Biology subjects

Murrell, D. J.

Publications and source records attributed to Murrell, D. J..

6 recordsLinked to original sources

bioRxiv2024

Colonisation lags predict sympatric diversity in birds

Speciation is the ultimate source of biodiversity. However, because most species arise in spatial isolation (allopatry), how speciation shapes patterns of co-occurring (sympatry) species richness remains unclear. Here we examine how the history of past speciation events influences the maximum sympatric species richness attained across passerine bird clades (n = 40 families). Using a phylogenetic model, we infer that the rate at which species assemble in sympatry is extremely slow, and on average millions of years after speciation onset. As a result, having accounted for clade size, sympatric species richness varies substantially across families, being highest in small or ancient clades comprised of older species that have had more time to accumulate in sympatry. While our analysis does not test the ecological or evolutionary processes causing the slow build-up of sympatric assemblages, our results show that speciation history has left an indelible legacy on current species richness patterns.

evolutionary biology↗

bioRxiv2023

How much data do we need? Reliability and data deficiency in global vertebrate biodiversity trends

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. But without a basis for real-world comparison, there is no way to directly assess an indicators 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 sampled to unsampled 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, and that 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.

ecology↗

bioRxiv2022

Disentangling the historical routes to community assembly in the global epicentre of biodiversity

AimThe coexistence and turnover of species along elevation gradients makes tropical mountains hotspots of biodiversity. However, understanding the historical processes through which species arising in geographic 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. LocationTropical mountains of Central- and South-America. Time periodThe last 12 myr. Major taxa studiedBirds. MethodsWe 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. ResultsWe show that the 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 conclusionsBird communities across tropical 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 subsequent expansion. The ability of closely related species to coexist without elevational divergence provides a direct and thus faster pathway to sympatry and may help explain the exceptional species richness of tropical mountains.

evolutionary biology↗

bioRxiv2022

multiplestressR: An R package to analyse factorial multiple stressor data using the additive and multiplicative null models

Globally, ecosystems are being affected by multiple simultaneous stressors (also termed drivers, factors, or perturbations). While the effects of single stressors are becoming increasingly well understood, there remains substantial uncertainty regarding how multiple stressors may interact to affect ecosystems. Accordingly, there is substantial interest in documenting how stressors combine to impact individuals through to entire communities. Indeed, understanding how stressors interact represents one of the grand challenges currently facing ecologists and conservationists. Popular methods used to classify stressor interactions comprise multiple steps, including complex mathematical equations. Accordingly, there is the potential for errors to occur at multiple points, any of which can result in erroneous conclusions being drawn. Furthermore, there are frequently minor methodological differences between studies which may limit, or even prevent, direct comparisons of their results from being made. Here, we introduce the multiplestressR R package, a statistical tool which addresses the above issues. The package allows researchers to easily conduct a rigorous analysis of their multiple stressor data and provides results which are simple to interpret. The multiplestressR package can implement either the additive or multiplicative null model using iterations of these tools which are commonplace within multiple stressor ecology. The multiplestressR package can classify interactions as being synergistic, antagonistic, reversal, or null and requires minimal experience in either R or statistics to implement. Additionally, we provide example R code which can be easily modified to analysis any given factorial multiple stressor dataset. Indeed, widespread use of this software will allow for an easier and more robust comparison of results. Ultimately, we hope that the multiplestressR package will provide a stronger understanding of how stressors combine to affect individuals, populations, communities, and ecosystems.

ecology↗

bioRxiv2021

Multiple stressor null models frequently fail to detect most interactions due to low statistical power

As most ecosystems are being challenged by multiple, co-occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non-null stressor interaction responses, greatly hindering progress. Determination of adequate sample size requires (i) knowledge of the detection ability of the inference method being used, and (ii) a consideration of the smallest biologically meaningful deviation from the null expectation. However, (i) has not been investigated and (ii) is yet to be discussed. Using both real and simulated data we show sample sizes typical of many experiments (<10) can only detect very large deviations from the additive null model, implying many important non-null stressor-pair interactions are being missed. We also highlight how only reporting statistically significant results at low samples sizes greatly overestimates the degree of non-additive stressor interactions. Computer code that simulates data under either additive or multiplicative null models is provided to estimate statistical power for user defined responses and sample sizes and we recommend this is used to aid experimental design and interpretation of results. We suspect that most experiments may require 20 or more replicates per treatment to have adequate power to detect non-additive. However, researchers still need to define the smallest interaction of interest, i.e. the lower limit for a biologically important interaction, which is likely to be system specific, meaning a general guide is unavailable. Sample sizes could potentially be increased by focussing on individual-level responses to multiple stressors, or by forming coordinated networks of researchers to repeat experiments in larger-scale studies. Our main analyses relate to the additive null model but we show similar problems occur for the multiplicative null model, and we encourage similar investigations into the statistical power of other null models and inference methods. Without knowledge of the detection abilities of the statistical tools at hand, or definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions.

ecology↗

bioRxiv2017

A global envelope test to detect early and late bursts of trait evolution

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 at each time point, and compares this to the null hypothesis that trait values follow an uncorrelated random walk along the time calibrated phylogenetic tree. A simulation envelope is normally created by calculating, at every time point, the 95% minimum and 95% maximum disparity values from multiple simulations of the null model on the phylogenetic tree. 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. However, this method of envelope construction leads to multiple testing and a poor, uncontrolled, false positive rate. As a consequence it cannot be recommended. A recently developed method in spatial statistics is introduced that constructs a confidence envelope by giving each DTT curve a single ranking value based upon its most extreme disparity value. This method avoids the pitfalls of multiple testing whilst retaining a visual interpretation. Results using simulated data show this new test has desirable type 1 properties and is at least as powerful in correctly rejecting the null hypothesis as the morphological disparity index and node height test that lack a visual interpretation. Three example datasets are reanalyzed to show how the new test may lead to different inferences being drawn. Overall the results suggest the new rank envelope test should be used in null model testing for DTT analyses, and that there is no need to combine the envelope test with other tests such as has been done previously. Moreover, the rank envelope method can easily be adopted into recently developed posterior predictive simulation methods. More generally, the rank envelope test should be adopted when-ever a null model produces a vector of correlated values and the user wants to determine where the empirical data is different to the null model.

evolutionary biology↗