The Experts below are selected from a list of 16269 Experts worldwide ranked by ideXlab platform
César Capinha - One of the best experts on this subject based on the ideXlab platform.
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Predicting the timing of Ecological Phenomena using dates of species occurrence records: a methodological approach and test case with mushrooms
International Journal of Biometeorology, 2019Co-Authors: César CapinhaAbstract:Spatiotemporal predictions of Ecological Phenomena are highly useful and significant in scientific and socio-economic applications. However, the inadequate availability of Ecological time-series data often impedes the development of statistical predictions. On the other hand, considerable amounts of temporally discrete biological records (commonly known as ‘species occurrence records’) are being stored in public databases, and often include the location and date of the observation. In this paper, we describe an approach to develop spatiotemporal predictions based on the dates and locations found in species occurrence records. The approach is based on ‘time-series classification’, a field of machine learning, and consists of applying a machine-learning algorithm to classify between time series representing the environmental variation that precedes the occurrence records and time series representing the full range of environmental variation that is available in the location of the records. We exemplify the application of the approach for predicting the timing of emergence of fruiting bodies of two mushroom species ( Boletus edulis and Macrolepiota procera ) in Europe, from 2009 to 2015. Predictions made from this approach were superior to those provided by a ‘null’ model representing the average seasonality of the species. Given the increased availability and information contained in species occurrence records, particularly those supplemented with photographs, the range of environmental events that could be possible to predict using this approach is vast.
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A machine learning approach for the spatiotemporal forecasting of Ecological Phenomena using dates of species occurrence records
2018Co-Authors: César CapinhaAbstract:Spatiotemporal forecasts of Ecological Phenomena are highly useful and significant in scientific and socio-economic applications. Nevertheless, developing the correlative models to make these forecasts is often stalled by the inadequate availability of the Ecological time-series data. On the contrary, considerable amounts of temporally discrete biological records are being stored in public databases, and often include the sites and dates of the observation. While these data are reasonably suitable for the development of spatiotemporal forecast models, this possibility remains mostly untested. In this paper, we test an approach to develop spatiotemporal forecasts based on the dates and locations found in species occurrence records. This approach is based on 'time-series classification', a field of machine learning, and involves the application of a machine-learning algorithm to classify between time-series representing the environmental conditions that precede the occurrence records and time-series representing other environmental conditions, such as those that generally occur in the sites of the records. We employed this framework to predict the timing of emergence of fruiting bodies of two mushroom species (Boletus edulis and Macrolepiota procera) in countries of Europe, from 2009 to 2015. We compared the predictions from this approach with those from a 'null' model, based on the calendar dates of the records. Forecasts made from the environmental-based approach were consistently superior to those drawn from the date-based approach, averaging an area under the receiver operating characteristic curve (AUC) of 0.9 for B. edulis and 0.88 for M. procera, compared to an average AUC of 0.83 achieved by the null models for both species. Prediction errors were distributed across the study area and along the years, lending support to the spatiotemporal representativeness of the values of accuracy measured. Our approach, based on species occurrence records, was able to provide useful forecasts of the timing of emergence of two mushroom species across Europe. Given the increased availability and information contained in this type of records, particularly those supplemented with photographs, the range of events that could be possible to forecast is vast.
Michael S. Rosenberg - One of the best experts on this subject based on the ideXlab platform.
