Global biodiversity is facing a crisis, which must be solved through effective policies and on‐the‐ground conservation. But governments, NGOs, and scientists need reliable indicators to guide ...research, conservation actions, and policy decisions. Developing reliable indicators is challenging because the data underlying those tools is incomplete and biased. For example, the Living Planet Index tracks the changing status of global vertebrate biodiversity, but taxonomic, geographic and temporal gaps and biases are present in the aggregated data used to calculate trends. However, without a basis for real‐world comparison, there is no way to directly assess an indicator's accuracy or reliability. Instead, a modelling approach can be used. We developed a model of trend reliability, using simulated datasets as stand‐ins for the “real world”, degraded samples as stand‐ins for indicator datasets (e.g., the Living Planet Database), and a distance measure to quantify reliability by comparing partially sampled to fully sampled trends. The model revealed that the proportion of species represented in the database is not always indicative of trend reliability. Important factors are the number and length of time series, as well as their mean growth rates and variance in their growth rates, both within and between time series. We found that many trends in the Living Planet Index need more data to be considered reliable, particularly trends across the global south. In general, bird trends are the most reliable, while reptile and amphibian trends are most in need of additional data. We simulated three different solutions for reducing data deficiency, and found that collating existing data (where available) is the most efficient way to improve trend reliability, whereas revisiting previously studied populations is a quick and efficient way to improve trend reliability until new long‐term studies can be completed and made available.
Global biodiversity indicators provide vital information about the changing state of global biodiversity to guide research, conservation actions, and policy decisions. However, little is known about how gaps and biases in the underlying data affect their accuracy or reliability. We developed a model of trend accuracy based on properties of biodiversity time series data and used it to quantitatively assess the reliability of trends in the Living Planet Index. Our results suggest that many trends need more data to be considered reliable, particularly reptile and amphibian trends, and trends across the global south.
Understanding how multiple co‐occurring environmental stressors combine to affect biodiversity and ecosystem services is an on‐going grand challenge for ecology. Currently, progress has been made ...through accumulating large numbers of smaller‐scale empirical studies that are then investigated by meta‐analyses to detect general patterns. There is particular interest in detecting, understanding and predicting ‘ecological surprises’ where stressors interact in a non‐additive (e.g. antagonistic or synergistic) manner, but so far few general results have emerged. However, the ability of the statistical tools to recover non‐additive interactions in the face of data uncertainty is unstudied, so crucially, we do not know how well the empirical results reflect the true stressor interactions. Here, we investigate the performance of the commonly implemented additive null model. A meta‐analysis of a large (545 interactions) empirical dataset for the effects of pairs of stressors on freshwater communities reveals additive interactions dominate individual studies, whereas pooling the data leads to an antagonistic summary interaction class. However, analyses of simulated data from food chain models, where the underlying interactions are known, suggest both sets of results may be due to observation error within the data. Specifically, we show that the additive null model is highly sensitive to observation error, with non‐additive interactions being reliably detected at only unrealistically low levels of data uncertainty. Similarly, plausible levels of observation error lead to meta‐analyses reporting antagonistic summary interaction classifications even when synergies co‐dominate. Therefore, while our empirical results broadly agree with those of previous freshwater meta‐analyses, we conclude these patterns may be driven by statistical sampling rather than any ecological mechanisms. Further investigation of candidate null models used to define stressor‐pair interactions is essential, and once any artefacts are accounted for, the so‐called ‘ecological surprises’ may be more frequent than was previously assumed.
Ecosystems often face multiple stressors that act simultaneously; hence, understanding how stressors interact is an important goal. We perform a meta‐analysis of 545 freshwater stressor‐pair interactions and find that most individual experiment results cannot be distinguished from additive (i.e., the sum of the effects of both stressors acting individually), whilst the summary effect computed across all interactions is antagonistic (i.e., less than additive). However, computer models show these results are expected under plausible levels of measurement error, even if non‐additive interactions dominate the data, questioning our ability to reliably detect the true ways in which ecosystem stressors interact.
The joint analysis of species’ evolutionary relatedness and their morphological evolution has offered much promise in understanding the processes that underpin the generation of biological diversity.
...Disparity through time (DTT) is a popular method that estimates the relative trait disparity within and between subclades, and compares this to the null hypothesis that trait values follow Brownian evolution along the time‐calibrated phylogenetic tree. To visualise the differences a confidence envelope is normally created by calculating, at every time point, the 97.5% minimum and 97.5% maximum disparity values from multiple simulations of the null model. The null hypothesis is rejected whenever the empirical DTT curve falls outside of this envelope, and these time periods may then be linked to events that may have sparked non‐random trait evolution.
