Breeding latitude predicts timing but not rate of spring migration in a widespread migratory bird in South America

Identifying the processes that determine avian migratory strategies in different environmental contexts is imperative to understanding the constraints to survival and reproduction faced by migratory birds across the planet. We compared the spring migration strategies of Fork‐tailed Flycatchers (Tyrannus s. savana) that breed at south‐temperate latitudes (i.e., austral migrants) vs. tropical latitudes (i.e., intratropical migrants) in South America. We hypothesized that austral migrant flycatchers are more time‐selected than intratropical migrants during spring migration. As such, we predicted that austral migrants, which migrate further than intratropical migrants, will migrate at a faster rate and that the rate of migration for austral migrants will be positively correlated with the onset of spring migration. We attached light‐level geolocators to Fork‐tailed Flycatchers at two tropical breeding sites in Brazil and at two south‐temperate breeding sites in Argentina and tracked their movements until the following breeding season. Of 286 geolocators that were deployed, 37 were recovered ~1 year later, of which 28 provided useable data. Rate of spring migration did not differ significantly between the two groups, and only at one site was there a significantly positive relationship between date of initiation of spring migration and arrival date. This represents the first comparison of individual migratory strategies among conspecific passerines breeding at tropical vs. temperate latitudes and suggests that austral migrant Fork‐tailed Flycatchers in South America are not more time‐selected on spring migration than intratropical migrant conspecifics. Low sample sizes could have diminished our power to detect differences (e.g., between sexes), such that further research into the mechanisms underpinning migratory strategies in this poorly understood system is necessary.Fil: Jahn, Alex. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Cereghetti, Joaquín. Universidad Nacional de La Pampa; ArgentinaFil: Cueto, Víctor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagóica. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ciencias Naturales - Sede Esquel. Centro de Investigación Esquel de Montaña y Estepa Patagónica; ArgentinaFil: Hallworth, Michael T.. Smithsonian Conservation Biology Institute; Estados UnidosFil: Levey, Douglas J.. National Science Foundation; Estados UnidosFil: Marini, Miguel Â.. Universidade do Brasília; BrasilFil: Masson, Diego. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; ArgentinaFil: Pizo, Marco A.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Sarasola, José Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; ArgentinaFil: Tuero, Diego Tomas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin

Spatially explicit species distribution models: A missed opportunity in conservation planning?

Aim: Systematic conservation planning is vital for allocating protected areas given the spatial distribution of conservation features, such as species. Due to incomplete species inventories, species distribution models (SDMs) are often used for predicting species habitat suitability and species probability of occurrence. Currently, SDMs mostly ignore spatial dependencies in species and predictor data. Here, we provide a comparative evaluation of how accounting for spatial dependencies, that is, autocorrelation, affects the delineation of optimized protected areas. Location: Southeast Australia, Southeast U.S. Continental Shelf, Danube River Basin. Methods: We employ Bayesian spatially explicit and non-spatial SDMs for terrestrial, marine and freshwater species, using realm-specific planning unit shapes (grid, hexagon and subcatchment, respectively). We then apply the software gurobi to optimize conservation plans based on species targets derived from spatial and non-spatial SDMs (10% 50% each to analyse sensitivity), and compare the delineation of the plans. Results: Across realms and irrespective of the planning unit shape, spatially explicit SDMs (a) produce on average more accurate predictions in terms of AUC, TSS, sensitivity and specificity, along with a higher species detection probability. All spatial optimizations meet the species conservation targets. Spatial conservation plans that use predictions from spatially explicit SDMs (b) are spatially substantially different compared to those that use non-spatial SDM predictions, but (c) encompass a similar amount of planning units. The overlap in the selection of planning units is smallest for conservation plans based on the lowest targets and vice versa. Main conclusions: Species distribution models are core tools in conservation planning. Not surprisingly, accounting for the spatial characteristics in SDMs has drastic impacts on the delineation of optimized conservation plans. We therefore encourage practitioners to consider spatial dependencies in conservation features to improve the spatial representation of future protected areas. © 2019 The Authors. Diversity and Distributions Published by John Wiley and Sons LtdThis study was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 642317. SDL has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie grant agreement No. 748625, and SCJ from the German Federal Ministry of Education and Research (BMBF) for the “GLANCE” project (Global Change Effects in River Ecosystems; 01 LN1320A). We wish to thank Gwen Iacona and two anonymous referees for their constructive comments on an earlier version of the manuscript

