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71
Modelling natural disturbances in forest ecosystems: a review
- ECOLOGICAL MODELLING
, 2011
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Increasing range mismatching of interacting species under global change is related to species traits
- Global Ecol. Biogeogr
"... ABSTRACT Aim We investigate the importance of interacting species for current and potential future species distributions, the influence of their ecological characteristics on projected range shifts when considering or ignoring interacting species, and the consistency of observed relationships acros ..."
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ABSTRACT Aim We investigate the importance of interacting species for current and potential future species distributions, the influence of their ecological characteristics on projected range shifts when considering or ignoring interacting species, and the consistency of observed relationships across different global change scenarios. Location Europe. Methods We developed ecological niche models (generalized linear models) for 36 European butterfly species and their larval host plants based on climate and land-use data. We projected future distributional changes using three integrated global change scenarios for 2080. Observed and projected mismatches in potential butterfly niche space and the niche space of their hosts were first used to assess changing range limitations due to interacting species and then to investigate the importance of different ecological characteristics. Results Most butterfly species were primarily limited by climate. Species dwelling in warm areas of Europe and tolerant to large variations in moisture conditions were projected to suffer less from global change. However, a gradient from climate to host plant control was apparent, reflecting the range size of the hosts. Future projections indicated increased mismatching of already host-plant-limited butterflies and their hosts. Butterflies that utilize plants with restricted ranges were projected to suffer most from global change. The directions of these relationships were consistent across the scenarios but the level of spatial mismatching of butterflies and their host plants increased with the severity of the scenario. Main conclusions Future changes in the co-occurrence of interacting species will depend on political and socio-economic development, suggesting that the composition of novel communities due to global change will depend on the way we create our future. A better knowledge of ecological species characteristics can be utilized to project the future fate and potential risk of extinction of interacting species leading to a better understanding of the consequences of changing biotic interactions. This will further enhance our abilities to assess and mitigate potential negative effects on ecosystem functions and services.
Their Evolution
- in the US: Consumption and Income Information in the Consumer Expenditure. Paper prepared for the ASPE-Initiated Workshop on Consumption among Low-Income Families, National Poverty
"... on biodiverSity ..."
Uncertainties in projecting spatial distributions of marine populations
- ICES Journal of Marine Science
, 2011
"... Projection of future spatial distributions of marine populations is a central issue for ecologists and managers. The measure of projec-tion uncertainty is particularly important, because projections can only be useful if they are given with a known and sufficiently high level of confidence. Uncertai ..."
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Cited by 7 (0 self)
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Projection of future spatial distributions of marine populations is a central issue for ecologists and managers. The measure of projec-tion uncertainty is particularly important, because projections can only be useful if they are given with a known and sufficiently high level of confidence. Uncertainties can arise for the observation process, conceptual and numerical model formulations, parameter estimates, model evaluation, appropriate consideration of spatial and temporal scales, and finally the potential of adaptation of living systems. Comprehensive analyses of these multiple sources of uncertainty have not been carried out so far, and how these uncer-tainties are considered in current studies has not yet been described. To analyse how these different sources of uncertainty are cur-rently considered in marine research, we did a survey of published literature during the period 2005–2009. From the 75 publications selected, we calculated how frequently each type of uncertainty was considered. We found that little attention is given to most sources of uncertainty, except for uncertainty in parameter estimates. As a result, most current projections are expected to be far less reliable than usually assumed. The conclusion is that, unless uncertainty can be better accounted for, such projections may be of limited use, or even risky to use for management purposes.
Partitioning variation in ecological communities: do the numbers add up
- J. Appl. Ecol
, 2010
"... 1. Statistical tests partitioning community variation into environmental and spatial components have been widely used to test ecological theories and explore the determinants of community struc-ture for applied conservation questions. Despite the wide use of these tests, there is considerable debate ..."
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1. Statistical tests partitioning community variation into environmental and spatial components have been widely used to test ecological theories and explore the determinants of community struc-ture for applied conservation questions. Despite the wide use of these tests, there is considerable debate about their relative effectiveness. 2. We used simulated communities to evaluate the most commonly employed tests that partition community variation: regression on distance matrices and canonical ordination using a third-order polynomial, principal components of neighbour matrices (PCNM) or Moran’s eigenvector maps (MEM) to model spatial components. Each test was evaluated under a variety of realistic sampling scenarios. 3. All tests failed to correctly model spatial and environmental components of variation, and in some cases produced biased estimates of the relative importance of components. Regression on distance matrices under-fit the spatial component, and ordination models consistently under-fit the environmental component. The PCNMandMEMapproaches often produced inflatedR2 statistics, apparently as a result of statistical artefacts involving selection of superfluous axes. This problem occurred regardless of the forward-selection technique used.
Projected Loss of a Salamander Diversity Hotspot as a Consequence of Projected Global Climate Change. PLoS
- ONE
, 2010
"... Abstract Background: Significant shifts in climate are considered a threat to plants and animals with significant physiological limitations and limited dispersal abilities. The southern Appalachian Mountains are a global hotspot for plethodontid salamander diversity. Plethodontids are lungless ecto ..."
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Abstract Background: Significant shifts in climate are considered a threat to plants and animals with significant physiological limitations and limited dispersal abilities. The southern Appalachian Mountains are a global hotspot for plethodontid salamander diversity. Plethodontids are lungless ectotherms, so their ecology is strongly governed by temperature and precipitation. Many plethodontid species in southern Appalachia exist in high elevation habitats that may be at or near their thermal maxima, and may also have limited dispersal abilities across warmer valley bottoms.
Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias. PLoS ONE. 2014; 9: e97122. doi: 10.1371/journal.pone.0097122 PMID
"... MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical spa ..."
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MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one ‘‘virtual’’ derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias.
Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI
, 2012
"... Abstract: Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of spe ..."
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Abstract: Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation
Biodiversity Indicators Show Climate Change Will Alter Vegetation in Parks and Protected Areas
, 2013
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Predicting the Potential Impacts of Climate Change on Stream Fish Assemblages
"... Abstract.—Stream fish are expected to be influenced by climate change as they are ectothermic animals living in lotic systems. Using fish presence–absence records in 1,110 stream sites across France, our study aimed at (1) modeling current and future distributions of 35 stream fish species, (2) usin ..."
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Abstract.—Stream fish are expected to be influenced by climate change as they are ectothermic animals living in lotic systems. Using fish presence–absence records in 1,110 stream sites across France, our study aimed at (1) modeling current and future distributions of 35 stream fish species, (2) using an ensemble forecasting approach (i.e., several general circulation models [GCM] × greenhouse gas emission scenarios [GES] × statistical species distribution models [SDM] combinations) to quantify the variability in the future fish species distribution due to each component, and (3) assessing the potential impacts of climate change on fish species distribution and assemblage structure by using a consensus method that accounted for the variability in future projections. We found that future projections of fish species distribution were relatively consistent among GCM × GES × SDM combinations, with 57 % of the total variability between projections being consensual. The statistical method used was the main driver of the variability between future projections, accounting for 70 % of the total variation. The projections were next influenced by the GCMs, whereas
