201 research outputs found
Data and code from: "Interaction network structure and spatial patterns influence invasiveness and invasibility in a stochastic model of plant communities"
No full textThis archive includes the scripts and data used to run and analyze the simulations involved in “Interaction network structure and spatial patterns influence invasiveness and invasibility in a stochastic model of plant communities” by Nicole L. Kinlock and Stephan B. Munch in Oikos (doi: 10.1111/oik.08453)Please see the README file for a detailed guide.</div
Novel nonparametric approaches to stock assessment and regime shift prediction
No full textEcosystem dynamics are often complex, nonlinear, and characterized by criticalthresholds or regime changes. Despite these difficulties, resource managers mustaccurately forecast species abundance and anticipate impending regime shifts in order toimplement sustainable management plans. In the first part of this thesis I explicitly describe a nonparametric method formultivariate forecasting which I call the MS-Map and evaluate its performance relative toa suite of parametric models. I found that, in the presence of noise, it is often possible toobtain more accurate forecasts from the MS-Map than from the model that was used togenerate the data. The inclusion of additional species yielded a large improvement forthe nonparametric MS-Map, a smaller improvement for the control model, and only aslight improvement for the alternative multi-species parametric model. When applied torockfish larval abundance data from the CalCOFI survey, the performance of the MS-Map improved when additional species were included. These results suggest that flexiblenonparametric modeling approaches should be considered for ecosystem management. In the second part of this thesis, using the three-group fishery model previouslystudied by Biggs et al. (2009), I tested a suite of statistical regime shift indicators underthe ecologically realistic conditions of high, correlated noise with short time series and arapidly changing driving variable. I found that all indicators perform poorly underrealistic conditions with the exception of the variance indicator. In contrast toexpectations from previous work, the noise spectrum did not have a strong effect onindicator performance. The amount of data used to calculate the indicator had a largeimpact on performance. Also contrary to prior work, I found that the value of the spectralratio was not a reliable indicator of an impending shift. Future research should focus ontechniques that incorporate multiple data sources simultaneously, thus reducing the timeneeded to detect an impending shift.Advisor(s): Stephan B. Munch. Committee Member(s): Ellen K. Pikitch; Perry deValpine.Stony Brook University Libraries. SBU Graduate School in Department of Marine and Atmospheric Science. Lawrence Martin (Dean of Graduate School)
Thermal plasticity within and across generations and its relevance to contemporary evolution
No full text122 pg.Understanding and predicting how populations will react to changes in the environment is a long-standing goal in evolutionary ecology. It is also of considerable practical importance, as anthropogenic changes stress species worldwide. The relevance of phenotypic plasticity is becoming more apparent as species are forced to cope with rapid changes in the environment. This dissertation explores ways in which phenotypic plasticity will play a major role in determining the future of populations. In Chapters 1 and 2, I evaluate a modeling framework that could be used to predict plastic changes in key life history traits of ectotherms brought about by temperature. This work, based on the metabolic theory of ecology (MTE), assumes that biological rates scale exponentially with temperature. I first show the validity of the MTE for predicting lifespan gradients within species and then apply this temperature-life history relationship to predict changes in ectotherms resulting from global temperature increases over the next 50 years. In Chapter 3, I experimentally test the plastic response of sheepshead minnows, Cyprinodon variegatus, an estuarine fish common to the east coast, to combinations of temperature (24, 29, 34??C) and food availability (60, 80, or 100% of maximum consumption). The thermal response of juvenile growth rate was mediated by food availability, while the age at maturation was independently affected by temperature and food. Notably, and despite very different thermal and feeding regimes, the fish matured within a small size window. In Chapters 4 and 5, I explore transgenerational plasticity (TGP) as a means to cope with temperature changes. When the temperature experienced by the parents acts as a reliable indicator of thermal offspring environment, a parent can "pre-program" offspring traits appropriate for the predicted environment. This transfer of information from parent to offspring has been termed TGP, and is well studied in plants and invertebrates. In these chapters, I show that thermal TGP has a strong effect in larval growth of sheepshead minnows. I also explore how transgenerational and phenotypic plasticity interact to shape the size of fish throughout life, and provide evidence suggesting that the TGP effect lasts for at least 2 generations.Advisor(s): Munch, Stephan B.. Committee Member(s): Conover, David O.Futuyma, Douglas J.Lonsdale, Darcy J.Travis, Joseph ;Stony Brook University Libraries. SBU Graduate School in Department of Marine and Atmospheric Science. Charles Taber (Dean of Graduate School)
Semiparametric Bayesian modeling of density dependence
