Across-model spread and shrinking in predicting peatland carbon dynamics under global change.

Large across-model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across-model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO2 levels and five temperature levels. We developed a matrix-based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter-model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter-model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO2 (eCO2 ) concentrations in the measurements, simulated NEP responded positively to eCO2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across-model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Dynamics and functional diversity of the smallest phytoplankton on the Northeast US Shelf.

Picophytoplankton are the most abundant primary producers in the ocean. Knowledge of their community dynamics is key to understanding their role in marine food webs and global biogeochemical cycles. To this end, we analyzed a 16-y time series of observations of a phytoplankton community at a nearshore site on the Northeast US Shelf. We used a size-structured population model to estimate in situ division rates for the picoeukaryote assemblage and compared the dynamics with those of the picocyanobacteria Synechococcus at the same location. We found that the picoeukaryotes divide at roughly twice the rate of the more abundant Synechococcus and are subject to greater loss rates (likely from viral lysis and zooplankton grazing). We describe the dynamics of these groups across short and long timescales and conclude that, despite their taxonomic differences, their populations respond similarly to changes in the biotic and abiotic environment. Both groups appear to be temperature limited in the spring and light limited in the fall and to experience greater mortality during the day than at night. Compared with Synechococcus, the picoeukaryotes are subject to greater top-down control and contribute more to the region's primary productivity than their standing stocks suggest.

Modeling temporal dynamics of genetic diversity in stage-structured plant populations with reference to demographic genetic structure.

Predicting temporal dynamics of genetic diversity is important for assessing long-term population persistence. In stage-structured populations, especially in perennial plant species, genetic diversity is often compared among life history stages, such as seedlings, juveniles, and flowerings, using neutral genetic markers. The comparison among stages is sometimes referred to as demographic genetic structure, which has been regarded as a proxy of potential genetic changes because individuals in mature stages will die and be replaced by those in more immature stages over the course of time. However, due to the lack of theoretical examination, the basic property of the stage-wise genetic diversity remained unclear. We developed a matrix model which was made up of difference equations of the probability of non-identical-by-descent of each life history stage at a neutral locus to describe the dynamics and the inter-stage differences of genetic diversity in stage-structured plant populations. Based on the model, we formulated demographic genetic structure as well as the annual change rate of the probability of non-identical-by-descent (denoted as η). We checked if theoretical expectations on demographic genetic structure and η obtained from our model agreed with computational results of stochastic simulation using randomly generated 3,000 life histories. We then examined the relationships of demographic genetic structure with effective population size Ne, which is the determinants of diversity loss per generation time. Theoretical expectations on η and demographic genetic structure fitted well to the results of stochastic simulation, supporting the validity of our model. Demographic genetic structure varied independently of Ne and η, while having a strong correlation with stable stage distribution: genetic diversity was lower in stages with fewer individuals. Our results indicate that demographic genetic structure strongly reflects stable stage distribution, rather than temporal genetic dynamics, and that inferring future genetic diversity solely from demographic genetic structure would be misleading. Instead of demographic genetic structure, we propose η as an useful tool to predict genetic diversity at the same time scale as population dynamics (i.e., per year), facilitating evaluation on population viability from a genetic point of view.

Modeling the evolution of herbicide resistance in weed species with a complex life cycle.

A growing number of weed species have evolved resistance to herbicides in recent years, which causes an immense financial burden to farmers. An increasingly popular method of weed control is the adoption of crops that are resistant to specific herbicides, which allows farmers to apply the herbicide during the growing season without harming the crop. If such crops are planted in the presence of closely related weed species, it is possible that resistance genes could transfer from the crop species to feral populations of the wild species via gene flow and become stably introgressed under ongoing selective pressure by the herbicide. We use a density-dependent matrix model to evaluate the effect of planting such crops on the evolution of herbicide resistance under a range of management scenarios. Our model expands on previous simulation studies by considering weed species with a more complex life cycle (perennial, rhizomatous weed species), studying the effect of environmental variation in herbicide effectiveness, and evaluating the role of common simplifying genetic assumptions on resistance evolution. Our model predictions are qualitatively similar to previous modeling studies using species with a simpler life cycle, which is, crop rotation in combination with rotation of herbicide site of action effectively controls weed populations and slows the evolution of herbicide resistance. We find that ignoring the effect of environmental variation can lead to an over- or under-prediction of the speed of resistance evolution. The effect of environmental variation in herbicide effectiveness depends on the resistance allele frequency in the weed population at the beginning of the simulation. Finally, we find that degree of dominance and ploidy level have a much larger effect on the predicted speed of resistance evolution compared to the rate of gene flow.

