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Abstract How individuals use space and, thus, the rate and the nature of their interactions with others are shaped by their environment. Exogenous changes that alter aggregation patterns, such as resource pulses, can therefore have a significant impact on seemingly unrelated processes like disease spread. White-tailed deer (Odocoileus virginianus) aggregate in oak forests during mast events, and chronic wasting disease (CWD) transmission patterns vary with deer density, so we hypothesize a link between the masting cycle and CWD dynamics. We investigate various possible effects of masting on deer, including shifts to more frequency-dependent CWD transmission due to aggregation, as well as elevated fecundity and decreased mortality of deer in response to the resource pulse, using a simplified compartment model of CWD spread. When masting affects epidemiological parameters, including the strength of frequency dependence in CWD transmission, disease spread during masting events significantly reduces the size of deer populations but, paradoxically, without any change in the proportion of the population in the CWD-diseased state. In contrast, demographic parameters were found in principle to be capable of altering both population size and disease incidence, though the observed effects were very small. While our quantitative findings should be validated using more detailed models of CWD transmission before they are taken as specific predictions about this system, our fundamental qualitative result appears to be quite general. That is, our conclusion that epidemiological rates only influence population size, but demographic rates may affect both population size and disease incidence, can be derived not only from the model we studied but also from classical epidemiological models as well. Our work extends the understanding of the far-reaching impacts of resource pulses through ecological communities by highlighting the vastly different consequences of the same resource pulse acting in different ways. 

During community assembly, abiotic factors can influence species at multiple stages during their life history, for example by affecting early settlement or establishment probabilities and thus initial densities (route 1: abiotic effects on density), or later by affecting the strength of biotic interactions during subsequent life stages (route 2: abiotic effects on interaction strengths). Since real abiotic landscapes are multivariate and complex, how these two distinct routes of abiotic influence affect community patterns has not been quantified. Using an individual-based spatially explicit simulation model, we compared scenarios where abiotic conditions shaped initial densities, interaction strengths, or both, of plant species with unique abiotic niches. We then partitioned the effect of the abiotic landscape on community patterns into components arising from variable density, variable interaction strengths, and their interaction. Even when plants responded to identical landscapes, variable density and variable interaction strengths led to different community patterns, and their combined effects were non-additive. Variable density promoted more spatial structure, while variable interaction strengths promoted higher local species richness. We highlight important implications these findings have in applied plant community ecology. 

Despite considerable study of population cycles, the striking variability of cycle periods in many cyclic populations has received relatively little attention. Mathematical models of cyclic population dynamics have historically exhibited much greater regularity in cycle periods than many real populations, even when accounting for environmental stochasticity. We contend, however, that the recent focus on understanding the impact of long, transient but recurrent epochs within population oscillations points the way to a previously unrecognized means by which environmental stochasticity can create cycle period variation. Specifically, consumer–resource cycles that bring the populations near a saddle point (a combination of population sizes toward which the populations tend, before eventually transitioning to substantially different levels) may be subject to a slow passage effect that has been dubbed a ‘saddle crawlby'. In this study, we illustrate how stochasticity that generates variability in how close predator and prey populations come to saddles can result in substantial variability in the durations of crawlbys and, as a result, in the periods of population cycles. Our work suggests a new mechanistic hypothesis to explain an important factor in the irregular timing of population cycles and provides a basis for understanding when environmental stochasticity is, and is not, expected to generate cyclic dynamics with variability across periods. 

https://issues.org/new-theory-increasingly-tangled-banks/ Twombly, Saran, Alan Hastings, Tom Miller, Michael Cortez, Karen Abbott, Tanjona Ramiadantsoa, Julie Blackwood, and Olivia Prosper. “New Theory for Increasingly Tangled Banks.” Issues in Science and Technology 38, no. 4 (Summer 2022): 39–44. Theory has fallen out of fashion in the sciences, in favor of data collection and number crunching. But the conceptual frameworks provided by theory are essential for addressing society’s most complex and urgent problems. 

Abstract New graduate students in biology programs may lack the quantitative skills necessary for their research and professional careers. The acquisition of these skills may be impeded by teaching and mentoring experiences that decrease rather than increase students’ beliefs in their ability to learn and apply quantitative approaches. In this opinion piece, we argue that revising instructional experiences to ensure that both student confidence and quantitative skills are enhanced may improve both educational outcomes and professional success. A few studies suggest that explicitly addressing productive failure in an instructional setting and ensuring effective mentoring may be the most effective routes to simultaneously increasing both quantitative self-efficacy and quantitative skills. However, there is little work that specifically addresses graduate student needs, and more research is required to reach evidence-backed conclusions. 

There is a growing recognition that ecological systems can spend extended periods of time far away from an asymptotic state, and that ecological understanding will therefore require a deeper appreciation for how long ecological transients arise. Recent work has defined classes of deterministic mechanisms that can lead to long transients. Given the ubiquity of stochasticity in ecological systems, a similar systematic treatment of transients that includes the influence of stochasticity is important. Stochasticity can of course promote the appearance of transient dynamics by preventing systems from settling permanently near their asymptotic state, but stochasticity also interacts with deterministic features to create qualitatively new dynamics. As such, stochasticity may shorten, extend or fundamentally change a system’s transient dynamics. Here, we describe a general framework that is developing for understanding the range of possible outcomes when random processes impact the dynamics of ecological systems over realistic time scales. We emphasize that we can understand the ways in which stochasticity can either extend or reduce the lifetime of transients by studying the interactions between the stochastic and deterministic processes present, and we summarize both the current state of knowledge and avenues for future advances. 

Long-range synchrony from short-range interactions is a familiar pattern in biological and physical systems, many of which share a common set of ‘universal’ properties at the point of synchronization. Common biological systems of coupled oscillators have been shown to be members of the Ising universality class, meaning that the very simple Ising model replicates certain spatial statistics of these systems at stationarity. This observation is useful because it reveals which aspects of spatial pattern arise independently of the details governing local dynamics, resulting in both deeper understanding of and a simpler baseline model for biological synchrony. However, in many situations a system’s dynamics are of greater interest than their static spatial properties. Here, we ask whether a dynamical Ising model can replicate universal and non-universal features of ecological systems, using noisy coupled metapopulation models with two-cycle dynamics as a case study. The standard Ising model makes unrealistic dynamical predictions, but the Ising model with memory corrects this by using an additional parameter to reflect the tendency for local dynamics to maintain their phase of oscillation. By fitting the two parameters of the Ising model with memory to simulated ecological dynamics, we assess the correspondence between the Ising and ecological models in several of their features (location of the critical boundary in parameter space between synchronous and asynchronous dynamics, probability of local phase changes and ability to predict future dynamics). We find that the Ising model with memory is reasonably good at representing these properties of ecological metapopulations. The correspondence between these models creates the potential for the simple and well-known Ising class of models to become a valuable tool for understanding complex biological systems.