Research in the lab has been diverse, focusing on populations, communities, and landscapes; using laboratory and field experimental and observational data; and spanning local to global scales. The unifying aspects are a common interest in the ecological consequences of human activities, and a quantitative approach. Example projects/themes are below.
We are currently focused on studies of community and metapopulation synchrony, the first theme below.
Synchrony in communities and metapopulations
Figure 1: The wavelet transform of the North Atlantic Oscillation (NAO) shows amount of oscillation in the NAO as a function of time and timescale (a, b). It is similar to plots of the synchrony of leaf-curling plum aphid populations across the UK (c, d), which show extent of synchrony as a function of time and timescale, normalized from 0 to 1. It turns out this reflects a causal synchronizing influence, as revealed by the statistics we have developed. Figure taken from working which led to a paper of Sheppard, Bell, Harrington and Reuman (Nature Climate Change, 2016).
Populations of the same species in locations hundreds of kilometers apart often fluctuate partly in unison, a phenomenon called spatial synchrony (or metapopulation synchrony, or population synchrony). For instance, British aphid species, of economic importance because they are major agricultural pests, outbreak 80% in synchrony over short distances and 50% in synchrony over 200km, a huge distance for most aphids. Metapopulation synchrony is widespread, and has been detected in birds, lemmings, fish such as cod, human pathogens such as measles, amphibians, and other species, many of major conservation, economic, or health importance. Metapopulation synchrony is important for several reasons, for instance: synchronized pest populations require a coordinated response; and in the seas, synchrony or lack thereof in plankton may affect foraging patterns of exploited fish such as cod because large fish need to forage over extensive areas. There is a large, multifaceted project in the lab to develop new statistical, computational and modelling methods based on wavelet transforms and other approaches for studying synchrony, and to apply them to aphid, plankton, fish, amphibian, forest-pest, ungulate, kelp, and many other data sets. Results are interpreted in light of parallel modelling projects. For instance, aphid data are from the Rothamsted Insect Survey, plankton data are from the Continuous Plankton Recorder dataset of the Sir Alister Hardy Foundation for Ocean Science (SAHFOS, now subsumed under the UK Marine Biological Association), fish data are from the International Bottom Trawl Survey (IBTS), kelp data are from off the coast of California, provided by Tom Bell and Kyle Cavanaugh, and deer data were from the Wisconsin Department of Natural Resources. We have also worked with several other datasets. These are world-class data sets, but we are also continuously considering new systems. Current, past and affiliated postdocs Jon Walter, Tom Anderson, Lei Zhao, Lawrence Sheppard, Brandon Mechtley, Shyamolina Ghosh, Scott Colborne, and Danny O’Donnell have contributed to this work. The work has investigated connections of synchrony to time and timescale dependence, geography, Taylor’s law, copula statistics and tail associations, insect behavior, fish migrations, population cycles, and other topics. More recently, we have also begun investigating community synchrony, a different (but related) ecological context. Peruse the papers listed below for a more complete picture.
Anderson et al, 2021, Synchronous effects produce cycles in deer populations and deer-vehicle collisions, Ecology Letters
Walter et al, 2020, Micro-scale geography of synchrony in a serpentine plant community, Journal of Ecology
Ghosh et al, 2020, A new approach to interspecific synchrony in population ecology using tail association, Ecology and Evolution
Ghosh et al, 2020, Tail associations in ecological variables and their impact on extinction risk, Ecosphere
Sheppard et al, 2020, Self-organizing cicada choruses respond to the local sound and light environment, Ecology and Evolution
Zhao et al, 2020, A new variance ratio metric to detect the timescale of compensatory dynamics, Ecosphere
Ghosh et al, 2020, Copulas and their potential for ecology, Advances in Ecological Research
Walter et al, 2019, Weather and regional crop composition variation drive spatial synchrony of lepidopteran agricultural pests, Ecological Entomology
Sheppard et al, 2019, Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas, Plos Computational Biology
Zhao et al, 2019, Proximate determinants of Taylor’s law slopes, Journal of Animal Ecology
Anderson et al, 2019, The dependence of synchrony on timescale and geography in freshwater plankton, Limnology and Oceanography
Desharnais et al, 2018, Temporal scale of environmental correlations affects ecological synchrony, Ecology Letters
Anderson et al, 2018, Using geography to infer the importance of dispersal for the synchrony of freshwater plankton, Oikos
Reuman et al, 2017, Synchrony affects Taylor’s law in theory and data, PNAS
Walter et al, 2017, The geography of spatial synchrony, Ecology Letters
Defriez et al, 2017, A global geography of synchrony for terrestrial vegetation, Global Ecology and Biogeography
Defriez et al, 2017, A global geography of synchrony for marine phytoplankton, Global Ecology and Biogeography
Sheppard et al, 2017, Rapid surrogate testing of wavelet coherences, European Physical Journal, Nonlinear and Biomedical Physics
Defriez et al, 2016, Climate-change-related regime shifts have altered spatial synchrony of plankton dynamics in the North Sea, Global Change Biology
Sheppard et al, 2016, Changes in large-scale climate alter spatial synchrony of aphid pests, Nature Climate Change
Some main external collaborators: Chris Reid, Richard Harrington, James Bell, Simon Jennings, Sanford Eigenbrode, Malin Pinsky, Joel Cohen, Erik Van Vleck, Tom Bell, Kyle Cavanaugh, Max Castorani, Lauren Hallett, Katharine Suding, Shaopeng Wang, Lauren Shoemaker, Kathy Cottingham, Andrew Rypel, Robert Desharnais, Robert Costantino.
