Estimating abundance without recaptures of marked pallid sturgeon in the Mississippi River.

Abundance estimates are essential for assessing the viability of populations and the risks posed by alternative management actions. An effort to estimate abundance via a repeated mark-recapture experiment may fail to recapture marked individuals. We devised a method for obtaining lower bounds on abundance in the absence of recaptures for both panmictic and spatially structured populations. The method assumes few enough recaptures were expected to be missed by random chance. The upper Bayesian credible limit on expected recaptures allows probabilistic statements about the minimum number of individuals present in the population. We applied this method to data from a 12-year survey of pallid sturgeon (Scaphirhynchus albus) in the lower and middle Mississippi River (U.S.A.). None of the 241 individuals marked was recaptured in the survey. After accounting for survival and movement, our model-averaged estimate of the total abundance of pallid sturgeon ≥3 years old in the study area had a 1%, 5%, or 25% chance of being <4,600, 7,000, or 15,000, respectively. When we assumed fish were distributed in proportion to survey catch per unit effort, the farthest downstream reach in the survey hosted at least 4.5-15 fish per river kilometer (rkm), whereas the remainder of the reaches in the lower and middle Mississippi River hosted at least 2.6-8.5 fish/rkm for all model variations examined. The lower Mississippi River had an average density of pallid sturgeon ≥3 years old of at least 3.0-9.8 fish/rkm. The choice of Bayesian prior was the largest source of uncertainty we considered but did not alter the order of magnitude of lower bounds. Nil-recapture estimates of abundance are highly uncertain and require careful communication but can deliver insights from experiments that might otherwise be considered a failure.

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library.

Introduction: Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies. Methods: Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values. Results: We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation. Discussion: A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.

Experimental evidence that source genetic variation drives pathogen emergence.

A pathogen can readily mutate to infect new host types, but this does not guarantee successful establishment in the new habitat. What factors, then, dictate emergence success? One possibility is that the pathogen population cannot sustain itself on the new host type (i.e. host is a sink), but migration from a source population allows adaptive sustainability and eventual emergence by delivering beneficial mutations sampled from the source's standing genetic variation. This idea is relevant regardless of whether the sink host is truly novel (host shift) or whether the sink is an existing or related, similar host population thriving under conditions unfavourable to pathogen persistence (range expansion). We predicted that sink adaptation should occur faster under range expansion than during a host shift owing to the effects of source genetic variation on pathogen adaptability in the sink. Under range expansion, source migration should benefit emergence in the sink because selection acting on source and sink populations is likely to be congruent. By contrast, during host shifts, source migration is likely to disrupt emergence in the sink owing to uncorrelated selection or performance tradeoffs across host types. We tested this hypothesis by evolving bacteriophage populations on novel host bacteria under sink conditions, while manipulating emergence via host shift versus range expansion. Controls examined sink adaptation when unevolved founding genotypes served as migrants. As predicted, adaptability was fastest under range expansion, and controls did not adapt. Large, similar and similarly timed increases in fitness were observed in the host-shift populations, despite declines in mean fitness of immigrants through time. These results suggest that source populations are the origin of mutations that drive adaptive emergence at the edge of a pathogen's ecological or geographical range.

Temperature extremes, density dependence, and southern pine beetle (Coleoptera: Curculionidae) population dynamics in east Texas.

Previous studies of the southern pine beetle, Dendroctonus frontalis Zimm., established that its population in east Texas responds to a delayed density-dependent process, whereas no clear role of climate has been determined. We tested two biological hypotheses for the influence of extreme temperatures on annual southern pine beetle population growth in the context of four alternative hypotheses for density-dependent population regulation. The significance of climate variables and their interaction with population regulation depended on the model of density dependence. The best model included both direct and delayed density dependence of a cubic rather than linear form. Population growth declined with the number of days exceeding 32 degrees C, temperatures previously reported to reduce brood survival. Density dependence also changed with the number of hot days. Growth was highest in years with average minimum winter temperatures. Severely cold winters may reduce survival, whereas warm winters may reduce the efficiency of spring infestation formation. Whereas most previous studies have incorporated climate as an additive effect on growth, we found that the form of delayed density dependence changed with the number of days >32 degrees C. The interaction between temperature and regulation, a potentially common phenomenon in ecology, may explain why southern pine beetle outbreaks do not occur at perfectly regular intervals. Factors other than climate, such as forest management and direct suppression, may have contributed significantly to the timing, severity, and eventual cessation of outbreaks since the mid-1950s.

Density Dependence and Growth Rate: Evolutionary Effects on Resistance Development to Bt (Bacillus thuringiensis).

It has long been recognized that pest population dynamics can affect the durability of a pesticide, but dose remains the primary component of insect resistance management (IRM). For transgenic pesticidal traits such as Bt (Bacillus thuringiensis Berliner (Bacillales: Bacillaceae)), dose (measured as the mortality of susceptibles caused by a toxin) is a relatively fixed characteristic and often falls below the standard definition of high dose. Hence, it is important to understand how pest population dynamics modify durability and what targets they present for IRM. We used a deterministic model of a generic arthropod pest to examine how timing and strength of density dependence interacted with population growth rate and Bt mortality to affect time to resistance. As in previous studies, durability typically reached a minimum at intermediate doses. However, high population growth rates could eliminate benefits of high dose. The timing of density dependence had a more subtle effect. If density dependence operated simultaneously with Bt mortality, durability was insensitive to its strengths. However, if density dependence was driven by postselection densities, decreasing its strength could increase durability. The strength of density dependence could affect durability of both single traits and pyramids, but its influence depended on the timing of density dependence and size of the refuge. Our findings suggest the utility of a broader definition of high dose, one that incorporates population-dynamic context. That maximum growth rates and timing and strength of interactions causing density dependent mortality can all affect durability, also highlights the need for ecologically integrated approaches to IRM research.

