Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various disciplines. However, many biological and medical analyses use relatively low sample size (N), ...contributing to concerns on reproducibility. What is the minimum N to identify the most plausible data pattern using regressions? Statistical power analysis is often used to answer that question, but it has its own problems and logically should follow model selection to first identify the most plausible model. Here we make null, simple linear and quadratic data with different variances and effect sizes. We then sample and use information theoretic model selection to evaluate minimum N for regression models. We also evaluate the use of coefficient of determination (R2) for this purpose; it is widely used but not recommended. With very low variance, both false positives and false negatives occurred at N < 8, but data shape was always clearly identified at N ≥ 8. With high variance, accurate inference was stable at N ≥ 25. Those outcomes were consistent at different effect sizes. Akaike Information Criterion weights (AICc wi) were essential to clearly identify patterns (e.g., simple linear vs. null); R2 or adjusted R2 values were not useful. We conclude that a minimum N = 8 is informative given very little variance, but minimum N ≥ 25 is required for more variance. Alternative models are better compared using information theory indices such as AIC but not R2 or adjusted R2. Insufficient N and R2-based model selection apparently contribute to confusion and low reproducibility in various disciplines. To avoid those problems, we recommend that research based on regressions or meta-regressions use N ≥ 25.
Ecology Letters (2011) 14: 1–8
Matrix projection models are among the most widely used tools in plant ecology. However, the way in which plant ecologists use and interpret these models differs from ...the way in which they are presented in the broader academic literature. In contrast to calls from earlier reviews, most studies of plant populations are based on < 5 matrices and present simple metrics such as deterministic population growth rates. However, plant ecologists also cautioned against literal interpretation of model predictions. Although academic studies have emphasized testing quantitative model predictions, such forecasts are not the way in which plant ecologists find matrix models to be most useful. Improving forecasting ability would necessitate increased model complexity and longer studies. Therefore, in addition to longer term studies with better links to environmental drivers, priorities for research include critically evaluating relative/comparative uses of matrix models and asking how we can use many short‐term studies to understand long‐term population dynamics.
Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 ...continents explained and predicted plant population dynamics. We parameterized stage‐based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts’ 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data‐collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk‐averse decisions than to expect precise forecasts from models. Habilidad de los Modelos Matriciales para Explicar el Pasado y Predecir el Futuro de las Poblaciones de Plantas
Abstract Despite enthusiasm for big data in the life sciences, challenges arise because of biases and incomplete data. Demographic studies often overlook dormant life stages, which can skew ...inferences. They also tend to focus on few populations and short time spans. We assessed omissions of seed banks in demographic studies, exploring trends across life forms, climates, and taxonomic groups. We compared 172 species (192 cases) with independent seed bank and demographic studies. Approximately 25% of the demographic studies excluded known seed bank stages. The probability of omissions was lower for annuals and shrubs and higher for perennial herbs. We found no evidence that ecoregion or phylogeny explained these omissions. Modeling choices and study designs may explain patterns of seed bank omissions. Considering more populations reduced the chance of omissions. Omissions raise concerns for ecological analyses using databases. Leveraging large data is important, but we must be careful to understand their biases and limitations.
The spatial scale at which demographic performance (e.g., net reproductive output) varies can profoundly influence landscape-level population growth and persistence, and many demographically ...pertinent processes such as species interactions and resource acquisition vary at fine scales. We compared the magnitude of demographic variation associated with fine-scale heterogeneity (<10 m), with variation due to larger-scale (>1 ha) fluctuations associated with fire disturbance. We used a spatially explicit model within an IPM modeling framework to evaluate the demographic importance of fine-scale variation. We used a measure of expected lifetime fruit production, EF
, that is assumed to be proportional to lifetime fitness. Demographic differences and their effects on EF
were assessed in a population of the herbaceous perennial Hypericum cumulicola (~2,600 individuals), within a patch of Florida rosemary scrub (400 × 80 m). We compared demographic variation over fine spatial scales to demographic variation between years across 6 yr after a fire. Values of EF
changed by orders of magnitude over <10 m. This variation in fitness over fine spatial scales (<10 m) is commensurate to postfire changes in fitness for this fire-adapted perennial. A life table response experiment indicated that fine-scale spatial variation in vital rates, especially survival, explains as much change in EF
as demographic changes caused by time-since-fire, a key driver in this system. Our findings show that environmental changes over a few tens of meters can have ecologically meaningful implications for population growth and extinction.