Here, simulated data are used to show this pointwise (ranking at each time point) method of envelope construction suffers from multiple testing and a poor, uncontrolled, false‐positive rate. As a consequence it cannot be recommended. Instead, each DTT curve can be given a single rank based upon their most extreme disparity value, relative to all other curves, and across all time points. Ordering curves this way leads to a test that avoids multiple testing, but still allows construction of a confidence envelope. The null hypothesis is rejected if the empirical DTT curve is ranked within the most extreme 5% ranked curves from the null model. Comparison of the rank envelope curve to the Morphological Disparity Index and Node Height tests shows it to have generally higher power to detect non‐Brownian trait evolution. An extension to allow simultaneous testing over multiple traits is also detailed.
Overall the results suggest the new rank envelope test should be used in null model testing for DTT analyses. The rank envelope method can easily be adapted into recently developed posterior predictive simulation methods used in model selection analyses. More generally, the rank envelope test should be adopted whenever a null model produces a vector of correlated values and the user wants to determine where the empirical data are different to the null model.
Priority effects can play a fundamental role in the assembly of ecological communities, but how they shape the dynamics of biodiversity over macroevolutionary timescales remains unclear. Here we ...develop and analyse a metacommunity model combining local priority effects with niche evolution, speciation and extinction. We show that by promoting the persistence of rare species, local priority effects cause the evolution of higher metacommunity diversity as well as major disparities in richness among evolutionary lineages. However, we also show how classic macroevolutionary patterns of niche incumbency—whereby rates of regional diversification and invasion slow down as ecological niches are filled—do not depend on local priority effects, arising even when invading species continuously displace residents. Together, these results clarify the connection between local priority effects and the filling of ecological niche space, and reveal how the impact of species arrival order on competition fundamentally shapes the generation and maintenance of biodiversity.
Priority effects can play a fundamental role in community assembly, but how they shape the dynamics of biodiversity over macroevolutionary timescales remains unclear. Here we develop and analyse a metacommunity model combining local priority effects and species diversification. Our results show that priority effects lead to the evolution of high, unevenly distributed species diversity, but are distinct from the commonly associated macroevolutionary concept of ‘niche incumbency’, helping to clarify the impact of species arrival order on the generation and maintenance of biodiversity.
1. It is widely held that if competition is important, adult or mature plants should be less-clustered than those in smaller size-classes. Self-thinning, the competition for limited resources, should ...remove individuals that are nearby neighbours, restricting recruitment of adults around other adult trees and resulting in a decrease in aggregation with size-class. 2. There is good empirical evidence for a reduction in clustering with increase in size-class, and the pattern is strongest for richly abundant species. However, few dynamical models have been developed to explore the relationship between the emergent spatial pattern of adults and juvenile plants and the underlying processes of neighbourhood competition and dispersal. In particular is it correct to assume that increased clustering with size-classes is inconsistent with self-thinning? 3. The hypothesis that self-thinning leads to less-clustered adult trees is assessed by analysing a deterministic approximation to a spatially explicit individual-based model that incorporates two size-classes, local dispersal, and neighbourhood competition that increases death rates and retards the growth of the smaller trees. 4. Analyses show that if recruitment of adults is limited by slow growth rates, low fecundity, or high juvenile-juvenile competition, then adults may well be more clustered than juveniles. This means an increase in aggregation with size-class is consistent with self-thinning processes. 5. The models support previous empirical results in that the numerically dominant species are more likely to show a reduction in clustering with size-class than species with a lower abundance. The models suggest rare species should show more adult clustering and less of a reduction in aggregation with size-class due to a numerical decrease in conspecific interactions. 6. Synthesis. The results show clearly that the Janzen-Connell hypothesis and self-thinning in general are consistent with adults being more clustered than juveniles, even when competitive interactions are quite strong. The pattern of relative clustering in juveniles and adults may therefore be indicative of adult recruitment rates, with weak decreases in aggregation with size-class caused by low fecundity, poor dispersal or slow growth, and strong decreases generated by high fecundity, good dispersal and/or fast growth rates.
Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as ...classification (e.g., assigning species to individual bird calls); clustering (e.g., clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g., accuracy of model predictions to original time series data); and anomaly detection (e.g., detecting possible catastrophic events from population time series). These common tasks in ecological research all rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored. Here, we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well suited to comparing noisy time series and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series and help us answer new ecological questions.
