Integration of software tools for integrative modeling of biomolecular systems

Integrative modeling computes a model based on varied types of input information, be it from experiments or prior models. Often, a type of input information will be best handled by a specific modeling software package. In such a case, we desire to integrate our integrative modeling software package, Integrative Modeling Platform (IMP), with software specialized to the computational demands of the modeling problem at hand. After several attempts, however, we have concluded that even in collaboration with the software’s developers, integration is either impractical or impossible. The reasons for the intractability of integration include software incompatibilities, differing modeling logic, the costs of collaboration, and academic incentives. In the integrative modeling software ecosystem, several large modeling packages exist with often redundant tools. We reason, therefore, that the other development groups have similarly concluded that the benefit of integration does not justify the cost. As a result, modelers are often restricted to the set of tools within a single software package. The inability to integrate tools from distinct software negatively impacts the quality of the models and the efficiency of the modeling. As the complexity of modeling problems grows, we seek to galvanize developers and modelers to consider the long-term benefit that software interoperability yields. In this article, we formulate a demonstrative set of software standards for implementing a model search using tools from independent software packages and discuss our efforts to integrate IMP and the crystallography suite Phenix within the Bayesian modeling framework.     

Identifying keystone trophic groups in benthic ecosystems: Implications for fisheries management

Many species inhabiting the benthic marine ecosystems of the central and northern Chilean coast have been intensively harvested and this exploitation has increased considerably in recent years. Despite this harvest pressure, few studies have attempted to establish a more holistic, systems-based management plan. On the contrary, research continues to rely on population models in which the species of interest are isolated from their ecological context. This work offers several keystone indices in order to help multispecies fisheries management. The indices used are: (1) functional indices based on steady-state and dynamic trophic models; (2) structural indices based on bottom-up and top-down control mechanisms; and (3) qualitative keystone species indices using loop models (mixed control). The quantitative trophic models were constructed using Ecopath with Ecosim (EwE; v. 5.0) software, and the qualitative model was analysed using Loop Analysis. All models describe the interactions of the most representative species and functional groups inhabiting the benthic ecosystems of Tongoy Bay, La Rinconada Marine Reserve (Antofagasta Bay), and the kelp forest of Mejillones Peninsula (Antofagasta). Even though our results only represent the short-term dynamics of these systems, we have found keystoneness properties of several species and functional groups, including primary producers, herbivores, and top predators. Despite this wide variability of groups, we detected a different core set of species or functional groups, each of which contained prey–predator and plant–herbivore relationships. Because the traditional keystone concept of a single species is difficult to apply, we suggest shifting away from this view towards a more holistic alternative such as that of a keystone species complex. This kind of approach would facilitate the design and assessment of sustainable management strategies for ecological marine ecosystems. Despite the ecological relevance of our results, further experimental studies and modelling using other theoretical frameworks should be performed.     

Moment closure approximations for stochastic kinetic models with rational rate laws

Stochastic models are often used when modelling chemical species that have low numbers of molecules. However, as these models become large, it can become computationally expensive to simulate even a single realisation of the system since even efficient simulation techniques have a high computational cost. One possible technique to approximate the stochastic system is moment closure. The moment closure approximation is used to provide analytic approximations to non-linear stochastic models. Until now, this approximation has only been applied to models with polynomial rate laws. In this paper we extend the moment closure method to cover models with rational rate laws.     

McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems

Individual-based modeling is widely applied to investigate the ecological mechanisms driving microbial community dynamics. In such models, the population or community dynamics emerge from the behavior and interplay of individual entities, which are simulated according to a predefined set of rules. If the rules that govern the behavior of individuals are based on generic and mechanistically sound principles, the models are referred to as next-generation individual-based models. These models perform particularly well in recapitulating actual ecological dynamics. However, implementation of such models is time-consuming and requires proficiency in programming or in using specific software, which likely hinders a broader application of this powerful method. Here we present McComedy, a modeling tool designed to facilitate the development of next-generation individual-based models of microbial consumer-resource systems. This tool allows flexibly combining pre-implemented building blocks that represent physical and biological processes. The ability of McComedy to capture the essential dynamics of microbial consumer-resource systems is demonstrated by reproducing and furthermore adding to the results of two distinct studies from the literature. With this article, we provide a versatile tool for developing next-generation individual-based models that can foster understanding of microbial ecology in both research and education.     

