768 research outputs found

    The importance of including habitat-specific behaviour in models of butterfly movement

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    Dispersal is a key process affecting population persistence and major factors affecting dispersal rates are the amounts, connectedness and properties of habitats in landscapes. We present new data on the butterfly Maniola jurtina in flower-rich and flower-poor habitats that demonstrates how movement and behaviour differ between sexes and habitat types, and how this effects consequent dispersal rates. Females had higher flight speeds than males but their total time in flight was four times less. The effect of habitat type was strong for both sexes, flight speeds were ~2.5x and ~1.7x faster on resource-poor habitats for males and females respectively, and flights were approximately 50% longer. With few exceptions females oviposited in the mown grass habitat, likely because growing grass offers better food for emerging caterpillars, but they foraged in the resource-rich habitat. It seems that females faced a trade-off between ovipositing without foraging in the mown grass or foraging without ovipositing where flowers were abundant. We show that taking account of habitat-dependent differences in activity, here categorised as flight or non-flight, is crucial to obtaining good fits of an individual-based model to observed movement. An important implication of this finding is that incorporating habitat-specific activity budgets is likely necessary for predicting longer-term dispersal in heterogeneous habitats as habitat-specific behaviour substantially influences the mean (>30% difference) and kurtosis (1.4x difference) of dispersal kernels. The presented IBMs provide a simple method to explicitly incorporate known activity and movement rates when predicting dispersal in changing and heterogeneous landscapes

    Dark Matter in Dwarf Spheroidals I: Models

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    This paper introduces a new two-parameter family of dwarf spheroidal (dSph) galaxy models. The density distribution has a Plummer profile and falls like the inverse fourth power of distance in projection, in agreement with the star-count data. The first free parameter controls the velocity anisotropy, the second controls the dark matter content. The dark matter distribution can be varied from one extreme of mass-follows-light through a near-isothermal halo with flat rotation curve to the other extreme of an extended dark halo with harmonic core. This family of models is explored analytically in some detail -- the distribution functions, the intrinsic moments and the projected moments are all calculated. For the nearby Galactic dSphs, samples of hundreds of discrete radial velocities are becoming available. A technique is developed to extract the anisotropy and dark matter content from such data sets by maximising the likelihood function of the sample of radial velocities. This is constructed from the distribution function and corrected for observational errors and the effects of binaries. Tests on simulated data sets show that samples of 1000 discrete radial velocities are ample to break the degeneracy between mass and anisotropy in the nearby dSphs. Interesting constraints can already be placed on the distribution of the dark matter with samples of 160 radial velocities (the size of the present-day data set for Draco).Comment: 16 pages, version in press at MNRA

    Data on the movement behaviour of four species of grassland butterfly

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    This Data in Brief article describes data on the movement behaviour of four species of grassland butterflies collected over three years and at four sites in southern England. The datasets consist of the movement tracks of and recorded using standard methods and presented as steps distances and turning angles. Sites consisted of nectar-rich field margins, meadows, and mown short turf grasslands with minimal flowers. In total, 783 unique movement tracks were collected. The data were used for analysing the movement behaviour of the species and for parameterising individual-based movement models

    Integrating the influence of weather into mechanistic models of butterfly movement

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    Understanding the factors influencing movement is essential to forecasting species persistence in a changing environment. Movement is often studied using mechanistic models, extrapolating short-term observations of individuals to longer-term predictions, but the role of weather variables such as air temperature and solar radiation, key determinants of ectotherm activity, are generally neglected. We aim to show how the effects of weather can be incorporated into individual-based models of butterfly movement thus allowing analysis of their effects. Methods: We constructed a mechanistic movement model and calibrated it with high precision movement data on a widely studied species of butterfly, the meadow brown (Maniola jurtina), collected over a 21-week period at four sites in southern England. Day time temperatures during the study ranged from 14.5 to 31.5⁰C and solar radiation from heavy cloud to bright sunshine. The effects of weather are integrated into the individual-based model through weather-dependent scaling of parametric distributions representing key behaviours: the durations of flight and periods of inactivity. Results: Flight speed was unaffected by weather, time between successive flights increased as solar radiation decreased, and flight duration showed a unimodal response to air temperature that peaked between approximately 23⁰C and 26⁰C. After validation, the model demonstrated that weather alone can produce a more than two-fold difference in predicted weekly displacement. Conclusions: Individual Based models provide a useful framework for integrating the effect of weather into movement models. By including weather effects we are able to explain a two-fold difference in movement rate of M. jurtina consistent with inter-annual variation in dispersal measured in population studies. Climate change for the studied populations is expected to decrease activity and dispersal rates since these butterflies already operate close to their thermal optimum

    Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks

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    Fremont cottonwood (Populus fremonti) is a foundation riparian tree species that drives community structure and ecosystem processes in southwestern U.S. ecosystems. Despite its ecological importance, little is known about the ecological and environmental processes that shape its genetic diversity, structure, and landscape connectivity. Here, we combined molecular analyses of 82 populations including 1312 individual trees dispersed over the species’ geographical distribution. We reduced the data set to 40 populations and 743 individuals to eliminate admixture with a sibling species, and used multivariate restricted optimization and reciprocal causal modeling to evaluate the effects of river network connectivity and climatic gradients on gene flow. Our results confirmed the following: First, gene flow of Fremont cottonwood is jointly controlled by the connectivity of the river network and gradients of seasonal precipitation. Second, gene flow is facilitated by mid-sized to large rivers, and is resisted by small streams and terrestrial uplands, with resistance to gene flow decreasing with river size. Third, genetic differentiation increases with cumulative differences in winter and spring precipitation. Our results suggest that ongoing fragmentation of riparian habitats will lead to a loss of landscape-level genetic connectivity, leading to increased inbreeding and the concomitant loss of genetic diversity in a foundation species. These genetic effects will cascade to a much larger community of organisms, some of which are threatened and endangered

    Fractal dimension and degree of order in sequential deposition of mixture

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    We present a number models describing the sequential deposition of a mixture of particles whose size distribution is determined by the power-law p(x)∌αxα−1p(x) \sim \alpha x^{\alpha-1}, x≀lx\leq l . We explicitly obtain the scaling function in the case of random sequential adsorption (RSA) and show that the pattern created in the long time limit becomes scale invariant. This pattern can be described by an unique exponent, the fractal dimension. In addition, we introduce an external tuning parameter beta to describe the correlated sequential deposition of a mixture of particles where the degree of correlation is determined by beta, while beta=0 corresponds to random sequential deposition of mixture. We show that the fractal dimension of the resulting pattern increases as beta increases and reaches a constant non-zero value in the limit ÎČ→∞\beta \to \infty when the pattern becomes perfectly ordered or non-random fractals.Comment: 16 pages Latex, Submitted to Phys. Rev.

    Management Effects on Greenhouse Gas Dynamics in Fen Ditches

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    Globally, large areas of peatland have been drained through the digging of ditches, generally to increase agricultural production. By lowering the water table it is often assumed that drainage reduces landscape-scale emissions of methane (CH4) into the atmosphere to negligible levels. However, drainage ditches themselves are known to be sources of CH4 and other greenhouse gases (GHGs), but emissions data are scarce, particularly for carbon dioxide (CO2) and nitrous oxide (N2O), and show high spatial and temporal variability. Here, we report dissolved GHGs and diffusive fluxes of CH4 and CO2 from ditches at three UK lowland fens under different management; semi-natural fen, cropland, and cropland restored to low-intensity grassland. Ditches at all three fens emitted GHGs to the atmosphere, but both fluxes and dissolved GHGs showed extensive variation both seasonally and within-site. CH4 fluxes were particularly large, with medians peaking at all three sites in August at 120-230 mg m-2 d-1. Significant between site differences were detected between the cropland and the other two sites for CO2 flux and all three dissolved GHGs, suggested that intensive agriculture has major effects on ditch biogeochemistry. Multiple regression models using environmental and water chemistry data were able to explain 29-59% of observed variation in dissolved GHGs. Annual CH4 fluxes from the ditches were 37.8, 18.3 and 27.2 g CH4 m-2 yr-1 for the semi-natural, grassland and cropland, and annual CO2 fluxes were similar (1100 to 1440 g CO2 m-2 yr-1) among sites. We suggest that fen ditches are important contributors to landscape-scale GHG emissions, particularly for CH4. Ditch emissions should be included in GHG budgets of human modified fens, particularly where drainage has removed the original terrestrial CH4 source, e.g. agricultural peatlands

    The Mid-infrared Evolution of the FU Orionis Disk

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    We present new SOFIA-FORCAST observations obtained in 2016 February of the archetypal outbursting low-mass young stellar object FU Orionis, and we compare the continuum, solid-state, and gas properties with mid-infrared data obtained at the same wavelengths in 2004 with Spitzer-IRS. In this study, we conduct the first mid-infrared spectroscopic comparison of an FUor over a long time period. Over a 12-year period, UBVR monitoring indicates that FU Orionis has continued its steady decrease in overall brightness by ~14%. We find that this decrease in luminosity occurs only at wavelengths ≟20 ÎŒm. In particular, the continuum shortward of the silicate emission complex at 10 ÎŒm exhibits a ~12% (~3σ) drop in flux density but no apparent change in slope; both the Spitzer and SOFIA spectra are consistent with a 7200 K blackbody. Additionally, the detection of water absorption is consistent with the Spitzer spectrum. The silicate emission feature at 10 ÎŒm continues to be consistent with unprocessed grains, unchanged over 12 years. We conclude that either the accretion rate in FU Orionis has decreased by ~12–14% over this time baseline or the inner disk has cooled, but the accretion disk remains in a superheated state outside the innermost region
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