Abstract
Aim
The exceptional turnover in biota with elevation and number of species coexisting at any elevation makes tropical mountains hotspots of biodiversity. However, understanding the ...historical processes through which species arising in geographical isolation (i.e. allopatry) assemble along the same mountain slope (i.e. sympatry) remains a major challenge. Multiple models have been proposed including (1) the sorting of already elevationally divergent species, (2) the displacement of elevation upon secondary contact, potentially followed by convergence, or (3) elevational conservatism, in which ancestral elevational ranges are retained. However, the relative contribution of these processes to generating patterns of elevational overlap and turnover is unknown.
Location
Tropical mountains of Central‐ and South‐America.
Time Period
The last 12 myr.
Major Taxa Studied
Birds.
Methods
We collate a dataset of 165 avian sister pairs containing estimates of phylogenetic age, geographical and regional elevational range overlap. We develop a framework based on continuous‐time Markov models to infer the relative frequency of different historical pathways in explaining present‐day overlap and turnover of sympatric species along elevational gradients.
Results
We show that turnover of closely related bird species across elevation can predominantly be explained by displacement of elevation ranges upon contact (81%) rather than elevational divergence in allopatry (19%). In contrast, overlap along elevation gradients is primarily (88%) explained by conservatism of elevational ranges rather than displacement followed by elevational expansion (12%).
Main Conclusions
Bird communities across elevation gradients are assembled through a mix of processes, including the sorting, displacement and conservatism of species elevation ranges. The dominant role of conservatism in explaining co‐occurrence of species on mountain slopes rejects more complex scenarios requiring displacement followed by expansion. The ability of closely related species to coexist without elevational divergence provides a direct and faster pathway to sympatry and helps explain the exceptional species richness of tropical mountains.
Classical theory states that if conspecifics have a greater competitive effect on individuals than heterospecifics then coexistence should occur, and ecologists have spent much effort exploring ways ...to generate coexistence when this condition is not met. One process that has received particular attention in the last two decades is the effect of within-species aggregation and between-species segregation caused by limited dispersal. A number of theories have emerged as to how this common spatial pattern may help maintain biodiversity, and the general conclusion that has emerged is that spatial structure should almost always help competitors to coexist. But does spatial structure really always aid biodiversity? An individual-based model based on a spatial extension to the Lotka-Volterra competition equations and its mathematical approximation are presented to determine how local spatial structure may affect communities in which there is strong niche differentiation. Two main results emerge from analyses of the models. First, intraspecific competition being greater than interspecific competition coexistence may no longer be sufficient to generate coexistence when spatial structure is strong; and the species with the highest intraspecific competition coefficient is likely to be excluded. Second, dominance hierarchies may be reversed so that a competitor may become the subordinate species when dispersal and competitive interactions occur over short spatial scales. Both results emerge because, even though a species may be globally rare, intense clumping means most interactions occur between conspecifics, and if this is very intense it may be sufficient to stop a species from invading. However, long-range dispersal may ameliorate these effects by reducing the frequency of conspecific interactions, and this is especially important when a species is rare since it is very likely to land in an area dominated by heterospecifics. These results are most relevant to sessile organisms that produce relatively few viable offspring that survive to adulthood and that have relatively weak dispersal. The conclusion is that within-species aggregation may hinder coexistence when the toughest competitor an individual is likely to face is a member of its own species.
Because it lays the template from which communities develop, the pattern of
dispersed seed is commonly believed to influence community structure. To test
the validity of this notion, we evaluated ...theoretical and empirical work
linking dispersal kernels to the relative abundance, distribution, dispersion,
and coexistence of species. We found considerable theoretical evidence that
seed dispersal affects species coexistence by slowing down exclusion through
local dispersal and a competition-dispersal trade-off, yet empirical support
was scant. Instead, most empirical investigations examined how dispersal
affects species distribution and dispersion, subjects with little theory. This
work also relied heavily on dispersal proxies and correlational analyses of
community patterns, methods unable to exclude alternative hypotheses. Owing to
the overall dichotomy between theory and empirical results, we argue that the
importance of dispersal cannot be taken for granted. We conclude by advocating
experiments that manipulate the seed dispersal pattern, and models that
incorporate empirically documented dispersal kernels.
Spatial point patterns are a commonly recorded form of data in ecology, medicine, astronomy, criminology, epidemiology and many other application fields. One way to understand their second order ...dependence structure is via their spectral density function. However, unlike time series analysis, for point patterns such approaches are currently underutilized. In part, this is because the interpretation of the spectral representation of the underlying point processes is challenging. In this paper, we demonstrate how to band-pass filter point patterns, thus enabling us to explore the spectral representation of point patterns in space by isolating the signal corresponding to certain sets of wavenumbers.
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