Testing prediction accuracy in short-term ecological studies

Applied ecology is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via k-fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored small mammal abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset (n = 28 sampling locations across 125 km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.     

Quantifying keystone species complexes: Ecosystem-based conservation management in the King George Island (Antarctic Peninsula)

A keystone species complex (KSC) is a small set of interacting species that play an outstandingly important role in community organization. Two KSC indices are suggested and have been calculated in the coastal benthic/pelagic ecosystem of Fildes Bay, King George Island (Antarctica). These indices of keystoneness emerge after considering: (1) functional indices based on steady-state and dynamic quantitative trophic models (using bottom-up, mixed and top-down control flow mechanisms); (2) structural indices including bottom-up and top-down control mechanisms, (3) semi-quantitative (qualitative) keystone indices using loop analysis (under mixed control); and (4) topological key player indices based on the centrality of node sets in the network. The models constructed and analyzed describe the interactions of the most abundant species and functional groups inhabiting the coastal ecological systems of Fildes Bay. Although our results only represent the transient dynamics of these ecological systems, the KSC indices identified the following trophically connected common core of components: the functional groups of Seastars (top-predators), the herbivorous sea urchin species Sterechinus neumayeri and the Phytoplankton (primary producers). The KSC indices for Fildes Bay could facilitate the design and assessment of conservation monitoring, especially when the Antarctic ecosystems are being severely stressed by the direct effects of global warming and UV radiation. A more holistic view of conservation remains difficult because the traditional view is based principally on single species. This imposes an even greater challenge, for global changes accompany the network of interacting species, co-varying with the variables of the natural system.     

Taxonomy and ecological niche modeling: Implications for the conservation of wood partridges (genus Dendrortyx)

Ecological niche models (ENMs) have a wide range of biological applications, particularly in conservation. To build these models, two sources of information are needed: occurrence records for the species of interest and environmental variables. However, taxonomic limits are often unclear, and the selection of occurrence data depends on the species concept being used. In this study we generated ENMs based on different taxonomic levels within the Dendrortyx group, which is comprised of three species and several subspecies; we analyzed the geographic and ecological distribution patterns and discuss the implications for the biogeography and conservation of this group. Our results suggest that the area with suitable climate depends on the taxonomic category used in the model, which in turn affects the interpretation of the importance of different biogeographic barriers and introduces variation into the potential differentiation of Dendrortyx. In terms of conservation, Dendrortyx macroura and Dendrortyx leucophrys are in a low risk category, that of “least concern,” although they may be amended to a higher category when their allopatric lineages are considered as the units for modeling. We suggest carrying out an a priori taxonomic analysis to facilitate the empirical identification of the units to be modeled in order to allow for a better ecological and biogeographic interpretation and more sound conservation policies.     

Revisiting the Role of Individual Variability in Population Persistence and Stability

Populations often exhibit a pronounced degree of individual variability and this can be important when constructing ecological models. In this paper, we revisit the role of inter-individual variability in population persistence and stability under predation pressure. As a case study, we consider interactions between a structured population of zooplankton grazers and their predators. Unlike previous structured population models, which only consider variability of individuals according to the age or body size, we focus on physiological and behavioural structuring. We first experimentally demonstrate a high degree of variation of individual consumption rates in three dominant species of herbivorous copepods (Calanus finmarchicus, Calanus glacialis, Calanus euxinus) and show that this disparity implies a pronounced variation in the consumption capacities of individuals. Then we construct a parsimonious predator-prey model which takes into account the intra-population variability of prey individuals according to behavioural traits: effectively, each organism has a ‘personality’ of its own. Our modelling results show that structuring of prey according to their growth rate and vulnerability to predation can dampen predator-prey cycles and enhance persistence of a species, even if the resource stock for prey is unlimited. The main mechanism of efficient top-down regulation is shown to work by letting the prey population become dominated by less vulnerable individuals when predator densities are high, while the trait distribution recovers when the predator densities are low.     

