MotivationThe BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables includedThe database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grainBioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL.
Abstract. Population-level estimates of species' distributions can reveal fundamental ecological processes and facilitate conservation. However, these may be difficult to obtain for mobile species, especially colonial central-place foragers (CCPFs; e.g., bats, corvids, social insects), because it is often impractical to determine the provenance of individuals observed beyond breeding sites. Moreover, some CCPFs, especially in the marine realm (e.g., pinnipeds, turtles, and seabirds) are difficult to observe because they range tens to ten thousands of kilometers from their colonies. It is hypothesized that the distribution of CCPFs depends largely on habitat availability and intraspecific competition. Modeling these effects may therefore allow distributions to be estimated from samples of individual spatial usage. Such data can be obtained for an increasing number of species using tracking technology. However, techniques for estimating population-level distributions using the telemetry data are poorly developed. This is of concern because many marine CCPFs, such as seabirds, are threatened by anthropogenic activities. Here, we aim to estimate the distribution at sea of four seabird species, foraging from approximately 5,500 breeding sites in Britain and Ireland. To do so, we GPS-tracked a sample of 230 European Shags Phalacrocorax aristotelis, 464 Black-legged Kittiwakes Rissa tridactyla, 178 Common Murres Uria aalge, and 281 Razorbills Alca torda from 13, 20, 12, and 14 colonies, respectively. Using Poisson point process habitat use models, we show that distribution at sea is dependent on (1) density-dependent competition among sympatric conspecifics (all species) and parapatric conspecifics (Kittiwakes and Murres); (2) habitat accessibility and coastal geometry, such that birds travel further from colonies with limited access to the sea; and (3) regional habitat availability. Using these models, we predict space use by birds from unobserved colonies and thereby map the distribution at sea of each species at both the colony and regional level. Space use by all four species' British breeding populations is concentrated in the coastal waters of Scotland, highlighting the need for robust conservation measures in this area. The techniques we present are applicable to any CCPF.
Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. conditions. We use mixed models to consider how SST, the potential energy anomaly 47 (indicating density stratification strength) and the timing of seasonal stratification influence 48 kittiwake productivity. Across all colonies, higher breeding success was associated with 49 weaker stratification before breeding and lower SSTs during the breeding season. Eight 50 colonies with sufficient data were modelled individually: higher productivity was associated 51 with later stratification at three colonies, weaker stratification at two, and lower SSTs at one, 52 whilst two colonies showed no significant relationships. Hence, key drivers of productivity 53 varied among colonies. Climate change projections, made using fitted models, indicated that 54 breeding success could decline by 21 -43% between 1961-90 and 2070-99. Climate change 55 therefore poses a longer-term threat to kittiwakes, but as this will be mediated via availability 56 of key prey species, other marine apex predators could also face similar threats. 57 58 4 INTRODUCTION 59Ecological impacts of climate change are increasingly well-understood, with changes in 60 species' ranges and phenology predicted and observed in both terrestrial and marine 61
The fitness benefits of intraspecific variation in physiological attributes have rarely been measured. Body condition, defined as the current status of metabolic reserves relative to likely demands, has often been implicated in subsequent survival, but has proved difficult to assess reliably in the live animal. A technique for assessing body condition, in terms of the main protein reserve of small birds, is presented. Pectoralis muscle thickness was measured in live birds using ultrasound reflection from the sternum. The relationship between the relative size of pectoralis muscles in autumn and the likelihood of overwinter survival in the dipper Cinclus cinclus was examined. The pectoralis reserves of male dippers surviving overwinter were significantly greater than those of birds which died or disappeared between late November and the breeding season in April. In contrast, variation in autumn condition of females was unrelated to overwinter survival.
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