Synchrony's double edge: transient dynamics and the Allee effect in stage structured populations.

In populations subject to positive density dependence, individuals can increase their fitness by synchronizing the timing of key life history events. However, phenological synchrony represents a perturbation from a population's stable stage structure and the ensuing transient dynamics create troughs of low abundance that can promote extinction. Using an ecophysiological model of a mass-attacking pest insect, we show that the effect of synchrony on local population persistence depends on population size and adult lifespan. Results are consistent with a strong empirical pattern of increased extinction risk with decreasing initial population size. Mortality factors such as predation on adults can also affect transient dynamics. Throughout the species range, the seasonal niche for persistence increases with the asynchrony of oviposition. Exposure to the Allee effect after establishment may be most likely at northern range limits, where cold winters tend to synchronize spring colonization, suggesting a role for transient dynamics in the determination of species distributions.

Virus population extinction via ecological traps.

Populations are at risk of extinction when unsuitable or when sink habitat exceeds a threshold frequency in the environment. Sinks that present cues associated with high-quality habitats, termed ecological traps, have especially detrimental effects on net population growth at metapopulation scales. Ecological traps for viruses arise naturally, or can be engineered, via the expression of viral-binding sites on cells that preclude viral reproduction. We present a model for virus population growth in a heterogeneous host community, parameterized with data from populations of the RNA bacteriophage Phi6 presented with mixtures of suitable host bacteria and either neutral or trap cells. We demonstrate that viruses can sustain high rates of population growth in the presence of neutral non-hosts as long as some host cells are present, whereas trap cells dramatically reduce viral fitness. In addition, we demonstrate that the efficacy of traps for viral elimination is frequency dependent in spatially structured environments such that population viability is a nonlinear function of habitat loss in dispersal-limited virus populations. We conclude that the ecological concepts applied to species conservation in altered landscapes can also contribute to the development of trap cell therapies for infectious human viruses.

Viral ecology and the maintenance of novel host use.

Viruses can occasionally emerge by infecting new host species. However, the early phases of emergence can hinge upon ecological sustainability of the virus population, which is a product of both within-host population growth and between-host transmission. Insufficient growth or transmission can force virus extinction before the latter phases of emergence, where genetic adaptations that improve host use may occur. We examined the early phase of emergence by studying the population dynamics of RNA phages in replicated laboratory environments containing native and novel host bacteria. To predict the breadth of transmission rates allowing viral persistence on each species, we developed a simple model based on in vitro data for phage growth rate over a range of initial population densities on both hosts. Validation of these predictions using serial passage experiments revealed a range of transmission rates for which the native host was a source and the novel host was a sink. In this critical range of transmission rates, periodic exposure to the native host was sufficient for the maintenance of the viral population on the novel host. We argue that this effect should facilitate adaptation by the virus to utilize the novel host--often crucial in subsequent phases of emergence.

Bacteriophage migration via nematode vectors: host-parasite-consumer interactions in laboratory microcosms.

Pathogens vectored by nematodes pose serious agricultural, economic, and health threats; however, little is known of the ecological and evolutionary aspects of pathogen transmission by nematodes. Here we describe a novel model system with two trophic levels, bacteriophages and nematodes, each of which competes for bacteria. We demonstrate for the first time that nematodes are capable of transmitting phages between spatially distinct patches of bacteria. This model system has considerable advantages, including the ease of maintenance and manipulation at the laboratory bench, the ability to observe many generations in short periods, and the capacity to freeze evolved strains for later comparison to their ancestors. More generally, experimental studies of complex multispecies interactions, host-pathogen coevolution, disease dynamics, and the evolution of virulence may benefit from this model system because current models (e.g., chickens, mosquitoes, and malaria parasites) are costly to maintain, are difficult to manipulate, and require considerable space. Our initial explorations centered on independently assessing the impacts of nematode, bacterium, and phage population densities on virus migration between host patches. Our results indicated that virus transmission increases with worm density and host bacterial abundance; however, transmission decreases with initial phage abundance, perhaps because viruses eliminate available hosts before migration can occur. We discuss the microbial growth dynamics that underlie these results, suggest mechanistic explanations for nematode transmission of phages, and propose intriguing possibilities for future research.

Determinism in a transient assemblage: the roles of dispersal and local competition.

Both dispersal and local competitive ability may determine the outcome of competition among species that cannot coexist locally. I develop a spatially implicit model of two-species competition at a small spatial scale. The model predicts the relative fitness of two competitors based on local reproductive rates and regional dispersal rates in the context of the number, size, and extinction probability of habitat patches in the landscape. I test the predictions of this model experimentally using two genotypes of the bacteriophagous soil nematode Caenorhabditis elegans in patchy microcosms. One genotype has higher fecundity while the other is a better disperser. With such a fecundity-dispersal trade-off between competitors, the model predicts that relative fitness will be affected most by local population size when patches do not go extinct and by the number of patches when there is a high probability of patch extinction. The microcosm experiments support the model predictions. Both approaches suggest that competitive dominance in a patchily distributed transient assemblage will depend upon the architecture and predictability of the environment. These mechanisms, operating at a small scale with high spatial admixture, may be embedded in a larger metacommunity process.