Science‐Driven Restoration: A Square Grid on a Round Earth?

Is formal science necessarily an effective framework and methodology for designing and implementing ecological restoration programs? My experience as an ecologist in Hawaii suggests that even when scientific research programs are explicitly designed to guide and facilitate restoration, the culture of science, heterogeneity of nature, and real‐world complexities of implementing land management practices often limit the practical relevance of conventional scientific research. Although alternative models such as adaptive management and transdisciplinary science may facilitate research that more robustly models the real world, there is often little professional support or incentive to orient even these nonconventional research approaches toward actually solving on‐the‐ground problems. Thus, if one’s goal is to accomplish ecological restoration as quickly and efficiently as possible, a trial‐and‐error/intelligent tinkering–type approach might often be better than using more rigorous, data‐intensive scientific methodology. However, the sympatric implementation of ecological restoration and scientific research programs can lead to valuable synergies such as mutual logistical and financial support and the exchange of distinct forms of knowledge. The professional activities and mere presence of scientists can also greatly enhance a program’s prestige and visibility, which in turn can indirectly promote more and better ecological restoration. Improving our understanding of when formal science can directly assist restoration projects and when its value will more likely be synergistic and indirect could lead to better science, better ecological restoration, and better relationships between these two cultures.     

Ecophenotypic plasticity leads to extraordinary gastropod shells found on the “Roof of the World”

The often extraordinary shell forms and shapes of gastropods found in palaeolakes, such as the highly diverse Gyraulus fauna of the famous Steinheim Basin, have been puzzling evolutionary biologists for centuries, and there is an ongoing debate whether these aberrant shell forms are indicative of true species (or subspecies) or ecophenotypic morphs. Interestingly, one of the Steinheim Gyraulus morphs – a corkscrew-like open-coiled shell – has a recent analogue in the Lake Bangong drainage system on the western Tibetan Plateau. Therefore, a combination of morphological, molecular, palaeolimnological, and ecological analyses was used in this study to assess whether the extraordinary shell shape in Gyraulus sp. from this drainage system represents a (young) ecophenotypic phenomenon or if it has been genetically fixed over an extended period of time. Our morphological, ecological, and palaeolimnological data suggest that the corkscrew-like specimens remain restricted to a small pond near Lake Bangong with an elevated pH value and that the colonization may have occurred recently. The phylogenetic reconstruction based on two gene fragments shows that these nonplanispiral specimens cluster within the previous described Tibetan Plateau Gyraulus clade N2. A network analysis indicates that some haplotypes are even shared by planispiral and nonplanispiral specimens. Given the ephemerality of the phenomenon, the compact network patterns inferred, the likely young phylogenetic age of the aberrant Gyraulus shells studied, and the ecological peculiarities of the study site, we suggest that the evolution of the aberrant shell forms on the Tibetan Plateau could likely be considered as a rapid ecophenotypic response, possibly induced by ecological stress. This finding may thus have implications for the ongoing debate about the processes that have caused the extraordinary shell diversity in palaeolakes such as the Steinheim Basin.     

Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish

We propose a new graphical approach to the analysis of multi-temporal morphological and ecological data concerning the life history of fish, which can typically serves models in ecomorphological investigations because they often undergo significant ontogenetic changes. These changes can be very complex and difficult to describe, so that visualization, abstraction and interpretation of the underlying relationships are often impeded. Therefore, classic ecomorphological analyses of covariation between morphology and ecology, performed by means of multivariate techniques, may result in non-exhaustive models. The Self Organizing map (SOM) is a new, effective approach for pursuing this aim. In this paper, lateral outlines of larval stages of gilthead sea bream (Sparus aurata) and dusky grouper (Epinephelus marginatus) were recorded and broken down using by means of Elliptic Fourier Analysis (EFA). Gut contents of the same specimens were also collected and analyzed. Then, shape and trophic habits data were examined by SOM, which allows both a powerful visualization of shape changes and an easy comparison with trophic habit data, via their superimposition onto the trained SOM. Thus, the SOM provides a direct visual approach for matching morphological and ecological changes during fish ontogenesis. This method could be used as a tool to extract and investigate relationships between shape and other sinecological or environmental variables, which cannot be taken into account simultaneously using conventional statistical methods.     

Assessment of appropriate species-specific time intervals to integrate GPS telemetry data in ecological niche models

Ecological niche models use presence-only data, which is often affected by lack of true absences leading to sampling bias. Over the last decade, there has been an uptick in the integration of occurrence data from global positioning systems telemetry data in ecological niche models and/or species distribution models. These data types can be affected by serial autocorrelation at high relocation frequencies yet have been used in ecological niche models using geographic filters and subsampling techniques. Yet, no study to date has attempted to discern a method to identify the appropriate time interval for a particular species if integrating GPS telemetry occurrence data in a MaxEnt framework. We demonstrate a rigorous spatial technique using a robust contemporary dataset from ocelots (Leopardus pardalis) to assess the appropriate time intervals to use in a species-specific ecological niche model. We assessed a range of daily time intervals (every 0.5, 1–4, 6, 8, and 12 h) commonly used in teresstrial mammalian carnivore studies. We observed the predictive performance of shorter time intervals every 2 h was comparable to much longer intervals every 12 h. These shorter intervals under/overestimated the least amount of data compared to 12 h. This study demonstrates that by accounting for serial autocorrelation and conducting rigorous spatial analyses, scientists can identify the appropriate time interval to integrate GPS telemetry data use in ecological niche models in MaxEnt. These results can also be transferable across highly mobile terrestrial taxa at different spatial scales, which can help inform species management or conservation strategies.     

There Is No Need To Be Normal: Generalized Linear Models of Natural Variation

Both ecological field studies and attempts to extrapolate from laboratory experiments to natural populations generally encounter the high degree of natural variability and chaotic behavior that typify natural ecosystems. Regardless of this variability and non-normal distribution, most statistical models of natural systems use normal error which assumes independence between the variance and mean. However, environmental data are often random or clustered and are better described by probability distributions which have more realistic variance to mean relationships. Until recently statistical software packages modeled only with normal error and researchers had to assume approximate normality on the original or transformed scale of measurement and had to live with the consequences of often incorrectly assuming independence between the variance and mean. Recent developments in statistical software allow researchers to use generalized linear models (GLMs) and analysis can now proceed with probability distributions from the exponential family which more realistically describe natural conditions: binomial (even distribution with variance less than mean), Poisson (random distribution with variance equal mean), negative binomial (clustered distribution with variance greater than mean). GLMs fit parameters on the original scale of measurement and eliminate the need for obfuscating transformations, reduce bias for proportions with unequal sample size, and provide realistic estimates of variance which can increase power of tests. Because GLMs permit modeling according to the non-normal behavior of natural systems and obviate the need for normality assumptions, they will likely become a widely used tool for analyzing toxicity data. To demonstrate the broad-scale utility of GLMs, we present several examples where the use of GLMs improved the statistical power of field and laboratory studies to document the rapid ecological recovery of Prince William Sound following the Exxon Valdez oil spill.     