Examination of the interaction between age-specific predation and chronic disease in the Greater Yellowstone Ecosystem.

Predators may create healthier prey populations by selectively removing diseased individuals. Predators typically prefer some ages of prey over others, which may, or may not, align with those prey ages that are most likely to be diseased. The interaction of age-specific infection and predation has not been previously explored and likely has sizable effects on disease dynamics. We hypothesize that predator cleansing effects will be greater when the disease and predation occur in the same prey age groups. We examine the predator cleansing effect using a model where both vulnerability to predators and pathogen prevalence vary with age. We tailor this model to chronic wasting disease (CWD) in mule deer and elk populations in the Greater Yellowstone Ecosystem, with empirical data from Yellowstone grey wolves and cougars. Model results suggest that under moderate, yet realistic, predation pressure from cougars and wolves independently, predators may decrease CWD outbreak size substantially and delay the accumulation of symptomatic deer and elk. The magnitude of this effect is driven by the ability of predators to selectively remove late-stage CWD infections that are likely the most responsible for transmission, but this may not be the age class they typically select. Thus, predators that select for infected young adults over uninfected juveniles have a stronger cleansing effect, and these effects are strengthened when transmission rates increase with increasing prey morbidity. There are also trade-offs from a management perspective-that is, increasing predator kill rates can result in opposing forces on prey abundance and CWD prevalence. Our modelling exploration shows that predators have the potential to reduce prevalence in prey populations when prey age and disease severity are considered, yet the strength of this effect is influenced by predators' selection for demography or body condition. Current CWD management focuses on increasing cervid hunting as the primary management tool, and our results suggest predators may also be a useful tool under certain conditions, but not necessarily without additional impacts on host abundance and demography. Protected areas with predator populations will play a large role in informing the debate over predator impacts on disease.

Modelling the responses of partially migratory metapopulations to changing seasonal migration rates: From theory to data.

Among-individual and within-individual variation in expression of seasonal migration versus residence is widespread in nature and could substantially affect the dynamics of partially migratory metapopulations inhabiting seasonally and spatially structured environments. However, such variation has rarely been explicitly incorporated into metapopulation dynamic models for partially migratory systems. We, therefore, lack general frameworks that can identify how variable seasonal movements, and associated season- and location-specific vital rates, can control system persistence. We constructed a novel conceptual framework that captures full-annual-cycle dynamics and key dimensions of metapopulation structure for partially migratory species inhabiting seasonal environments. We conceptualize among-individual variation in seasonal migration as two variable vital rates: seasonal movement probability and associated movement survival probability. We conceptualize three levels of within-individual variation (i.e. plasticity), representing seasonal or annual variation in seasonal migration or lifelong fixed strategies. We formulate these concepts as a general matrix model, which is customizable for diverse life-histories and seasonal landscapes. To illustrate how variable seasonal migration can affect metapopulation growth rate, demographic structure and vital rate elasticities, we parameterize our general models for hypothetical short- and longer-lived species. Analyses illustrate that elasticities of seasonal movement probability and associated survival probability can sometimes equal or exceed those of vital rates typically understood to substantially influence metapopulation dynamics (i.e. seasonal survival probability or fecundity), that elasticities can vary non-linearly, and that metapopulation outcomes depend on the level of within-individual plasticity. We illustrate how our general framework can be applied to evaluate the consequences of variable and changing seasonal movement probability by parameterizing our models for a real partially migratory metapopulation of European shags Gulosus aristotelis assuming lifelong fixed strategies. Given observed conditions, metapopulation growth rate was most elastic to breeding season adult survival of the resident fraction in the dominant population. However, given doubled seasonal movement probability, variation in survival during movement would become the primary driver of metapopulation dynamics. Our general conceptual and matrix model frameworks, and illustrative analyses, thereby highlight complex ways in which structured variation in seasonal migration can influence dynamics of partially migratory metapopulations, and pave the way for diverse future theoretical and empirical advances.

Microhabitats associated with solar energy development alter demography of two desert annuals.