No full text110 pg.Density dependence is a foundation of population biology. Analysis of population data with parametric models has long provided estimates of the maximum reproductive rate and the form of density dependence. These in turn determine the limit of sustainable harvest and the population's stability, respectively. However, standard parametric analyses of population data generate incorrect inferences of density dependence in noisy and short series. Therefore, there is a clear need for improved statistical methods for inferring density dependence. In this thesis, I developed new semiparametric Bayesian (SB) methods for estimating reproductive rates and for identifying forms of density dependence. Using simulated data, I validated the superiority of the SB methods to parametric alternatives. Then, I conducted SB analyses of 285 fish populations' datasets to estimate reproductive rates and to identify the forms of density dependence. I compared the results of the SB analyses with those based on standard parametric analyses of the same datasets. The SB analysis indicated that the forms of density dependence in 3.4% of the datasets are Allee effects, whereas the parametric analysis indicated 1.5%, suggesting that Allee effects are more than twice as often as previously thought. However, both the SB and the parametric model (the linear model) generated essentially the same estimates of the reproductive rates, indicating that the linear model may be a reasonable approach to inferring the reproductive rates of fish populations.Advisor(s): Munch, Stephan B; Cerrato, Robert M. Committee Member(s): Ferson, Scott ; Ginzburg, Lev ; Sugihara, George.Stony Brook University Libraries. SBU Graduate School in Department of Marine and Atmospheric Science. Charles Taber (Dean of Graduate School)
Recommended from our members
Empirical Modeling of Population Recovery Using Marine Rotifers
No full textThree quarters of the world’s fisheries are classified as overexploited or depleted. Management programs have mainly focused on reducing the fishing pressure on these stocks. However, some stocks fail to rebound even after fishing effort is reduced and hatchery programs may be used to facilitate population recovery. Despite substantial investment in hatchery supplementation, failed programs outnumber successful ones. It therefore seems vital to explore the abiotic and biotic factors that hinder their success. This thesis addresses the performance of several active recovery policies through the use of multispecies microcosms. Specifically, I ask 1) whether one or several supplementation efforts are needed before a sustainable stock population is established and 2) what factors influence the success or failure of recovery in these microcosms. My results show that the community within an ecosystem may strongly influence a recovery program’s likelihood of success and that multiple small additions may offer a better chance of success than one or several large additions. My results support previously made arguments that community ecology is an important framework for fisheries management. Moreover, commercial fishing alters community structure and this may happen in a way that inhibits population recovery. I suggest reconceiving population recovery as ‘facilitated invasion’ may provide useful guidance for designing future recovery programs
Assessing seasonal demographic covariation to understand environmental‐change impacts on a hibernating mammal
No full textNatural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development and reproduction). The resulting covariation in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor‐analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual‐based demography from yellow‐bellied marmots (Marmota flaviventer) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneously, negative responses of winter survival and reproductive‐status change to declining environmental quality result in a higher risk of population quasi‐extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments
Extensions of empirical dynamic modeling for prediction and management in ecological systems
No full textHumans simultaneously depend on and affect the health of natural ecosystems on a global scale, so it is important to establish ecosystem management practices that will ensure longevity and mutualism in the relationship between humans and nature. For decades, scientists have worked in a single-species paradigm to inform most management decisions in ecology. Specifically, species have traditionally been modeled and assessed individually, with limited consideration of how they interact with other species and drivers in their ecosystems. This has led to inaccurate predictions in the past, so there has been a recent push to account for more complexity in ecological models, as this would facilitate better management decisions. While one natural extension is to incorporate multiple variables into mechanistic models, this is challenging and inefficient with our current understanding of ecosystems. Alternatively, data-driven models offer a way to predict population dynamics without requiring specific inputs for all ecosystem components.In this dissertation, we explore empirical dynamic modeling, a data-driven approach to forecasting which is derived from principles of dynamical systems theory. Empirical dynamic modeling is a promising tool that accounts for system complexity without requiring strong assumptions or full system observations. However, it cannot cope with some limitations that are common in ecological datasets, including short time series and missing samples. Thus, we develop extensions of empirical dynamic modeling to address these limitations. We then apply this approach along with optimal control methods to generate management decisions in ecological pest control scenarios. Throughout the dissertation, we demonstrate the effectiveness of our method developments on a wide range of simulated data examples in addition to empirical data from high-impact terrestrial and aquatic ecosystems
Supplement 1. Matlab software used in this study.