SEALNET: Facial recognition software for ecological studies of harbor seals

Methods for long‐term monitoring of coastal species such as harbor seals (Phoca vitulina) are often costly, time‐consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for identification of individual seals and demonstrate its utility in ecological and population studies. We created a software package, SealNet, that automates photo identification of seals, using a graphical user interface (GUI) software to detect, align, and chip seal faces from photographs and a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal) to classify individual seals. We piloted the SealNet technology with a population of harbor seals located within Casco Bay on the coast of Maine, USA. Across two years of sampling, 2019 and 2020, at seven haul‐out sites in Middle Bay, we obtained a dataset optimized for the development and testing of SealNet. We processed 1752 images representing 408 individual seals and achieved 88% Rank‐1 and 96% Rank‐5 accuracy in closed set seal identification. In identifying individual seals, SealNet software outperformed a similar face recognition method, PrimNet, developed for primates but retrained on seals. The ease and wealth of image data that can be processed using SealNet software contributes a vital tool for ecological and behavioral studies of marine mammals in the developing field of conservation technology.

In this paper, we describe the successful application of our newly developed automated facial recognition software as a tool for ecological analysis of harbor seals (Phoca vitulina). We outline an emerging method of data collection and analysis that facilitates rapid interpretation of large photo datasets over wide temporal and geographic scales. In addition, we use this machine learning‐based technology in a preliminary ecological study in a wild population of seals in the Casco Bay region of Maine to demonstrate the effectiveness of this non‐invasive method for use in mark‐recapture and site fidelity studies in the field.     

Modeling Disease Vector Occurrence When Detection Is Imperfect II: Drivers of Site-Occupancy by Synanthropic Triatoma brasiliensis in the Brazilian Northeast

Background

Understanding the drivers of habitat selection by insect disease vectors is instrumental to the design and operation of rational control-surveillance systems. One pervasive yet often overlooked drawback of vector studies is that detection failures result in some sites being misclassified as uninfested; naïve infestation indices are therefore biased, and this can confound our view of vector habitat preferences. Here, we present an initial attempt at applying methods that explicitly account for imperfect detection to investigate the ecology of Chagas disease vectors in man-made environments.

Methodology

We combined triplicate-sampling of individual ecotopes (n = 203) and site-occupancy models (SOMs) to test a suite of pre-specified hypotheses about habitat selection by Triatoma brasiliensis. SOM results were compared with those of standard generalized linear models (GLMs) that assume perfect detection even with single bug-searches.

Principal Findings

Triatoma brasiliensis was strongly associated with key hosts (native rodents, goats/sheep and, to a lesser extent, fowl) in peridomestic environments; ecotope structure had, in comparison, small to negligible effects, although wooden ecotopes were slightly preferred. We found evidence of dwelling-level aggregation of infestation foci; when there was one such focus, same-dwelling ecotopes, whether houses or peridomestic structures, were more likely to become infested too. GLMs yielded negatively-biased covariate effect estimates and standard errors; both were, on average, about four times smaller than those derived from SOMs.

Conclusions/Significance

Our results confirm substantial population-level ecological heterogeneity in T. brasiliensis. They also suggest that, at least in some sites, control of this species may benefit from peridomestic rodent control and changes in goat/sheep husbandry practices. Finally, our comparative analyses highlight the importance of accounting for the various sources of uncertainty inherent to vector studies, including imperfect detection. We anticipate that future research on infectious disease ecology will increasingly rely on approaches akin to those described here.     

Using custom scientific workflow software and GIS to inform protected area climate adaptation planning in the Greater Yellowstone Ecosystem

Anticipating the ecological effects of climate change to inform natural resource climate adaptation planning represents one of the primary challenges of contemporary conservation science. Species distribution models have become a widely used tool to generate first-pass estimates of climate change impacts to species probabilities of occurrence. There are a number of technical challenges to constructing species distribution models that can be alleviated by the use of scientific workflow software. These challenges include data integration, visualization of modeled predictor–response relationships, and ensuring that models are reproducible and transferable in an adaptive natural resource management framework. We used freely available software called VisTrails Software for Assisted Habitat Modeling (VisTrails:SAHM) along with a novel ecohydrological predictor dataset and the latest Coupled Model Intercomparison Project 5 future climate projections to construct species distribution models for eight forest and shrub species in the Greater Yellowstone Ecosystem in the Northern Rocky Mountains USA. The species considered included multiple species of sagebrush and juniper, Pinus flexilis, Pinus contorta, Pseudotsuga menziesii, Populus tremuloides, Abies lasciocarpa, Picea engelmannii, and Pinus albicaulis. Current and future species probabilities of occurrence were mapped in a GIS by land ownership category to assess the feasibility of undertaking present and future management action. Results suggested that decreasing spring snowpack and increasing late-season soil moisture deficit will lead to deteriorating habitat area for mountain forest species and expansion of habitat area for sagebrush and juniper communities. Results were consistent across nine global climate models and two representative concentration pathway scenarios. For most forest species their projected future distributions moved up in elevation from general federal to federally restricted lands where active management is currently prohibited by agency policy. Though not yet fully mature, custom scientific workflow software shows considerable promise to ease many of the technical challenges inherent in modeling the potential ecological impacts of climate change to support climate adaptation planning.     

Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts

Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m – 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for supporting landscape-scale decision-making as relevant data and open source, user-friendly, and peer-reviewed software become widely available.     

Aposematism and crypsis are not enough to explain dorsal polymorphism in the Iberian adder

Aposematic organisms can show phenotypic variability across their distributional ranges. The ecological advantages of this variability have been scarcely studied in vipers. We explored this issue in Vipera seoanei, a species that exhibits five geographically structured dorsal colour phenotypes across Northern Iberia: two zigzag patterned (Classic and Cantabrica), one dorsal-strip patterned (Bilineata), one even grey (Uniform), and one melanistic (Melanistic). We compared predation rates (raptors and mammals) on plasticine models resembling each colour phenotype in three localities. Visual modelling techniques were used to infer detectability (i.e. conspicuousness) of each model type for visually guided predators (i.e. diurnal raptors). We hypothesize that predation rates will be lower for the two zigzag models (aposematism hypothesis) and that models with higher detectability would show higher predation rates (detectability hypothesis). Classic and Bilineata models were the most conspicuous, while Cantabrica and Uniform were the less. Melanistic presented an intermediate conspicuousness. Predation rate was low (3.24% of models) although there was variation in attack frequency among models. Zigzag models were scarcely predated supporting the aposematic role of the zigzag pattern in European vipers to reduce predation (aposematism hypothesis). From the non-zigzag models, high predation occurred on Bilineata and Melanistic models, and low on Uniform models, partially supporting our detectability hypothesis. These results suggest particular evolutionary advantages for non-zigzag phenotypes such as better performance of Melanistic phenotypes in cold environments or better crypsis of Uniform phenotypes. Polymorphism in V. seoanei may respond to a complex number of forces acting differentially across an environmental gradient.     

Modeling future range expansion and management strategies for an invasive squirrel species

Successful management of an invasive species requires in depth knowledge of the invader, the invaded ecosystem, and their interactions. The complexity of the species-system interactions can be reduced and represented in ecological models for better comprehension. In this study, a spatially explicit population model was created using the RAMAS software package to simulate the past and future invasion dynamics of the eastern grey squirrel (Sciurus carolinensis) in the fragmented habitat in case study areas in Ireland. This invasive squirrel species causes economic damage by bark stripping forest crops and is associated with the decline of its native congener (S. vulgaris). Three combinations of demographic and dispersal parameters, which best matched the distribution of the species shortly after introduction, were used to simulate invasion dynamics. Future population expansion was modeled under scenarios of no control and two different management strategies: fatal culls and immunocontraceptive vaccination programmes. In the absence of control, the grey squirrel range is predicted to expand to the south and southwest of Ireland endangering internationally important habitats, vulnerable forest crops, and the native red squirrel. The model revealed that region-wide intensive and coordinated culls would have the greatest impact on grey squirrel populations. Control strategies consisting solely of immunocontraceptive vaccines, often preferred by public interest groups, are predicted to be less effective. Complete eradication of the grey squirrel from Ireland is not economically feasible and strategic evidence-based management is required to limit further range expansion. Ecological models can be used to choose between informed management strategies based on predicted outcomes.