Political and economic initiatives intended to increase energy production while reducing carbon emissions are driving demand for solar energy. Consequently, desert regions are now targeted for development of large-scale photovoltaic solar energy facilities. Where vegetation communities are left intact or restored within facilities, ground-mounted infrastructure may have negative impacts on desert-adapted plants because it creates novel rainfall runoff and shade conditions. We used experimental solar arrays in the Mojave Desert to test how these altered conditions affect population dynamics for a closely related pair of native annual plants: rare Eriophyllum mohavense and common E. wallacei. We estimated aboveground demographic rates (seedling emergence, survivorship, and fecundity) over 7 yr and used seed bank survival rates from a concurrent study to build matrix models of population growth in three experimental microhabitats. In drier years, shade tended to reduce survival of the common species, but increase survival of the rare species. In a wet year, runoff from panels tended to increase seed output for both species. Population growth projections from microhabitat-specific matrix models showed stronger effects of microhabitat under wetter conditions, and relatively little effect under dry conditions (lack of rainfall was an overwhelming constraint). Performance patterns across microhabitats in the wettest year differed between rare and common species. Projected growth of E. mohavense was substantially reduced in shade, mediated by negative effects on aboveground demographic rates. Hence, the rare species were more susceptible to negative effects of panel infrastructure in wet years that are critical to seed bank replenishment. Our results suggest that altered shade and water runoff regimes associated with energy infrastructure will have differential effects on demographic transitions across annual species and drive population-level processes that determine local abundance, resilience, and persistence.

Decreased Precipitation Predictability Negatively Affects Population Growth through Differences in Adult Survival.

Global climate change is leading to decreased climatic predictability. Theoretical work indicates that changes in the climate's intrinsic predictability will affect population dynamics and extinction, but experimental evidence is scarce. Here, we experimentally tested whether differences in intrinsic precipitation predictability affect population dynamics of the European common lizard (Zootoca vivipara) by simulating more predictable (MP) and less predictable (LP) precipitation in 12 seminatural populations over 3 years and measuring different vital rates. A seasonal age-structured matrix model was parametrized to assess treatment effects on vital rates and asymptotic population growth (λ). There was a nonsignificant trend for survival being higher in MP than in LP precipitation, and no differences existed in reproductive rates. Small nonsignificant survival differences in adults explained changes in λ, and survival differences among age classes were in line with predictions from cohort resonance. As a result, λ was significantly higher in MP than in LP precipitation. This experimentally shows that small effects have major consequences on λ, that forecasted decreases in precipitation predictability are likely to exacerbate the current rate of population decline and extinction, and that stage-structured matrix models are required to unravel the aftermath of climate change.

Ecological drivers of jellyfish blooms - The complex life history of a 'well-known' medusa (Aurelia aurita).

Jellyfish blooms are conspicuous demographic events with significant ecological and socio-economic impact. Despite worldwide concern about an increased frequency and intensity of such mass occurrences, predicting their booms and busts remains challenging. Forecasting how jellyfish populations may respond to environmental change requires considering their complex life histories. Metagenic life cycles, which include a benthic polyp stage, can boost jellyfish mass occurrences via asexual recruitment of pelagic medusae. Here we present stage-structured matrix population models with monthly, individual-based demographic rates of all life stages of the moon jellyfish Aurelia aurita L. (sensu stricto). We investigate the life-stage dynamics of these complex populations under low and high food conditions to illustrate how changes in medusa density depend on non-medusa stage dynamics. We show that increased food availability can be an important ecological driver of jellyfish mass occurrences, as it can temporarily shift the population structure from polyp- to medusa-dominated. Projecting populations for a winter warming scenario additionally enhanced the booms and busts of jellyfish blooms. We identify demographic key variables that control the intensity and frequency of jellyfish blooms in response to environmental drivers such as habitat eutrophication and climate change. By contributing to an improved understanding of mass occurrence phenomena, our findings provide perspective for future management of ecosystem health.

Estimating the number and length of episodes in disability using a Markov chain approach.