Funding: James S McDonnell Foundation, UK NERC, NSF (Math Bio program, Biological Oceanography program), KU
Whole community effects of warming
Figure 3: Two nearby sampled streams of very different temperatures. One stream was used to warm the other using a heat exchanger. Photo credit: Jón S. Ólafsson.
Warming has very well documented and comparatively well understood effects on populations and single species (latitudinal and altitudinal range shifts and phenological shifts, body size reductions over time), but community level effects of warming are still poorly understood. Nevertheless, whole-community effects are likely to have important consequences for biodiversity and ecosystem functioning. Members of the lab form the quantitative end of an international collaborative network studying community-level warming effects in two unique model systems. The first is a system of replicated pond mescosms, half of them warmed on a long-term basis by ~4 degrees C from ambient (Fig. 1). The systems are intensively sampled for their plankton populations as well as for carbon and nutrient exchange and cycling. The second is a unique system of geothermal streams in the Hengill region of Iceland (the banner photo of this page is a view of the system from above). The streams range in temperature from 5 to 25 degrees C, but are all within 1km of each other (Fig. 2), and are connected to the same main stem, so the dispersal constraints and covariates that often confound biogeographic studies of the effects of temperature are not a problem. The system also includes a temperature manupulation of one of the streams (Fig. 3), so that experimental, short-term warming can be investigated as well as making observations about long-terms effects of warming.
O’Gorman et al, 2017, Nature Climate Change
Yvon-Durocher et al, 2015, Plos Biology
Reuman et al, 2014, Journal of Animal Ecology
Adams et al, 2013, Global Change Biology
Hudson et al, 2013, Methods in Ecology and Evolution
O’Gorman et al, 2012, Advances in Ecological Research, 47, 81.
Woodward et al, 2010, Advances in Ecological Research 42, 71.
Main external collaborators: Mark Trimmer, Gab Yvon-Durocher, also the researchers listed on the Hengill website.
Funding: UK NERC; NSF; European funders
History of habitat fragmentation
Figure 2: A stylized, one-dimensional example of a terrageny (a). Green is habitat, white is destroyed habitat. “Terrageneitc distance”, which shows how many fragmentation events separate patches, is in (b). Figure taken from Ewers et al, 2014, Ecol Lett.
Figure 1: Maps of estimated future extinction and extinction debt across the Amazon according to three possible legislation scenarios. Figure taken from Wearn et al, 2012, Science.
Habitat size and degree of habitat fragmentation have long been known to affect remaining biodiversity and its distribution across a landscape. For instance, both the amount and degree of contiguity of undisturbed rainforest in a part of the Amazon can determine the number and type of species that live in the region. It is increasingly recognized, however, that not only the current configuration of the habitat in a region affects its biodiversity, but also the historical trajectory by which the habitat arrived at its current state. The habitat in a landscape may have been degraded and fragmented slowly or quickly, recently or a long time ago, with substantially different implications for the current and future biodiversity it supports. Members of the lab have made contributions to two large efforts to understand and quantify two distinct but related aspects of this historical influence. The first effort Wearn et al, 2012, Science) considered extinction debt, the phenomenon whereby extinction events continue to occur gradually over time following an episode of partial habitat destruction, sometimes continuing decades after the destruction itself. So reduced habitats harbor species which are committed to extinction. We estimated local extinctions and extinction debts of birds, mammals, and amphibians across the Amazon (e.g., Fig. 1, see also the movies in the supplementary materials of (Wearn et al, 2012, Science). The second effort considered the history of habitat fragmentation in an area using a data device similar to a phylogeny, which we named a “terrageny”. Splits in a phylogeny represent evolutionary divergences, whereas splits in a terrageny represent fragmentation events (Fig. 2). We developed theory on how terrageneitc structure should affect communities within fragments and distributions of biodiversity across the landscape (Ewers et al, 2014, Ecol Lett). Both projects and ongoing work are collaborations with the lab of Rob Ewers at Imperial College London.