No full textFile List
Folder lists:
Data
GP(the linear model)
GP(the constant model)
GP(the S model)
GP(the lnS model)
Ricker(integral)
BH(integral)
Download all files at once -- AllFilesAtOnce.zip (md5: b667a9b12b231ebbb2d06da38f6b117a) Description
'Data' contains a script file used for generating simulated data sets.
'GP(the linear model)' contains functions used for running the semiparametric Bayesian model with the linear mean function) to estimate log(alpha). The outputs of this model is mean ('mloga') and variance ('vloga)' of the posterior distribution of 'log(alpha)', where 'alpha' is reproductive rate estimated at S=0.
'GP(the constant model)' contains functions used for running the semiparametric Bayesian model (with the constant mean function) to estimate log(alpha). The outputs of this model is mean ('mloga') and variance ('vloga)' of the posterior distribution of 'log(alpha)', where 'alpha' is reproductive rate estimated at S=0.
'GP(the S model)' contains functions used for running the semiparametric Bayesian model (un-conditional model) to estimate log(alpha). The outputs of this model is mean ('mloga') and variance ('vloga)' of the posterior distribution of 'log(alpha)', where 'alpha' is reproductive rate estimated at S=0.
'GP(GP(the lnS model))' contains functions used for running the semiparametric Bayesian model (un-conditional model) to estimate log(alpha) (developed in Munch et al. 2005). The outputs of this model is mean ('mloga') and variance ('vloga)' of the posterior distribution of 'log(alpha)', where 'alpha' is reproductive rate estimated at S=0.
'Ricker(integral)' contains functions used for estimating log(alpha) using the Ricker model. The outputs of this model is mean ('mloga') and variance ('vloga)' of the posterior distribution of 'log(alpha)', where 'alpha' is reproductive rate estimated at S=0.
'BH(integral)' contains functions used for estimating log(alpha) using the Beverton-Holt model. The outputs of this model is mean ('mloga') and variance ('vloga)' of the posterior distribution of 'log(alpha)', where 'alpha' is reproductive rate estimated at S=0.
</div
Predation and its Consequences: Insights into the Modeling of Interference
No full textStony Brook University Libraries. SBU Graduate School in ecology and evolution.
Lawrence Martin (Dean of Graduate School), Lev R. Ginzburg, Dissertation Advisor
Professor, Department of Ecology and Evolution, Stony Brook University, Jessica Gurevitch, Chairperson of Defense
Professor, Department of Ecology and Evolution, Stony Brook University, Stephan B. Munch
Assistant Professor, Marine Sciences Research Center, Stony Brook University, Leo S. Luckinbill
Associate Professor of Biological Sciences,
College of Liberal Arts and Sciences, Wayne State University
Appendix B. Prior specification and parameter estimation.
No full textPrior specification and parameter estimation
- …