BACKGROUND: Markov models are a key tool for calculating expected time spent in a state, such as active life expectancy and disabled life expectancy. In reality, individuals often enter and exit states recurrently, but standard analytical approaches are not able to describe this dynamic. We develop an analytical matrix approach to calculating the expected number and length of episodes spent in a state. METHODS: The approach we propose is based on Markov chains with rewards. It allows us to identify the number of entries into a state and to calculate the average length of episodes as total time in a state divided by the number of entries. For sampling variance estimation, we employ the block bootstrap. Two case studies that are based on published literature illustrate how our methods can provide new insights into disability dynamics. RESULTS: The first application uses a classic textbook example on prednisone treatment and liver functioning among liver cirrhosis patients. We replicate well-known results of no association between treatment and survival or recovery. Our analysis of the episodes of normal liver functioning delivers the new insight that the treatment reduced the likelihood of relapse and extended episodes of normal liver functioning. The second application assesses frailty and disability among elderly people. We replicate the prior finding that frail individuals have longer life expectancy in disability. As a novel finding, we document that frail individuals experience three times as many episodes of disability that were on average twice as long as the episodes of nonfrail individuals. CONCLUSIONS: We provide a simple analytical approach for calculating the number and length of episodes in Markov chain models. The results allow a description of the transition dynamics that goes beyond the results that can be obtained using standard tools for Markov chains. Empirical applications using published data illustrate how the new method is helpful in unraveling the dynamics of the modeled process.

The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model.

BACKGROUND: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. METHODS: Data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. RESULTS: The optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. CONCLUSION: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

Sex-specific early survival drives adult sex ratio bias in snowy plovers and impacts mating system and population growth.

Adult sex ratio (ASR) is a central concept in population biology and a key factor in sexual selection, but why do most demographic models ignore sex biases? Vital rates often vary between the sexes and across life history, but their relative contributions to ASR variation remain poorly understood-an essential step to evaluate sex ratio theories in the wild and inform conservation. Here, we combine structured two-sex population models with individual-based mark-recapture data from an intensively monitored polygamous population of snowy plovers. We show that a strongly male-biased ASR (0.63) is primarily driven by sex-specific survival of juveniles rather than adults or dependent offspring. This finding provides empirical support for theories of unbiased sex allocation when sex differences in survival arise after the period of parental investment. Importantly, a conventional model ignoring sex biases significantly overestimated population viability. We suggest that sex-specific population models are essential to understand the population dynamics of sexual organisms: reproduction and population growth are most sensitive to perturbations in survival of the limiting sex. Overall, our study suggests that sex-biased early survival may contribute toward mating system evolution and population persistence, with implications for both sexual selection theory and biodiversity conservation.

Evidence of intraspecific prey switching: stage-structured predation of polar bears on ringed seals.

Prey switching is a phenomenon in which a predator disproportionately consumes the most abundant prey type, and switches to preferentially consume another prey type if the first becomes relatively rare. This concept may be expanded outside of its usual usage describing switching between prey species (interspecific), to describe switching between prey stages within a given species (intraspecific). Polar bears (Ursus maritimus) are thought to seek out naive ringed seal (Pusa hispida) pups in the spring, but how that may change in years with low seal productivity is unknown. We addressed two main questions: If polar bears typically select for ringed seals' pups, how does this change in years with reduced ringed-seal productivity? How does polar bear predation during years with low ringed-seal productivity impact the ringed seal population? We created a matrix population model for ringed seals to get an estimate of each stage's availability to polar bears in the spring. These estimates of availability were combined with existing studies on the ages of seals consumed by polar bears in years of both high and low ringed seal productivity. Our results suggest that polar bears typically strongly select for ringed seal pups, but switch to disproportionately select older ringed seals in years with low pup availability. The effects of this on ringed seal population growth appear negligible. Non-intuitive results on the effect of prey switching on the prey population emphasize the importance of considering environmental sequences rather than individual years.

Generation Time in Structured Populations.

Generation time is an intuitively simple concept, but for structured populations there are multiple definitions and no general understanding of how they relate to each other. François Bienvenu and Stéphane Legendre, in their note "A New Approach to the Generation Time in Matrix Population Models," appearing in the June 2015 issue of The American Naturalist, introduced a new measure of generation time Ta, the average time between birth events in an ancestral lineage, and derived the remarkably simple formula [Formula: see text] for any matrix model, where F is the fecundity matrix, v is reproductive value, and w is stable population structure. Here I generalize their formula and interpretations of Ta to a continuous or continuous-discrete population structure and derive similar formulas for three other established generation time measures: average parent age across all births at one time ([Formula: see text]) and mean parent age at birth events for a cohort (µ1) or generation (Tc). The new formulas reveal that these differently defined measures are unexpectedly often identical in value and clarify when they differ.

Measurement error of state variables creates substantial bias in results of demographic population models.

