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  1. Abstract

    Adelges tsugaeAnnand, the hemlock woolly adelgid, is an invasive insect pest ofTsuga canadensis(L.) Carriere, eastern hemlock, in the eastern United States. AnA. tsugaeinfestation often results in the death ofT. canadensiswithin years and has caused significant changes to hemlock forests. Cycles inT. canadensishealth andA. tsugaedensities seem to be an important feature inA. tsugaepopulation dynamics in the eastern United States. To investigate mechanisms leading to such cycles, we construct a model composed of systems of ordinary differential equations with time‐dependent parameters to represent seasonality. The model captures the coupled cycles inT. canadensishealth andA. tsugaedensity. We use field data from Virginia to develop the model and to perform parameter estimation. The mechanisms we represent in the model include anA. tsugaedensity‐dependentT. canadensisgrowth rate, aT. canadensishealth‐dependentA. tsugaemortality rate, and a density‐dependentA. tsugaemortality rate, which produce cycles inT. canadensishealth andA. tsugaedensity commonly seen with theA. tsugaesystem in the eastern United States. We test sets of initial conditions to determine the scenarios that will likely lead toT. canadensismortality and explore longer term dynamics of the system. In general, low and highT. canadensishealth initial condition values result in likelyT. canadensismortality whileT. canadensiswith medium health initial condition values are predicted to survive.

     
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  2. Abstract

    Metapopulation models include spatial population dynamics such as dispersion and migration between subpopulations. Integral projection models (IPMs) can include demographic rates as a function of size. Traditionally, metapopulation models do not included detailed populaiton models such as IPMs. In some situations, both local population dynamics (e.g. size‐based survival) and spatial dynamics are important.

    We present a Python package,MetaIPM, which places IPMs into a metapopulation framework, and allow users to readily construct and apply these models that combine local population dynamics within a metapopulation framework.

    MetaIPMincludes an IPM for each subpopulation that is connected to other subpopulations via a metapopulation movement model. These movements can include dispersion, migration or other patterns. The IPM can include for size‐specific demographic rates (e.g. survival, recruitment) as well as management actions, such as length‐based harvest (e.g. gear specific capture sizes, varying slot limits across political boundaries). The model also allows for changes in metapopulation connectivity between locations, such as a fish passage ladders to enhance movement or deterrents to reduce movement. Thus, resource managers can useMetaIPMto compare different management actions such as the harvest gear type (which can be length‐specific) and harvest locations.

    We demonstrate howMetaIPMmay be applied to inform managers seeking to limit the spread of an invasive species in a system with important metapopulation dynamics. Specifically, we compared removal lengths (all length fish versus longer fish only) for an invasive fish population in a fragmented, inland river system.MetaIPMallowed users to compare the importance of harvesting source populations away from the invasion front, as well as species at the invasion front. The model would also allow for future comparisons of different deterrent placement locations in the system.

    Moving beyond our example system, we describe howMetaIPMcan be applied to other species, systems and management approaches. TheMetaIPMpackages includes Jupyter Notebooks documenting the package as well as a second set of JupyterNotebooks showing the application of the package to our example system.

     
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  3. null (Ed.)