Aims
The stress gradient hypothesis predicts that competition will be important in productive environments while facilitation will be common in environments with high stress or consumer pressure. ...However, abiotic stress and grazing may vary independently and even occur simultaneously. Here we examine the outcome of plant interactions in grazed wetlands where consumer pressure and abiotic stress occur concurrently. We hypothesized that cattle grazing and microhabitat would alter the outcome of plant interactions. Given that wetland edges are drier and less productive than wetland centers we expected that facilitation would be greatest in drier wetland edges due to greater abiotic stress regardless of cattle presence.
Location
Archbold Biological Station's Buck Island Ranch (BIR), south‐central Florida, USA (27°09′ N, 81°11′ W).
Methods
We conducted an experiment for two growing seasons in ten wetlands, five exposed to cattle grazing and five fenced. Two wetland obligate plants were included (Panicum hemitomon and Alternanthera philoxeroides), and plots were assigned to three treatments (a) all neighbors removed; (b) all neighbors removed except Juncus effusus, a dominant, unpalatable plant; and (c) all neighbors intact (control), in both wetland centers and edges. Differences in survival, change in height and number of leaves were assessed.
Results
In ungrazed wetlands, plant survival was higher in wetland edges vs centers, while it did not differ between microhabitats in grazed wetlands. Survival in wetland edges was further increased by the presence of Juncus effusus. Positive interactions under grazed conditions were clear when plant height was assessed, but negative interactions affected leaf production in both ungrazed and grazed wetlands.
Conclusions
Grazing interacts with wetland microhabitat to alter plant survival. Facilitative interactions on plant height were apparent in grazed wetlands. Understanding how plant interactions change under different biotic and abiotic contexts is important for informing ecosystem restoration and management.
We examined plant interactions in grazed wetlands where consumer pressure and abiotic stress occur concurrently. Grazing and microhabitat interacted to impact plant survival. Competition decreased survival in ungrazed wet microhabitats while plant survival was similar in dry and wet microhabitats in grazed wetlands. Assessing multiple effects on plant interactions is important for understanding plant community change and management.
The frequency of ecological disturbances, such as fires, is changing due to changing land use and climatic conditions. Disturbance‐adapted species may thus require the manipulation of disturbance ...regimes to persist.
However, the effects of changes in other abiotic factors, such as climatic conditions, are frequently disregarded in studies of such systems. Where climatic effects are included, relatively simple approaches that disregard seasonal variation in the effects are typically used.
We compare predictions of population persistence using different fire return intervals (FRIs) under recent and predicted future climatic conditions for the rare fire‐dependent herb Eryngium cuneifolium. We used functional linear models (FLMs) to estimate the cumulative effect of climatic variables across the annual cycle, allowing the strength and direction of the climatic impacts to differ over the year. We then estimated extinction probabilities and minimum population sizes under past and forecasted future climatic conditions and a range of FRIs.
Under forecasted climate change, E. cuneifolium is predicted to persist under a much broader range of FRIs, because increasing temperatures are associated with faster individual growth. Climatic impacts on fecundity do not result in a temporal trend in this vital rate due to antagonistic seasonal effects operating through winter and summer temperatures. These antagonistic seasonal climatic effects highlight the importance of capturing the seasonal dependence of climatic effects when forecasting their future fate.
Synthesis. Awareness of the potential effects of climate change on disturbance‐adapted species is necessary for developing suitable management strategies for future environmental conditions. However, our results suggest that widely used simple methods for modelling climate impacts, that disregard seasonality in such effects, may produce misleading inferences.
Awareness of the potential effects of climate change on disturbance‐adapted species is necessary for developing suitable management strategies for future environmental conditions. However, our results suggest that widely used simple methods for modelling climate impacts, that disregard seasonality in such effects, may produce misleading inferences. Thus, the widespread use of statistical tools which negate the need to select a single time period are necessary to fully understand the impacts of climate change on optimal management strategies.
Diversity of ageing across the tree of life Jones, Owen R; Scheuerlein, Alexander; Salguero-Gómez, Roberto ...