Integral projection and matrix population models are commonly used in ecological and conservation studies to assess the health and extinction risk of populations. These models use one (or more) measurable state variable(s), such as size or age, to predict individual performance, which, ideally, is the sole determinant of an individual's expected fate. However, even if ecologists successfully identify and measure the observable state variable(s) that best predicts individual fate, we are rarely, if ever, able to perfectly measure state for many species, especially those with size structure, where total plant biomass or starch stores, for example, may be the best predictors of fate. Here, we used a series of simulations to test how this imperfect quantification of actual state ("measurement error") leads to inaccurate prediction of state-dependent fates and influences the predictions of structured population models. We simulated 10 yr of best practice field data collection using known vital rate functions and incorporated measurement error of different magnitudes and types (completely random, temporal, and individual based) for two size-structured life histories. We found that even for conservative error rates, most types of measurement error increased the median predicted population growth rate by 1-2% growth per year. However, the magnitude of this error differed substantially with life history strategy and error type, with some scenarios resulting in >8% median overestimation of population growth rate. This effect arises largely from the well-known econometrics problem of "regression dilution" (overestimation of the intercept and underestimation of the slope of a regression when the predictor variable is measured with error), which in our simulations typically results in overly optimistic predictions of small or young individuals' vital rates. Our results suggest that the problem of measurement error for state variables, present in many demographic studies but virtually unacknowledged in the ecological literature, may lead to substantial misestimation of population behavior, resulting in erroneous inferences about not only growth, but also extinction risk and other aspects of population dynamics.

Trait-based predictions and responses from laboratory mite populations to harvesting in stochastic environments.

Predictions on population responses to perturbations are often derived from trait-based approaches like integral projection models (IPMs), but are rarely tested. IPMs are constructed from functions that describe survival, growth and reproduction in relation to the traits of individuals and their environment. Although these functions comprise biologically non-informative statistical coefficients within standard IPMs, model parameters of the recently developed dynamic energy budget IPM (DEB-IPM) are life-history traits like "length at maturation" and "maximum reproduction rate". Testing predictions from mechanistic IPMs against empirical observations can therefore provide functional insights into the links between individual life history, the environment and population dynamics. Here, we compared the population dynamics of the bulb mite (Rhizoglyphus robini) predicted by a DEB-IPM with those observed in an experiment where populations experienced daily food rations that were either positively correlated over time (red noise), negatively (blue noise) or uncorrelated (white noise). We also selectively harvested large adults in half of these populations. The model failed to generate detailed predictions of population structure as juvenile numbers were overestimated; likely because juvenile-adult interference competition was underestimated. The model performed well at the population level as, for both harvested and unharvested populations, simulations matched the observed, long-term stochastic growth rate λs . We next generalised the model to investigate how stochastic change affects mite λs , which correlated well with the frequency f of experiencing periods of good environment, but, due to the relationship between f and noise colour ρ, did not correlate well with shifts in ρ. The sensitivity of λs to perturbations in life-history parameters depended on the type of stochastic change, as well as population growth. Our findings show that responses to differential mortality depend on individual life-history traits, environmental characteristics and population growth. As long-term climate change causes ever greater environmental fluctuations, trait-based approaches will be increasingly important in predicting population responses to change. We therefore conclude by illustrating what questions can be examined with mechanistic trait-based models like the DEB-IPM, the answers to which will advance our knowledge of the functional links between individual traits, the environment and population dynamics.

Density feedbacks mediate effects of environmental change on population dynamics of a semidesert rodent.

Population dynamics are the result of an interplay between extrinsic and intrinsic environmental drivers. Predicting the effects of environmental change on wildlife populations therefore requires a thorough understanding of the mechanisms through which different environmental drivers interact to generate changes in population size and structure. In this study, we disentangled the roles of temperature, food availability and population density in shaping short- and long-term population dynamics of the African striped mouse, a small rodent inhabiting a semidesert with high intra- and interannual variation in environmental conditions. We parameterized a female-only stage-structured matrix population model with vital rates depending on temperature, food availability and population density, using monthly mark-recapture data from 1609 mice trapped over 9 years (2005-2014). We then applied perturbation analyses to determine relative strengths and demographic pathways of these drivers in affecting population dynamics. Furthermore, we used stochastic population projections to gain insights into how three different climate change scenarios might affect size, structure and persistence of this population. We identified food availability, acting through reproduction, as the main driver of changes in both short- and long-term population dynamics. This mechanism was mediated by strong density feedbacks, which stabilized the population after high peaks and allowed it to recover from detrimental crashes. Density dependence thus buffered the population against environmental change, and even adverse climate change scenarios were predicted to have little effect on population persistence (extinction risk over 100 years <5%) despite leading to overall lower abundances. Explicitly linking environment-demography relationships to population dynamics allowed us to accurately capture past population dynamics. It further enabled establishing the roles and relative importances of extrinsic and intrinsic environmental drivers, and we conclude that doing this is essential when investigating impacts of climate change on wildlife populations.

