An official website of the United States government
Abstract Matrix population models are frequently built and used by ecologists to analyse demography and elucidate the processes driving population growth or decline. Life Table Response Experiments (LTREs) are comparative analyses that decompose the realized difference or variance in population growth rate () into contributions from the differences or variances in the vital rates (i.e. the matrix elements). Since their introduction, LTREs have been based on approximations and have not included biologically relevant interaction terms.We used the functional analysis of variance framework to derive an exact LTRE method, which calculates the exact response of to the difference or variance in a given vital rate, for all interactions among vital rates—including higher‐order interactions neglected by the classical methods. We used the publicly available COMADRE and COMPADRE databases to perform a meta‐analysis comparing the results of exact and classical LTRE methods. We analysed 186 and 1487 LTREs for animal and plant matrix population models, respectively.We found that the classical methods often had small errors, but that very high errors were possible. Overall error was related to the difference or variance in the matrices being analysed, consistent with the Taylor series basis of the classical method. Neglected interaction terms accounted for most of the errors in fixed design LTRE, highlighting the importance of two‐way interaction terms. For random design LTRE, errors in the contribution terms present in both classical and exact methods were comparable to errors due to neglected interaction terms. In most examples we analysed, evaluating exact contributions up to three‐way interaction terms was sufficient for interpreting 90% or more of the difference or variance in .Relative error, previously used to evaluate the accuracy of classical LTREs, is not a reliable metric of how closely the classical and exact methods agree. Error compensation between estimated contribution terms and neglected contribution terms can lead to low relative error despite faulty biological interpretation. Trade‐offs or negative covariances among matrix elements can lead to high relative error despite accurate biological interpretation. Exact LTRE provides reliable and accurate biological interpretation, and the R packageexactLTREmakes the exact method accessible to ecologists.
more »
« less
Random errors are neither: On the interpretation of correlated data
Abstract Many statistical models currently used in ecology and evolution account for covariances among random errors. Here, I address five points: (i) correlated random errors unite many types of statistical models, including spatial, phylogenetic and time‐series models; (ii) random errors are neither unpredictable nor mistakes; (iii) diagnostics for correlated random errors are not useful, but simulations are; (iv) model predictions can be made with random errors; and (v) can random errors be causal?These five points are illustrated by applying statistical models to analyse simulated spatial, phylogenetic and time‐series data. These three simulation studies are paired with three types of predictions that can be made using information from covariances among random errors: predictions for goodness‐of‐fit, interpolation, and forecasting.In the simulation studies, models incorporating covariances among random errors improve inference about the relationship between dependent and independent variables. They also imply the existence of unmeasured variables that generate the covariances among random errors. Understanding the covariances among random errors gives information about possible processes underlying the data.Random errors are caused by something. Therefore, to extract full information from data, covariances among random errors should not just be included in statistical models; they should also be studied in their own right. Data are hard won, and appropriate statistical analyses can make the most of them.
more »
« less
- Award ID(s):
- 2134446
- PAR ID:
- 10489669
- Publisher / Repository:
- John Wiley and Sons
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 13
- Issue:
- 10
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 2092 to 2105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Theory predicts that trophic specialization (i.e. low dietary diversity) should make consumer populations sensitive to environmental disturbances. Yet diagnosing specialization is complicated both by the difficulty of precisely quantifying diet composition and by definitional ambiguity: what makes a diet ‘diverse’?We sought to characterize the relationship between taxonomic dietary diversity (TDD) and phylogenetic dietary diversity (PDD) in a species‐rich community of large mammalian herbivores in a semi‐arid East African savanna. We hypothesized that TDD and PDD would be positively correlated within and among species, because taxonomically diverse diets are likely to include plants from many lineages.By using DNA metabarcoding to analyse 1,281 faecal samples collected across multiple seasons, we compiled high‐resolution diet profiles for 25 sympatric large‐herbivore species. For each of these populations, we calculated TDD and PDD with reference to a DNA reference library for local plants.Contrary to our hypothesis, measures of TDD and PDD were either uncorrelated or negatively correlated with each other. Thus, these metrics reflect distinct dimensions of dietary specialization both within and among species. In general, grazers and ruminants exhibited greater TDD, but lower PDD, than did browsers and non‐ruminants. We found significant seasonal variation in TDD and/or PDD for all but four species (Grevy's zebra, buffalo, elephant, Grant's gazelle); however, the relationship between TDD and PDD was consistent across seasons for all but one of the 12 best‐sampled species (plains zebra).Our results show that taxonomic generalists can be phylogenetic specialists, and vice versa. These two dimensions of dietary diversity suggest contrasting implications for efforts to predict how consumers will respond to climate change and other environmental perturbations. For example, populations with low TDD may be sensitive to phylogenetically ‘random’ losses of food species, whereas populations with low PDD may be comparatively more sensitive to environmental changes that disadvantage entire plant lineages—and populations with low dietary diversity in both taxonomic and phylogenetic dimensions may be most vulnerable of all.more » « less
