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      The compensatory potential of increased immigration following intensive American mink population control is diluted by male-biased dispersal

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          Abstract

          Attempts to mitigate the impact of invasive species on native ecosystems increasingly target large land masses where control, rather than eradication, is the management objective. Depressing numbers of invasive species to a level where their impact on native biodiversity is tolerable requires overcoming the impact of compensatory immigration from non-controlled portions of the landscape. Because of the expected scale-dependency of dispersal, the overall size of invasive species management areas relative to the dispersal ability of the controlled species will determine the size of any effectively conserved core area unaffected by immigration from surrounding areas. However, when dispersal is male-biased, as in many mammalian invasive carnivores, males may be overrepresented amongst immigrants, reducing the potential growth rate of invasive species populations in re-invaded areas. Using data collected from a project that gradually imposed spatially comprehensive control on invasive American mink ( Neovison vison) over a 10,000 km 2 area of NE Scotland, we show that mink captures were reduced to almost zero in 3 years, whilst there was a threefold increase in the proportion of male immigrants. Dispersal was often long distance and linking adjacent river catchments, asymptoting at 38 and 31 km for males and females respectively. Breeding and dispersal were spatially heterogeneous, with 40 % of river sections accounting for most captures of juvenile (85 %), adult female (65 %) and immigrant (57 %) mink. Concentrating control effort on such areas, so as to turn them into “attractive dispersal sinks” could make a disproportionate contribution to the management of recurrent re-invasion of mainland invasive species management areas.

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          COLONY: a program for parentage and sibship inference from multilocus genotype data.

          Pedigrees, depicting genealogical relationships between individuals, are important in several research areas. Molecular markers allow inference of pedigrees in wild species where relationship information is impossible to collect by observation. Marker data are analysed statistically using methods based on Mendelian inheritance rules. There are numerous computer programs available to conduct pedigree analysis, but most software is inflexible, both in terms of assumptions and data requirements. Most methods only accommodate monogamous diploid species using codominant markers without genotyping error. In addition, most commonly used methods use pairwise comparisons rather than a full-pedigree likelihood approach, which considers the likelihood of the entire pedigree structure and allows the simultaneous inference of parentage and sibship. Here, we describe colony, a computer program implementing full-pedigree likelihood methods to simultaneously infer sibship and parentage among individuals using multilocus genotype data. colony can be used for both diploid and haplodiploid species; it can use dominant and codominant markers, and can accommodate, and estimate, genotyping error at each locus. In addition, colony can carry out these inferences for both monoecious and dioecious species. The program is available as a Microsoft Windows version, which includes a graphical user interface, and a Macintosh version, which uses an R-based interface. © 2009 Blackwell Publishing Ltd.
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            Estimating Relatedness Using Genetic Markers

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              Using observation-level random effects to model overdispersion in count data in ecology and evolution

              Overdispersion is common in models of count data in ecology and evolutionary biology, and can occur due to missing covariates, non-independent (aggregated) data, or an excess frequency of zeroes (zero-inflation). Accounting for overdispersion in such models is vital, as failing to do so can lead to biased parameter estimates, and false conclusions regarding hypotheses of interest. Observation-level random effects (OLRE), where each data point receives a unique level of a random effect that models the extra-Poisson variation present in the data, are commonly employed to cope with overdispersion in count data. However studies investigating the efficacy of observation-level random effects as a means to deal with overdispersion are scarce. Here I use simulations to show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdispersion is simply ignored. Conversely, OLRE fail to reduce bias in zero-inflated data, and in some cases increase bias at high levels of overdispersion. There was a positive relationship between the magnitude of overdispersion and the degree of bias in parameter estimates. Critically, the simulations reveal that failing to account for overdispersion in mixed models can erroneously inflate measures of explained variance (r 2), which may lead to researchers overestimating the predictive power of variables of interest. This work suggests use of observation-level random effects provides a simple and robust means to account for overdispersion in count data, but also that their ability to minimise bias is not uniform across all types of overdispersion and must be applied judiciously.
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                Author and article information

                Contributors
                x.lambin@abdn.ac.uk
                Journal
                Biol Invasions
                Biol. Invasions
                Biological Invasions
                Springer International Publishing (Cham )
                1387-3547
                1573-1464
                1 July 2016
                1 July 2016
                2016
                : 18
                : 10
                : 3047-3061
                Affiliations
                [1 ]GRID grid.7107.1, ISNI 0000000419367291, School of Biological Sciences, , University of Aberdeen, ; Aberdeen, AB24 2TZ UK
                [2 ]GRID grid.413454.3, ISNI 0000000119580162, Mammal Research Institute, , Polish Academy of Science, ; 17-230 Białowieża, Poland
                [3 ]GRID grid.7080.f, Present Address: Centre de Recerca Ecològica i Aplicacions Forestals (CREAF), , Universitat Autònoma de Barcelona, ; Bellaterra, Barcelona, Spain
                Article
                1199
                10.1007/s10530-016-1199-x
                7175656
                32355453
                af246ea8-6ed0-4aa9-8fa9-79d431626ab8
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 15 December 2015
                : 21 June 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000270, Natural Environment Research Council;
                Award ID: NE/J01396X/1
                Award ID: NE/E006434/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000654, Marie Curie Cancer Care;
                Award ID: FP7-PEOPLE-2011-IEF 300288
                Award Recipient :
                Categories
                Original Paper
                Custom metadata
                © Springer International Publishing Switzerland 2016

                mink,control,compensation,immigration,dispersal,hotspots
                mink, control, compensation, immigration, dispersal, hotspots

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