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A balanced view of scale in spatial statistical analysis
Ecography, 2002Co-Authors: Jennifer L. Dungan, Pierre Legendre, Joe N. Perry, Mark R. T. Dale, S. Citron-pousty, Marie-josée Fortin, A. Jakomulska, M. Miriti, Michael S. RosenbergAbstract:Concepts of spatial scale, such as extent, grain, resolution, range, footprint, support and cartographic ratio are not interchangeable. Because of the potential confusion among the definitions of these terms, we suggest that authors avoid the term "scale" and instead refer to specific concepts. In particular, we are careful to discriminate between observation scales, scales of Ecological Phenomena and scales used in spatial statistical analysis. When scales of observation or analysis change, that is, when the unit size, shape, spacing or extent are altered, statistical results are expected to change. The kinds of results that may change include estimates of the population mean and variance, the strength and character of spatial autocorrelation and spatial anisotropy, patch and gap sizes and multivariate relationships, The First three of these results (precision of the mean, variance and spatial autocorrelation) can sometimes be estimated using geostatistical support-effect models. We present four case studies of organism abundance and cover illustrating some of these changes and how conclusions about Ecological Phenomena (process and structure) may be affected. We identify the influence of observational scale on statistical results as a subset of what geographers call the Modifiable Area Unit Problem (MAUP). The way to avoid the MAUP is by careful construction of sampling design and analysis. We recommend a set of considerations for sampling design to allow useful tests for specific scales of a phenomenon under study. We further recommend that Ecological studies completely report all components of observation and analysis scales to increase the possibility of cross-study comparisons.
Martine Maron - One of the best experts on this subject based on the ideXlab platform.
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Non-random patterns of vegetation clearing and potential biases in studies of habitat area effects.
Landscape Ecology, 2017Co-Authors: Jeremy S. Simmonds, Berndt J. Van Rensburg, Martine MaronAbstract:Context Native vegetation extent is often a proxy for habitat area in studies of human-modified landscapes. However, the loss and retention of native vegetation is rarely random among landscapes. Instead, the extent of native vegetation in landscapes may be correlated with abiotic factors, thereby obscuring or distorting relationships between Ecological Phenomena and area.
Jennifer L. Dungan - One of the best experts on this subject based on the ideXlab platform.
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A balanced view of scale in spatial statistical analysis
Ecography, 2002Co-Authors: Jennifer L. Dungan, Pierre Legendre, Joe N. Perry, Mark R. T. Dale, S. Citron-pousty, Marie-josée Fortin, A. Jakomulska, M. Miriti, Michael S. RosenbergAbstract:Concepts of spatial scale, such as extent, grain, resolution, range, footprint, support and cartographic ratio are not interchangeable. Because of the potential confusion among the definitions of these terms, we suggest that authors avoid the term "scale" and instead refer to specific concepts. In particular, we are careful to discriminate between observation scales, scales of Ecological Phenomena and scales used in spatial statistical analysis. When scales of observation or analysis change, that is, when the unit size, shape, spacing or extent are altered, statistical results are expected to change. The kinds of results that may change include estimates of the population mean and variance, the strength and character of spatial autocorrelation and spatial anisotropy, patch and gap sizes and multivariate relationships, The First three of these results (precision of the mean, variance and spatial autocorrelation) can sometimes be estimated using geostatistical support-effect models. We present four case studies of organism abundance and cover illustrating some of these changes and how conclusions about Ecological Phenomena (process and structure) may be affected. We identify the influence of observational scale on statistical results as a subset of what geographers call the Modifiable Area Unit Problem (MAUP). The way to avoid the MAUP is by careful construction of sampling design and analysis. We recommend a set of considerations for sampling design to allow useful tests for specific scales of a phenomenon under study. We further recommend that Ecological studies completely report all components of observation and analysis scales to increase the possibility of cross-study comparisons.
Lawrence R. Walker - One of the best experts on this subject based on the ideXlab platform.
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Four opportunities for studies of Ecological succession
Trends in Ecology and Evolution, 2011Co-Authors: Karel Prach, Lawrence R. WalkerAbstract:Lessons learned from the study of Ecological succession have much to offer contemporary environmental problem solving but these lessons are being underutilized. As anthropogenic disturbances increase, succession is more relevant than ever. In this review, we suggest that succession is particularly suitable to address concerns about biodiversity loss, climate change, invasive species, and Ecological restoration. By incorporating modern experimental techniques and linking results across environmental gradients with meta-analyses, studies of succession can substantially improve our understanding of other Ecological Phenomena. Succession can help predict changes in biodiversity and ecosystem services impacted by invasive species and climate change and guide manipulative responses to these disruptions by informing restoration efforts. Succession is still a critical, integrative concept that is central to ecology.
