data, respectively. Our framework provides an explicit way to estimate how much phylogenetic community pattern remains in the residual variation after including measured functional traits. Knowing that functional traits account for most of the phylogenetic pattern should provide confidence that important traits for phylogenetic community structure have been identified. Conversely, knowing that there is unexplained residual phylogenetic information should spur the search for additional functional traits or other processes underlying community assembly

Do Movement Patterns and Habitat Use Differ Between Optimal- and Suboptimal-sized Northern Bobwhite Coveys?

The group size of social animals and spatial structure of the environment can affect group behavior and movement decisions. Our objective was to investigate movement patterns and habitat use of northern bobwhite coveys (Colinus virginianus) of different size. Using radiotelemetry, we continuously monitored covey group size, daily movement, and habitat use on 12 independent 259-ha study areas in eastern Kansas, USA, during the winters between 1997 and 2000. We used correlated random walk models and fractal dimension models to determine if covey size affected movement characteristics or habitat selection. Intermediate-sized coveys (9–12 individuals, close to optimal covey size) exhibited daily movements that were substantially smaller and weekly home ranges that were more composed of woody escape cover than coveys of smaller or larger sizes. From the fractal dimension analyses, these coveys exhibited movement in between linear and a random walk at small spatial scales but very linear at large spatial scales. Large coveys had increased daily movement and tended to move in straighter lines (as indicated by the high proportion of turning angles [i.e., the angle between an initial direction and a new direction] around 0° and 180° and their multiscale fractal dimension) and they incorporated more cropland into their range, presumably to meet the feeding requirements of a larger group. In contrast, small coveys (1–4 individuals) tended to move more and increase the size of their home range, travel with a greater diversity of turning angles, and show movement patterns that were largely tortuous across a greater number of habitat patches at larger spatial scales (700 m). Small coveys have lower fitness and add new membership to increase fitness so it is possible that the movement behavior we observed represented a shift into a foray mode where bobwhites were searching for new membership. For areas with small populations and covey sizes, this information will help biologists better plan for habitat management to assist these coveys with their winter fitness

phyr: Anrpackage for phylogenetic species-distribution modelling in ecological communities

Model-based approaches are increasingly popular in ecological studies. A good example of this trend is the use of joint species distribution models to ask questions about ecological communities. However, most current applications of model-based methods do not include phylogenies despite the well-known importance of phylogenetic relationships in shaping species distributions and community composition. In part, this is due to a lack of accessible tools allowing ecologists to fit phylogenetic species distribution models easily. To fill this gap, therpackagephyr(pronounced fire) implements a suite of metrics, comparative methods and mixed models that use phylogenies to understand and predict community composition and other ecological and evolutionary phenomena. Thephyrworkhorse functions are implemented in C++ making all calculations and model estimations fast. phyrcan fit a variety of models such as phylogenetic joint-species distribution models, spatiotemporal-phylogenetic autocorrelation models, and phylogenetic trait-based bipartite network models.phyralso estimates phylogenetically independent trait correlations with measurement error to test for adaptive syndromes and performs fast calculations of common alpha and beta phylogenetic diversity metrics. Allphyrmethods are united under Brownian motion or Ornstein-Uhlenbeck models of evolution, and phylogenetic terms are modelled as phylogenetic covariance matrices. The functions and model formula syntax we propose inphyrprovide an easy-to-use collection of tools that we hope will ignite the use of phylogenies to address a variety of ecological questions

Ecological history affects zooplankton community responses to acidification

The effects of ecological history are frequently ignored in attempts to predict community responses to environmental change. In this study, we explored the possibility that ecological history can cause differences in community responses to perturbation using parallel acidification experiments in three sites with different pH histories in the Northern Highland Lake District of Wisconsin, USA. In Trout Lake, high acid neutralizing capacity had historically buffered changes in pH. In contrast, the two basins of Little Rock Lake (Little Rock-Reference and Little Rock-Treatment) had experienced seasonal fluctuations in pH. Furthermore, the two lake basins were separated with a curtain and Little Rock-Treatment was experimentally acidified in the late 1980s. In each site, we conducted mesocosm experiments to compare zooplankton community dynamics in control (ambient pH) and acidified (pH 4.7) treatments. Zooplankton community responses were strongest in Trout Lake and weakest in Little Rock-Treatment suggesting that ecological history affected responses to acidification. In part, variation in community sensitivity to acidification was driven by differences in species composition. However, the results of a reciprocal transplant experiment indicated that changes in the acid tolerance of populations during past acidification events may make zooplankton communities less sensitive to subsequent pH stress. Our study highlights the role that ecological history may play in community-level responses to environmental change

Midge-stabilized sediment drives the composition of benthic cladoceran communities in Lake Mývatn, Iceland

