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      MODISTools – downloading and processing MODIS remotely sensed data in R

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          Abstract

          Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R 2 values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub ( https://github.com/seantuck12/MODISTools).

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          Primary forests are irreplaceable for sustaining tropical biodiversity.

          Human-driven land-use changes increasingly threaten biodiversity, particularly in tropical forests where both species diversity and human pressures on natural environments are high. The rapid conversion of tropical forests for agriculture, timber production and other uses has generated vast, human-dominated landscapes with potentially dire consequences for tropical biodiversity. Today, few truly undisturbed tropical forests exist, whereas those degraded by repeated logging and fires, as well as secondary and plantation forests, are rapidly expanding. Here we provide a global assessment of the impact of disturbance and land conversion on biodiversity in tropical forests using a meta-analysis of 138 studies. We analysed 2,220 pairwise comparisons of biodiversity values in primary forests (with little or no human disturbance) and disturbed forests. We found that biodiversity values were substantially lower in degraded forests, but that this varied considerably by geographic region, taxonomic group, ecological metric and disturbance type. Even after partly accounting for confounding colonization and succession effects due to the composition of surrounding habitats, isolation and time since disturbance, we find that most forms of forest degradation have an overwhelmingly detrimental effect on tropical biodiversity. Our results clearly indicate that when it comes to maintaining tropical biodiversity, there is no substitute for primary forests.
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            Monitoring change in vertebrate abundance: the living planet index.

            The task of measuring the decline of global biodiversity and instituting changes to halt and reverse this downturn has been taken up in response to the Convention on Biological Diversity's 2010 target. It is an undertaking made more difficult by the complex nature of biodiversity and the consequent difficulty in accurately gauging its depletion. In the Living Planet Index, aggregated population trends among vertebrate species indicate the rate of change in the status of biodiversity, and this index can be used to address the question of whether or not the 2010 target has been achieved. We investigated the use of generalized additive models in aggregating large quantities of population trend data, evaluated potential bias that results from collation of existing trends, and explored the feasibility of disaggregating the data (e.g., geographically, taxonomically, regionally, and by thematic area). Our results show strengths in length and completeness of data, little evidence of bias toward threatened species, and the possibility of disaggregation into meaningful subsets. Limitations of the data set are still apparent, in particular the dominance of bird data and gaps in tropical-species population coverage. Population-trend data complement the longer-term, but more coarse-grained, perspectives gained by evaluating species-level extinction rates. To measure progress toward the 2010 target, indicators must be adapted and strategically supplemented with existing data to generate meaningful indicators in time. Beyond 2010, it is critical a strategy be set out for the future development of indicators that will deal with existing data gaps and that is intricately tied to the goals of future biodiversity targets.
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              The effect of energy and seasonality on avian species richness and community composition.

              We analyzed geographic patterns of richness in both the breeding and winter season in relation to a remotely sensed index of seasonal production (normalized difference vegetation index [NDVI]) and to measures of habitat heterogeneity at four different spatial resolutions. The relationship between avian richness and NDVI was consistent between seasons, suggesting that the way in which available energy is converted to bird species is similar at these ecologically distinct times of year. The number and proportion of migrant species in breeding communities also increased predictably with the degree of seasonality. The NDVI was a much better predictor of seasonal richness at finer spatial scales, whereas habitat heterogeneity best predicted richness at coarser spatial resolutions. While we find strong support for a positive relationship between available energy and species richness, seasonal NDVI explained at most 61% of the variation in richness. Seasonal NDVI and habitat heterogeneity together explain up to 69% of the variation in richness.
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                Author and article information

                Journal
                Ecol Evol
                Ecol Evol
                ece3
                Ecology and Evolution
                Blackwell Publishing Ltd (Oxford, UK )
                2045-7758
                2045-7758
                December 2014
                02 December 2014
                : 4
                : 24
                : 4658-4668
                Affiliations
                [1 ]Department of Plant Sciences, University of Oxford Oxford, OX1 3RB, U.K
                [2 ]Department of Life Sciences, Imperial College London, Silwood Park Buckhurst Road, Ascot, Berkshire, SL5 7PY, U.K
                [3 ]Department of Life Sciences, Natural History Museum Cromwell Road, London, SW7 5BD, U.K
                [4 ]School of Life Sciences, University of Sussex Brighton, BN1 9QG, U.K
                Author notes
                Sean L. Tuck, Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, U.K. Tel: 01865 275140 E-mail: sean.tuck@ 123456plants.ox.ac.uk

                Funding Information This work has been supported by the UK Natural Environment Research Council (NERC; grants NE/K500811/1 and NE/J011193/1), and the Hans Rausing Scholarship.

                Article
                10.1002/ece3.1273
                4278818
                25558360
                f2e05920-041c-4f7b-8285-435e3d88034e
                © 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 April 2014
                : 29 August 2014
                : 01 September 2014
                Categories
                Original Research

                Evolutionary Biology
                conservation biology,earth observation,global change,land processes,macroecology,predicts,remote-sensing,satellite imagery

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