Influence of body condition on the population dynamics of Atlantic salmon with consideration of the potential impact of sea lice.

Atlantic salmon Salmo salar is an iconic species of high conservation and economic importance. At sea, individuals typically are subject to sea lice infestation, which can have detrimental effects on their host. Over recent decades, the body condition and marine survival in NE Atlantic stocks have generally decreased, reflected in fewer adults returning to rivers, which is partly attributable to sea lice. We developed a deterministic stage-structured population model to assess condition-mediated population dynamics resulting in changing fecundity, age at sexual maturation and marine survival rate. The model is parameterized using data from the North Esk system, north-east Scotland. Both constant and density-dependent juvenile survival rates are considered. We show that even small sea lice-mediated changes in mean body condition of MSW can cause substantial population declines, whereas 1SW condition is less influential. Density dependence alleviates the condition-mediated population effect. The resilience of the population to demographic perturbations declines as adult condition is reduced. Indirect demographic changes in salmonid life-history traits (e.g., body condition) are often considered unimportant for population trajectory. The model shows that Atlantic salmon population dynamics can be highly responsive to sea lice-mediated effects on adult body condition, thus highlighting the importance of non-lethal parasitic long-term effects.

Simulated developmental and reproductive impacts on amphibian populations and implications for assessing long-term effects.

Fish endpoints measured in early life stage toxicity tests are often used as representative of larval amphibian sensitivity in Ecological Risk Assessment (ERA). This application potentially overlooks the impact of developmental delays on amphibian metamorphosis, and thereby reduced survival, in amphibian populations constrained by habitat availability. Likewise, the effects of reduced productivity or altered sexual development as a result of chemical exposure are not presented in terms of lower population fecundity in these surrogate tests. Translating endpoints measured in toxicity tests to those that are more representative of amphibian ecology and population dynamics provides a means of identifying how developmental effects result in long-term impacts. Here we compare effects of developmental delay on metamorphosis success in six anuran species and simulate population-level impacts of subsequent reductions in larval survival as well as potential reductions in fecundity as a result of developmental impacts. We use deterministic matrix models to compare realistic combinations of amphibian demographic rates and relative impacts of reduced growth on larval survival and subsequently on population growth. Developmental delays are less detrimental in species with longer and less synchronous larval periods. All six species were most sensitive to changes in first-year survival, and damping ratios were generally a good indicator of resilience to perturbations in both larval survival and fecundity. Further identification of species and population-level vulnerabilities can improve the evaluation of sublethal effects in relevant context for ERA.

Temporal-specific roles of Rac1 during vascular development and retinal angiogenesis.

Angiogenesis, the formation of new blood vessels by remodeling and growth of pre-existing vessels, is a highly orchestrated process that requires a tight balance between pro-angiogenic and anti-angiogenic factors and the integration of their corresponding signaling networks. The family of Rho GTPases, including RhoA, Rac1, and Cdc42, play a central role in many cell biological processes that involve cytoskeletal changes and cell movement. Specifically for Rac1, we have shown that excision of Rac1 using a Tie2-Cre animal line results in embryonic lethality in midgestation (embryonic day (E) 9.5), with multiple vascular defects. However, Tie2-Cre can be also expressed during vasculogenesis, prior to angiogenesis, and is active in some hematopoietic precursors that can affect vessel formation. To circumvent these limitations, we have now conditionally deleted Rac1 in a temporally controlled and endothelial-restricted fashion using Cdh5(PAC)-iCreERT2 transgenic mice. In this highly controlled experimental in vivo system, we now show that Rac1 is required for embryonic vascular integrity and angiogenesis, and for the formation of superficial and deep vascular networks in the post-natal developing retina, the latter involving a novel specific function for Rac1 in vertical blood vessel sprouting. Aligned with these findings, we show that RAC1 is spatially involved in endothelial cell migration, invasion, and radial sprouting activities in 3D collagen matrix in vitro models. Hence, Rac1 and its downstream molecules may represent potential anti-angiogeneic therapeutic targets for the treatment of many human diseases that involve aberrant neovascularization and blood vessel overgrowth.