-
Abstract Predictions of biodiversity trajectories under climate change are crucial in order to act effectively in maintaining the diversity of species. In many ecological applications, future predictions are made under various global warming scenarios, as described by a range of different climate models. We propose a clustering methodology to synthesize and interpret the outputs of these various predictions.We propose an interpretable and flexible two‐step methodology to measure the similarity between predicted species range maps and to cluster the future scenario predictions utilizing a spectral clustering technique. We implement and provide code for this method.We find that clustering based on predicted species range maps is mainly driven by the amount of warming rather than climate model or future scenario. We contrast this with clustering based only on predicted climate variables, which is driven primarily by climate models, that is, scenarios of the same climate model are clustered together, even when the amount of warming input to the models is varied.The differences between species‐based and climate‐based clusterings illustrate that it is crucial to incorporate ecological information to understand the relevant differences between climate models. Our findings can be used to better synthesize forecasts of biodiversity change under the wide spectrum of results that emerge when considering potential future scenarios.more » « less
-
Abstract Relationships between plant biodiversity and productivity are highly variable across studies in managed grasslands, partly because of the challenge of accounting for confounding's and reciprocal relationships between biodiversity and productivity in observational data collected at a single point in time. Identifying causal effects in the presence of these challenges requires new analytical approaches and repeated observations to determine the temporal ordering of effects.Though rarely available, data collected at multiple time points within a growing season can help to disentangle the effects of biodiversity on productivity and vice versa. Here we advance this understanding using seasonal grassland surveys from 150 managed grassland sites repeated over 2 years, along with statistical methods that are relatively new in ecology, that aim to infer causal relationships from observational data. We compare our approach to common methods used in ecology, that is, mixed‐effect models, and to analyses that use observations from only one point in time within the growing seasons.We find that mixed models overestimated the effect of biodiversity on productivity by two standard errors as compared to our main models, which find no evidence for a strong positive effect. For the effect of productivity on biodiversity we found a negative effect using mixed models which was highly sensitive to the time at which the data was collected within the growing season. In contrast, our main models found no evidence for an effect. Conventional models overestimated the effects between biodiversity and productivity, likely due to confounding variables.Synthesis. Understanding the biodiversity‐productivity relationships is a focal topic in ecology, but unravelling their reciprocal nature remains challenging. We demonstrate that higher‐resolution longitudinal data along with methods to control for a broader suite of confounding variables can be used to resolve reciprocal relationships. We highlight future data needs and methods that can help us to resolve biodiversity‐productivity relationships, crucial for reconciling a long‐running debate in ecology and ultimately, to understand how biodiversity and ecosystem functioning respond to global change.more » « less
-
ABSTRACT Freshwater mussels (Bivalvia: Unionida) are among the most imperilled freshwater taxa. Yet, there is a lack of basic life history information for mussels, including data on their growth and longevity. These data help inform conservation efforts, as they can indicate whether species or populations may be vulnerable to decline and inform which species may be best adapted to certain habitats. We aimed to quantify growth and longevity in five mussel species from four river systems in the southeastern United States and test whether growth was related to stream flow. We also interpreted our findings in the context of life history theory.To model mussel growth and longevity, we cut radial thick sections from the shells of mussels and used high‐resolution photography to image the shells. We identified annual growth rings (annuli) and used von Bertalanffy growth models to estimate growth rate (K) and maximum age (Amax) across 13 mussel populations. We then used biochronological methods to remove age‐related variation in annual growth in each shell. We tested whether annual growth was correlated with stream flow using discharge‐based statistics.We found substantial variation inKandAmaxamong species and among populations of the same species.Kwas negatively related toAmax. We did not find consistent correlations between annual growth and stream flow.Our estimates ofKandAmaxalign with previous studies on closely related species and populations. They also match the eco‐evolutionary prediction that growth rate and longevity are negatively related. Life history theory predicts that short‐lived species with higher growth rates should be better adapted to environments with cyclical disturbance regimes, whereas longer‐lived species with low growth rates should be better adapted to stable environments. The lack of correlation between annual growth and stream flow suggests that mussel growth may be limited by other factors in our study system.While some species seem to have relatively narrow ranges for growth and longevity, other species show wide variation among populations. This highlights the need for species‐ and population‐specific conservation efforts. Fundamental life history information can be integrated with other species traits to predict how freshwater taxa may respond to ecological threats.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/2041-210X.13971