β-diversity scaling patterns are consistent across metrics and taxa

We thank the University of St Andrews Bioinformatics Unit (Wellcome Trust ISSF grant 105621/Z/14/Z). L.H.A.was supported by Fundação para a Ciência e Tecnologia, Portugal (POPH/FSE SFRH/BD/90469/2012), A.E.M. by the ERC BioTIME (250189) and BioCHANGE (727440), and B.J.M. by USDA Hatch grant to MAFES #1011538 and NSF ABI grant #1660000. The BioTIME database was funded by ERC AdG BioTIME (250189) and ERC PoC BioCHANGE (727440).β‐diversity (variation in community composition) is a fundamental component of biodiversity, with implications for macroecology, community ecology and conservation. However, its scaling properties are poorly understood. Here, we systematically assessed the spatial scaling of β‐diversity using 12 empirical large‐scale datasets including different taxonomic groups, by examining two conceptual types of β‐diversity and explicitly considering the turnover and nestedness components. We found highly consistent patterns across datasets. Multiple‐site β‐diversity (i.e. variation across multiple sites) scaling curves were remarkably consistent, with β‐diversity decreasing with sampled area according to a power law. For pairwise dissimilarities, the rates of increase of dissimilarity with geographic distance remained largely constant across scales, while grain size (or scale level) had a stronger effect on overall dissimilarity. In both analyses, turnover was the main contributor to β‐diversity, following total β‐diversity patterns closely, while the nestedness component was largely insensitive to scale changes. Our results highlight the importance of integrating both inter‐ and intraspecific aggregation patterns across spatial scales, which underpin substantial differences in community structure from local to regional scales.PostprintPeer reviewe

Continuous inference for aggregated point process data

The paper introduces new methods for inference with count data registered on a set of aggregation units. Such data are omnipresent in epidemiology because of confidentiality issues: it is much more common to know the county in which an individual resides, say, than to know their exact location in space. Inference for aggregated data has traditionally made use of models for discrete spatial variation, e.g. conditional auto-regressive models. We argue that such discrete models can be improved from both a scientific and an inferential perspective by using spatiotemporally continuous models to model the aggregated counts directly. We introduce methods for delivering (limiting) continuous inference with spatiotemporal aggregated count data in which the aggregation units might change over time and are subject to uncertainty. We illustrate our methods by using two examples: from epidemiology, spatial prediction of malaria incidence in Namibia, and, from politics, forecasting voting under the proposed changes to parliamentary boundaries in the UK. © 2018 Royal Statistical Societ

Multi‐dimensional biodiversity hotspots and the future of taxonomic, ecological and phylogenetic diversity: A case study of North American rodents

AimWe investigate geographic patterns across taxonomic, ecological and phylogenetic diversity to test for spatial (in)congruency and identify aggregate diversity hotspots in relationship to present land use and future climate. Simulating extinctions of imperilled species, we demonstrate where losses across diversity dimensions and geography are predicted.LocationNorth America.Time periodPresent day, future.Major taxa studiedRodentia.MethodsUsing geographic range maps for rodent species, we quantified spatial patterns for 11 dimensions of diversity: taxonomic (species, range weighted), ecological (body size, diet and habitat), phylogenetic (mean, variance, and nearest‐neighbour patristic distances, phylogenetic distance and genus‐to‐species ratio) and phyloendemism. We tested for correlations across dimensions and used spatial residual analyses to illustrate regions of pronounced diversity. We aggregated diversity hotspots in relationship to predictions of land‐use and climate change and recalculated metrics following extinctions of IUCN‐listed imperilled species.ResultsTopographically complex western North America hosts high diversity across multiple dimensions: phyloendemism and ecological diversity exceed predictions based on taxonomic richness, and phylogenetic variance patterns indicate steep gradients in phylogenetic turnover. An aggregate diversity hotspot emerges in the west, whereas spatial incongruence exists across diversity dimensions at the continental scale. Notably, phylogenetic metrics are uncorrelated with ecological diversity. Diversity hotspots overlap with land‐use and climate change, and extinctions predicted by IUCN status are unevenly distributed across space, phylogeny or ecological groups.Main conclusionsComparison of taxonomic, ecological and phylogenetic diversity patterns for North American rodents clearly shows the multifaceted nature of biodiversity. Testing for geographic patterns and (in)congruency across dimensions of diversity facilitates investigation into underlying ecological and evolutionary processes. The geographic scope of this analysis suggests that several explicit regional challenges face North American rodent fauna in the future. Simultaneous consideration of multi‐dimensional biodiversity allows us to assess what critical functions or evolutionary history we might lose with future extinctions and maximize the potential of our conservation efforts.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154236/1/geb13050.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154236/2/geb13050_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154236/3/geb13050-sup-0001-Supinfo1.pd

Mapping nitrate leaching to upper groundwater in the sandy regions of The Netherlands, using conceptual knowledge

The European Community asks its Member States to provide a comprehensive and coherent overview of their groundwater chemical status. It is stated that simple conceptual models are necessary to allow assessments of the risks of failing to meet quality objectives. In The Netherlands two monitoring networks (one for agriculture and one for nature) are operational, providing results which can be used for an overview. Two regression models, based upon simple conceptual models, link measured nitrate concentrations to data from remote sensing images of land use, national forest inventory, national cattle inventory, fertiliser use statistics, atmospheric N deposition, soil maps and weather monitoring. The models are used to draw a nitrate leaching map and to estimate the size of the area exceeding the EU limit value in the early 1990s. The 95% confidence interval for the fraction nature and agricultural areas where the EU limit value for nitrate (50 mg/l) was exceeded amounted to 0.77–0.85 while the lower 97.5% confidence limit for the fraction agricultural area where the EU limit value was exceeded amounted to 0.94. Although the two conceptual models can be regarded as simple, the use of the models to give an overview was experienced as complex