The importance of environmental disturbances as drivers of ecological communities depends not only on the magnitude of the disturbance, but also on the disturbance-specific sensitivity of the community. Organisms that alter the physical structure of their surroundings can affect the sensitivity of their habitat to environmental disturbance, and may alter the potential for disturbance to shape ecological communities. Such organisms therefore act as ecosystem engineers by indirectly modifying the resources available to other species. The benthos of shallow, eutrophic Lake Mývatn, Iceland, is frequently disturbed by wind events that lead to sediment resuspension. The impact of wind, however, depends on the abundance of midges (Chironomidae) whose larval tubes bind sediment and reduce wind-driven resuspension. Here, we investigate the long-term effect of fluctuations in midge abundance on the benthic cladoceran community using two lake sediment cores representing 30 and 140 years of deposition. In both cores, midge remains show a significant positive correlation with abundance of a large benthic surface-dwelling cladoceran, Eurycercus lamellatus, relative to the abundance of a small within-sediment-dwelling cladoceran, Alona rectangula. To experimentally investigate whether this shift could have been caused by midges acting as ecosystem engineers, we subjected cladoceran communities to sediment resuspension events within mesocosms. We found a significant decrease in abundance of the large epibenthic E. lamellatus relative to the abundance of small infaunal Alona spp. when subjected to disturbance. These findings show that physical alteration of benthic sediment and hence the sensitivity of the sediment to disturbance may explain the community shift in cladocerans observed with fluctuating midge abundance in Lake Mývatn.National Science Foundation Graduate Research Fellowship. Grant Number: DGE-1256259 LTREB. Grant Number: DEB-1052160Peer Reviewe

Methods For Detecting Early Warnings Of Critical Transitions In Time Series Illustrated Using Simulated Ecological Data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.Organismic and Evolutionary Biolog

Association of Accelerometry-Measured Physical Activity and Cardiovascular Events in Mobility-Limited Older Adults: The LIFE (Lifestyle Interventions and Independence for Elders) Study.

BACKGROUND:Data are sparse regarding the value of physical activity (PA) surveillance among older adults-particularly among those with mobility limitations. The objective of this study was to examine longitudinal associations between objectively measured daily PA and the incidence of cardiovascular events among older adults in the LIFE (Lifestyle Interventions and Independence for Elders) study. METHODS AND RESULTS:Cardiovascular events were adjudicated based on medical records review, and cardiovascular risk factors were controlled for in the analysis. Home-based activity data were collected by hip-worn accelerometers at baseline and at 6, 12, and 24 months postrandomization to either a physical activity or health education intervention. LIFE study participants (n=1590; age 78.9±5.2 [SD] years; 67.2% women) at baseline had an 11% lower incidence of experiencing a subsequent cardiovascular event per 500 steps taken per day based on activity data (hazard ratio, 0.89; 95% confidence interval, 0.84-0.96; P=0.001). At baseline, every 30 minutes spent performing activities ≥500 counts per minute (hazard ratio, 0.75; confidence interval, 0.65-0.89 [P=0.001]) were also associated with a lower incidence of cardiovascular events. Throughout follow-up (6, 12, and 24 months), both the number of steps per day (per 500 steps; hazard ratio, 0.90, confidence interval, 0.85-0.96 [P=0.001]) and duration of activity ≥500 counts per minute (per 30 minutes; hazard ratio, 0.76; confidence interval, 0.63-0.90 [P=0.002]) were significantly associated with lower cardiovascular event rates. CONCLUSIONS:Objective measurements of physical activity via accelerometry were associated with cardiovascular events among older adults with limited mobility (summary score >10 on the Short Physical Performance Battery) both using baseline and longitudinal data. CLINICAL TRIAL REGISTRATION:URL: http://www.clinicaltrials.gov. Unique identifier: NCT01072500

Data from: R2s for correlated data: phylogenetic models, LMMs, and GLMMs

Many researchers want to report an R2 to measure the variance explained by a model. When the model includes correlation among data, such as phylogenetic models and mixed models, defining an R2 faces two conceptual problems. (i) It is unclear how to measure the variance explained by predictor (independent) variables when the model contains covariances. (ii) Researchers may want the R2 to include the variance explained by the covariances by asking questions such as “How much of the data is explained by phylogeny?” Here, I investigate three R2s for phylogenetic and mixed models. R2resid is an extension of the ordinary least-squares R2 that weights residuals by variances and covariances estimated by the model; it is closely related to R2glmm presented by Nakagawa and Schielzeth (2013). R2pred is based on predicting each residual from the fitted model and computing the variance between observed and predicted values. R2lik is based on the likelihood of fitted models and therefore reflects the amount of information that the models contain. These three R2s are formulated as partial R2s, making it possible to compare the contributions of predictor variables and variance components (phylogenetic signal and random effects) to the fit of models. Because partial R2s compare a full model with a reduced model without components of the full model, they are distinct from marginal R2s that partition additive components of the variance. The properties of the R2s for phylogenetic models were assessed using simulations for continuous and binary response data (phylogenetic generalized least squares and phylogenetic logistic regression). Because the R2s are designed broadly for any model for correlated data, the R2s were also compared for LMMs and GLMMs. R2resid, R2pred, and R2lik all have similar performance in describing the variance explained by different components of models. However, R2pred gives the most direct answer to the question of how much variance in the data is explained by a model. R2resid is most appropriate for comparing models fit to different datasets, because it does not depend on sample sizes. And R2lik is most appropriate to assess the importance of different components within the same model applied to the same data, because it is most closely associated with statistical significance tests