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Lancaster, United Kingdom

Mountain plants are considered among the species most vulnerable to climate change, especially at high latitudes where there is little potential for poleward or uphill dispersal. Satellite monitoring can reveal spatiotemporal variation in vegetation activity, offering a largely unexploited potential for studying responses of montane ecosystems to temperature and predicting phenological shifts driven by climate change. Here, a novel remote-sensing phenology approach is developed that advances existing techniques by considering variation in vegetation activity across the whole year, rather than just focusing on event dates (e.g. start and end of season). Time series of two vegetation indices (VI), normalized difference VI (NDVI) and enhanced VI (EVI) were obtained from the moderate resolution imaging spectroradiometer MODIS satellite for 2786 Scottish mountain summits (600-1344 m elevation) in the years 2000-2011. NDVI and EVI time series were temporally interpolated to derive values on the first day of each month, for comparison with gridded monthly temperatures from the preceding period. These were regressed against temperature in the previous months, elevation and their interaction, showing significant variation in temperature sensitivity between months. Warm years were associated with high NDVI and EVI in spring and summer, whereas there was little effect of temperature in autumn and a negative effect in winter. Elevation was shown to mediate phenological change via a magnification of temperature responses on the highest mountains. Together, these predict that climate change will drive substantial changes in mountain summit phenology, especially by advancing spring growth at high elevations. The phenological plasticity underlying these temperature responses may allow long-lived alpine plants to acclimate to warmer temperatures. Conversely, longer growing seasons may facilitate colonization and competitive exclusion by species currently restricted to lower elevations. In either case, these results show previously unreported seasonal and elevational variation in the temperature sensitivity of mountain vegetation activity. © 2013 John Wiley & Sons Ltd. Source

Dawson A.,UK Center for Ecology and Hydrology
General and Comparative Endocrinology

Photoperiod is the major cue used by birds to time breeding seasons and molt. However, the annual cycle in photoperiod changes with latitude. Within species, for temperate and high latitude species, gonadal maturation and breeding start earlier at lower latitudes but regression and molt both occur at similar times at different latitudes. Earlier gonadal maturation can be explained simply by the fact that considerable maturation occurs before the equinox when photoperiod is longer at lower latitudes - genetic differences between populations are not necessary to explain earlier breeding at lower latitudes. Gonadal regression is caused either by absolute photorefractoriness or, in some species with long breeding seasons, relative photorefractoriness. In either case, the timing of regression and molt cannot be explained by absolute prevailing photoperiod or rate of change in photoperiod - birds appear to be using more subtle cues from the pattern of change in photoperiod. However, there may be no difference between absolute and relative photorefractory species in how they utilise the annual cycle in photoperiod to time regression. © 2013 Elsevier Inc. Source

Chapman D.S.,UK Center for Ecology and Hydrology
Global Ecology and Biogeography

Aim Species distribution models (SDMs) are used to infer niche responses and predict climate change-induced range shifts. However, their power to distinguish real and chance associations between spatially autocorrelated distribution and environmental data at continental scales has been questioned. Here this is investigated at a regional (10 km) scale by modelling the distributions of 100 plant species native to the UK.Location UK.Methods SDMs fitted using real climate data were compared with those utilizing simulated climate gradients. The simulated gradients preserve the exact values and spatial structure of the real ones, but have no causal relationships with any species and so represent an appropriate null model. SDMs were fitted as generalized linear models (GLMs) or by the Random Forest machine-learning algorithm and were either non-spatial or included spatially explicit trend surfaces or autocovariates as predictors.Results Species distributions were significantly but erroneously related to the simulated gradients in 86% of cases (P < 0.05 in likelihood-ratio tests of GLMs), with the highest error for strongly autocorrelated species and gradients and when species occupied 50% of sites. Even more false effects were found when curvilinear responses were modelled, and this was not adequately mitigated in the spatially explicit models. Non-spatial SDMs based on simulated climate data suggested that 70-80% of the apparent explanatory power of the real data could be attributable to its spatial structure. Furthermore, the niche component of spatially explicit SDMs did not significantly contribute to model fit in most species.Main conclusions Spatial structure in the climate, rather than functional relationships with species distributions, may account for much of the apparent fit and predictive power of SDMs. Failure to account for this means that the evidence for climatic limitation of species distributions may have been overstated. As such, predicted regional- and national-scale impacts of climate change based on the analysis of static distribution snapshots will require re-evaluation. © 2010 Crown Copyright. Source

Hill M.O.,UK Center for Ecology and Hydrology
Methods in Ecology and Evolution

Data on the occurrence of species in grid cells are collected by biological recording schemes, typically with the intention of publishing an atlas. Interpretation of such data is often hampered by the lack of information on the effort that went into collecting them. This is the 'recorder effort problem'. One measure of recorder effort is the proportion of a suite of common species ('benchmark species') found at a given location and time. Benchmark species have in the past been taken as a uniform set across a territory. However, if records are available from a neighbourhood surrounding a given location, then a local set benchmark species can be defined by pooling records from the neighbourhood and selecting the commonest species in the pooled set. Neighbourhoods differ in species richness, so that the list of species that 'ought' to be found in one location may be longer than that for another. If the richness of a neighbourhood can be estimated, then a suite of benchmark species can be standardized to be the commonest of a fixed proportion of the total expected for the neighbourhood. Recording effort is then defined as the proportion of benchmark species that were found. A method of estimating species richness is proposed here, based on the local frequencies f j of species in neighbouring grid cells. Species discovery is modelled as a Poisson process. It is argued that when a neighbourhood is well sampled, the frequency-weighted mean frequency /∑f j of species in the neighbourhood will assume a standard value. The method was applied to a data set of 2000000 records detailing the occurrence of bryophytes in 3695 out of the total 3854hectads (10-km squares) in Great Britain, Ireland, the Isle of Man and the Channel Islands. 6.Three main applications are outlined: estimation of recording effort, scanning data for unexpected presences or absences and measurement of species trends over time. An explicit statistical model was used to estimate trends, modelling the probability of species j being found at location i and time t as the outcome of Poisson process with intensity Q ijtx jt, where x jt is a time factor for species j, and Q ijt depends on recording effort at location i and time t and on the time-independent probability of species j being found in hectad i. © 2011 The Author. Methods in Ecology and Evolution © 2011 British Ecological Society. Source

Dawson A.,UK Center for Ecology and Hydrology
Frontiers in Neuroendocrinology

This paper reviews current knowledge of photoperiod control of GnRH-1 secretion and proposes a model in which two processes act together to regulate GnRH1 secretion. Photo-induction controls GnRH1 secretion and is directly related to prevailing photoperiod. Photo-inhibition, a longer term process, acts through GnRH1 synthesis. It progresses each day during daylight hours, but reverses during darkness. Thus, photo-inhibition gradually increases when photoperiods exceed 12. h, and reverses under shorter photoperiods. GnRH1 secretion on any particular day is the net result of these two processes acting in tandem. The only difference between species is their sensitivity to photo-inhibition. This can potentially explain differences in timing and duration of breeding seasons between species, why some species become absolutely photorefractory and others relatively photorefractory, why breeding seasons end at the same time at different latitudes within species, and why experimental protocols sometimes produce results that appear counter to what happens naturally. © 2014 The Author. Source

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