Non-Parametric Approximations for Anisotropy Estimation in Two-dimensional Differentiable Gaussian Random Fields

Spatially referenced data often have autocovariance functions with elliptical isolevel contours, a property known as geometric anisotropy. The anisotropy parameters include the tilt of the ellipse (orientation angle) with respect to a reference axis and the aspect ratio of the principal correlation lengths. Since these parameters are unknown a priori, sample estimates are needed to define suitable spatial models for the interpolation of incomplete data. The distribution of the anisotropy statistics is determined by a non-Gaussian sampling joint probability density. By means of analytical calculations, we derive an explicit expression for the joint probability density function of the anisotropy statistics for Gaussian, stationary and differentiable random fields. Based on this expression, we obtain an approximate joint density which we use to formulate a statistical test for isotropy. The approximate joint density is independent of the autocovariance function and provides conservative probability and confidence regions for the anisotropy parameters. We validate the theoretical analysis by means of simulations using synthetic data, and we illustrate the detection of anisotropy changes with a case study involving background radiation exposure data. The approximate joint density provides (i) a stand-alone approximate estimate of the anisotropy statistics distribution (ii) informed initial values for maximum likelihood estimation, and (iii) a useful prior for Bayesian anisotropy inference.Comment: 39 pages; 8 figure

Evolving thermal thresholds explain the distribution of temperature sex reversal in an Australian dragon lizard

Aim: Species with temperature-dependent sex determination (TSD) are particularly vulnerable to climate change because a resultant skew in population sex ratio can have severe demographic consequences and increase vulnerability to local extinction. The Australian central bearded dragon (Pogona vitticeps) has a thermosensitive ZZ male/ZW female system of genetic sex determination (GSD). High incubation temperatures cause reversal of the ZZ genotype to a viable female phenotype. Nest temperatures in the wild are predicted to vary on a scale likely to produce heterogeneity in the occurrence of sex reversal, and so we predict that sex reversal will correlate positively with inferred incubation conditions. Location: Mainland Australia. Methods: Wild-caught specimens of P. vitticeps vouchered in museum collections and collected during targeted field trips were genotypically and phenotypically sexed to determine the distribution of sex reversal across the species range. To determine whether environmental conditions or genetic structure can explain this distribution, we infer the incubation conditions experienced by each individual and apply a multi-model inference approach to determine which conditions associate with sex reversal. Further, we conduct reduced representation sequencing on a subset of specimens to characterize the population structure of this broadly distributed species. Results: Here we show that sex reversal in this widespread Australian dragon lizard is spatially restricted to the eastern part of the species range. Neither climatic variables during the inferred incubation period nor geographic population genetic structure explain this disjunct distribution of sex reversal. The main source of genetic variation arose from isolation by distance across the species range. Main conclusions: We propose that local genetic adaptation in the temperature threshold for sex reversal can counteract the sex-reversing influence of high incubation temperatures in P. vitticeps. Our study demonstrates that complex evolutionary processes need to be incorporated into modelling biological responses to future climate scenarios

Electrical Power Fluctuations in a Network of DC/AC inverters in a Large PV Plant: relationship between correlation, distance and time scale

This paper analyzes the correlation between the fluctuations of the electrical power generated by the ensemble of 70 DC/AC inverters from a 45.6 MW PV plant. The use of real electrical power time series from a large collection of photovoltaic inverters of a same plant is an impor- tant contribution in the context of models built upon simplified assumptions to overcome the absence of such data. This data set is divided into three different fluctuation categories with a clustering proce- dure which performs correctly with the clearness index and the wavelet variances. Afterwards, the time dependent correlation between the electrical power time series of the inverters is esti- mated with the wavelet transform. The wavelet correlation depends on the distance between the inverters, the wavelet time scales and the daily fluctuation level. Correlation values for time scales below one minute are low without dependence on the daily fluctuation level. For time scales above 20 minutes, positive high correlation values are obtained, and the decay rate with the distance depends on the daily fluctuation level. At intermediate time scales the correlation depends strongly on the daily fluctuation level. The proposed methods have been implemented using free software. Source code is available as supplementary material

A large-scale study of a poultry trading network in Bangladesh: implications for control and surveillance of avian influenza viruses

Since its first report in 2007, avian influenza (AI) has been endemic in Bangladesh. While live poultry marketing is widespread throughout the country and known to influence AI dissemination and persistence, trading patterns have not been described. The aim of this study is to assess poultry trading practices and features of the poultry trading networks which could promote AI spread, and their potential implications for disease control and surveillance. Data on poultry trading practices was collected from 849 poultry traders during a cross-sectional survey in 138 live bird markets (LBMs) across 17 different districts of Bangladesh. The quantity and origins of traded poultry were assessed for each poultry type in surveyed LBMs. The network of contacts between farms and LBMs resulting from commercial movements of live poultry was constructed to assess its connectivity and to identify the key premises influencing it