Funding: UK NERC
Applications and implications of body-size scaling in communities
Many biological rates (e.g., metabolic rates, population growth rates, consumption rates) are strongly correlated with body size, and members of the lab (and many others) have used or studied these relationships for many years for a variety of purposes in community ecology. Indeed, the “whole community effects of warming” research topic described above is also an outgrowth of this general allometric approach. A feature that distinguishes work in this vein in the Reuman lab is the focus on complete local communities. Early work included characterizations of population density-versus-body size scaling in complete local communities (e.g., Reuman et al, 2004, J Anim Ecol; Cohen et al, 2009, PNAS), and variation in these relationships among communities (e.g., Reuman et al, 2009, Adv Ecol Res). An energetic-balance explanation for observed patterns was provided and confronted with data from many ecosystems in Reuman et al, 2008, Ecol Lett. We have studied the body-size scaling of metabolic rates directly (Hudson et al, 2013, J Anim Ecol). Recently we have used size-scaling of rates and a mechanistic model to propose a competition-based mechanism for widely observed shifts toward smaller body sizes in ectotherms: at least for phytoplankton and phytobenthos, warming accentuates competitive advantages that smaller cells have in nutrient uptake and growth (Reuman et al, 2014, J Anim Ecol). In a recent modelling effort (Hudson et al, 2013, Proc Roy Soc B), we demonstrated that allometric scaling of biological rates is not only sufficient but also necessary to make dynamical models of complex communities realistic in important respects. This theme informs the previous theme – see above. We have also studied the biogeography of the size-scaling of diversity in marine systems – see below.
Further information: See papers mentioned in the text.
Funding: UK NERC, NSF
Video: Demonstration of model-data agreement as per Hudson et al, 2013, Proc Roy Soc B. A dynamical model settles down to represent community patterns of population density versus body mass quite accurately in the left panel, but not in the right panel. It is in this sense that body-mass allometries of biological rates such as growth rates, metabolic rates, and consumption rates are necessary and sufficient for accurate modelling of community dynamics. Data are from Tuesday Lake, a well-studied system. Green circles are phytoplankton species, blue squares are zooplankton species, and purple diamonds are fish. The smaller dots are model predictions, so good agreement means the vertical stalks connecting model and data are small. The music is just for giggles. Video credit: Lawrence Hudson.
Biogeography of marine communities
Most species are small. The nature of this bias and its causes and ramifications have been studied in an evolutionary context for decades. But we carried out a major study which was one of the first to focus instead on the community level, asking how diversity-versus-body mass relationships can be explained for geographically delineated but taxon-inclusive assemblages, i.e. all the species in a region? Specifically, we asked how diversity is distributed across an axis of body size in the world’s continental shelf seas. We defined and used the “diversity spectrum” to describe how diversity varies with body size – this is a species analogue of the classic abundance/size spectrum of individuals. We produced a state-of-the-art unified theoretical and empirical synthesis (Reuman et al, 2014, J Anim Ecol; see also this commentary). Several theoretical predictions were borne out by our global empirical analysis. For instance, fewer large species per small species were predicted (and were found) in colder and larger regions.
Figure 1: Diversity spectrum slopes in the world’s Large Marine Ecosystems (LMEs). More negative slopes mean fewer large species per small species. For instance, slope -0.5 means a 10-fold reduction in diversity for each 100-fold increase in mass; slope -0.1 means only a 1.6-fold reduction for the same increase in mass. LMEs for which insufficient data were available to perform fits or for which diversity spectra are nonlinear are cross hatched. Data were from the asymptotic mass range 1 to 1000kg. Figure taken from Reuman et al, 2014, J Anim Ecol.
Projects can be available, co-supervised by other researchers, involving field or laboratory components. A good example is the work of Lisa Signorile, PhD, a student who graduated from the lab. In collaboration with Chris Carbone and Jinliang Wang of the London Zoological Society, Peter Lurz of the University of Edinburgh, Sandro Bertolino of the University of Turin, and Dan, Lisa studied the population genetics of invasive American grey squirrels in Europe. She collected DNA from over 1500 squirrels from over 50 populations in England, Wales, Scotland, Northern Ireland, the Republic of Ireland, Italy, and the USA, and came to several conclusions about the genetic structure of the populations which should impact management of this and other invasions. One main result was linking expansion rate of new population nuclei to local genetic diversity. Managers should prioritize prevention of mergers of genetically distinct populations and translocations between such populations.
Signorile et al, 2016, Biological Conservation
Signorile et al, 2016, Diversity and Distributions
Signorile et al, 2014, Diversity and Distributions
Signorile et al, 2014, Biological Invasions
Main external collaborators: Chris Carbone, Jinliang Wang, Peter Lurz, Sandro Bertolino
Funding: UK NERC