Nature (London),
01/2014, Letnik:
505, Številka:
7482
Journal Article
Recenzirano
Odprti dostop
Evolution drives, and is driven by, demography. A genotype moulds its phenotype's age patterns of mortality and fertility in an environment; these two patterns in turn determine the genotype's ...fitness in that environment. Hence, to understand the evolution of ageing, age patterns of mortality and reproduction need to be compared for species across the tree of life. However, few studies have done so and only for a limited range of taxa. Here we contrast standardized patterns over age for 11 mammals, 12 other vertebrates, 10 invertebrates, 12 vascular plants and a green alga. Although it has been predicted that evolution should inevitably lead to increasing mortality and declining fertility with age after maturity, there is great variation among these species, including increasing, constant, decreasing, humped and bowed trajectories for both long- and short-lived species. This diversity challenges theoreticians to develop broader perspectives on the evolution of ageing and empiricists to study the demography of more species.
1. Quantifying interactive effects of environmental drivers on population dynamics can be critical for a robust analysis of population viability. Fire regimes, among the most widespread disturbances ...driving population dynamics, are increasingly modified by and interact with human activities. However, viability of fire-adapted species is typically assessed overlooking disturbance interactions, potentially resulting in suboptimal management actions. 2. We investigated whether increasing human disturbances in fire-prone ecosystems may pose a threat or an opportunity to improve population viability, using demographic data of the carnivorous, post-fire recruiting plant Drosophyllum lusitonicum, endemic to heathlands in the southwestern Mediterranean Basin. We built integral projection models and simulated population dynamics under different combinations of two key disturbance types affecting populations: fire and livestock browsing and trampling. We used perturbation analyses to determine potential long-term consequences of maintaining fundamentally different disturbance types. 3. Despite most populations inhabiting browsed habitats, simulations showed a greater extinction risk in populations under high livestock pressure compared with ones under low or moderate pressures. Extinction risk decreased when fire return intervals shortened in populations under low or moderate livestock pressure; however, the opposite pattern emerged in heavily browsed populations, where short intervals between fires increased extinction. 4. Elasticity analyses showed that decreases in viability under frequent disturbance interactions (heavy browsing and frequent fire) may be explained by selection against seed dormancy in populations with frequent browsing and trampling. This may potentially cause populations to collapse when fires kill above-ground plants without populations being able to recover from a seed bank. 5. Synthesis and applications. Incorporating disturbance interactions can result in a different assessment of viability of a fire-adapted species than considering fire regimes alone. In Mediterranean ecosystems, fire management may be more effective when integrating moderate human activities. However, replacing fires by human disturbances, a currently widespread strategy in many fire-prone ecosystems, is not recommended since it may fundamentally alter population dynamics and selection pressures and decrease viability of fire-adapted species.
Dormant life stages are often critical for population viability in stochastic environments, but accurate field data characterizing them are difficult to collect. Such limitations may translate into ...uncertainties in demographic parameters describing these stages, which then may propagate errors in the examination of population‐level responses to environmental variation. Expanding on current methods, we 1) apply data‐driven approaches to estimate parameter uncertainty in vital rates of dormant life stages and 2) test whether such estimates provide more robust inferences about population dynamics. We built integral projection models (IPMs) for a fire‐adapted, carnivorous plant species using a Bayesian framework to estimate uncertainty in parameters of three vital rates of dormant seeds – seed‐bank ingression, stasis and egression. We used stochastic population projections and elasticity analyses to quantify the relative sensitivity of the stochastic population growth rate (log λs) to changes in these vital rates at different fire return intervals. We then ran stochastic projections of log λs for 1000 posterior samples of the three seed‐bank vital rates and assessed how strongly their parameter uncertainty propagated into uncertainty in estimates of log λs and the probability of quasi‐extinction, Pq(t). Elasticity analyses indicated that changes in seed‐bank stasis and egression had large effects on log λs across fire return intervals. In turn, uncertainty in the estimates of these two vital rates explained > 50% of the variation in log λs estimates at several fire‐return intervals. Inferences about population viability became less certain as the time between fires widened, with estimates of Pq(t) potentially > 20% higher when considering parameter uncertainty. Our results suggest that, for species with dormant stages, where data is often limited, failing to account for parameter uncertainty in population models may result in incorrect interpretations of population viability.
PDF
