Aim Environmental niche models that utilize presence-only data have been increasingly employed to model species distributions and test ecological and evolutionary predictions. The ideal method for evaluating the accuracy of a niche model is to train a model with one dataset and then test model predictions against an independent dataset. However, a truly independent dataset is often not available, and instead random subsets of the total data are used for 'training' and 'testing' purposes. The goal of this study was to determine how spatially autocorrelated sampling affects measures of niche model accuracy when using subsets of a larger dataset for accuracy evaluation.Location The distribution of Centaurea maculosa (spotted knapweed; Asteraceae) was modelled in six states in the western United States: California, Oregon, Washington, Idaho, Wyoming and Montana.Methods Two types of niche modelling algorithms -the genetic algorithm for rule-set prediction (GARP) and maximum entropy modelling (as implemented with Maxent) -were used to model the potential distribution of C. maculosa across the region. The effect of spatially autocorrelated sampling was examined by applying a spatial filter to the presence-only data (to reduce autocorrelation) and then comparing predictions made using the spatial filter with those using a random subset of the data, equal in sample size to the filtered data. ResultsThe accuracy of predictions from both algorithms was sensitive to the spatial autocorrelation of sampling effort in the occurrence data. Spatial filtering led to lower values of the area under the receiver operating characteristic curve plot but higher similarity statistic (I) values when compared with predictions from models built with random subsets of the total data, meaning that spatial autocorrelation of sampling effort between training and test data led to inflated measures of accuracy. Main conclusionsThe findings indicate that care should be taken when interpreting the results from presence-only niche models when training and test data have been randomly partitioned but occurrence data were non-randomly sampled (in a spatially autocorrelated manner). The higher accuracies obtained without the spatial filter are a result of spatial autocorrelation of sampling effort between training and test data inflating measures of prediction accuracy. If independently surveyed data for testing predictions are unavailable, then it may be necessary to explicitly account for the spatial autocorrelation of sampling effort between randomly partitioned training and test subsets when evaluating niche model predictions.
Empirically derived species distributions models (SDMs) are increasingly relied upon to forecast species vulnerabilities to future climate change. However, many of the assumptions of SDMs may be violated when they are used to project species distributions across significant climate change events. In particular, SDM's in theory assume stable fundamental niches, but in practice, they assume stable realized niches. The assumption of a fixed realized niche relative to climate variables remains unlikely for various reasons, particularly if novel future climates open up currently unavailable portions of species' fundamental niches. To demonstrate this effect, we compare the climate distributions for fossil-pollen data from 21 to 15 ka BP (relying on paleoclimate simulations) when communities and climates with no modern analog were common across North America to observed modern pollen assemblages. We test how well SDMs are able to project 20th century pollen-based taxon distributions with models calibrated using data from 21 to 15 ka. We find that taxa which were abundant in areas with no-analog late glacial climates, such as Fraxinus, Ostrya/ Carpinus and Ulmus, substantially shifted their realized niches from the late glacial period to present. SDMs for these taxa had low predictive accuracy when projected to modern climates despite demonstrating high predictive accuracy for late glacial pollen distributions. For other taxa, e.g. Quercus, Picea, Pinus strobus, had relatively stable realized niches and models for these taxa tended to have higher predictive accuracy when projected to present. Our findings reinforce the point that a realized niche at any one time often represents only a subset of the climate conditions in which a taxon can persist. Projections from SDMs into future climate conditions that are based solely on contemporary realized distributions are potentially misleading for assessing the vulnerability of species to future climate change.
SignificanceExpansive development for urbanization, agriculture, and resource extraction has resulted in much of the Earth’s vegetation existing as fragmented, isolated patches. Conservation planning typically deprioritizes small, isolated patches, as they are assumed to be of relatively little ecological value, instead focusing attention on conserving large, highly connected areas. Yet, our global analysis shows that, if we gave up on small patches of vegetation, we would stand to lose many species that are confined to those environments, and biodiversity would decline as a result. We should rethink the way we prioritize conservation to recognize the critical role that small, isolated patches play in conserving the world’s biodiversity. Restoring and reconnecting small isolated vegetation patches should be an immediate conservation priority.
International audienceAimGlobal changes are predicted to have severe consequences for biodiversity; 34 biodiversity hotspots have become international priorities for conservation, with important efforts allocated to their preservation, but the potential effects of global changes on hotspots have so far received relatively little attention. We investigate whether hotspots are quantitatively and qualitatively threatened to the same order of magnitude by the combined effects of global changes. LocationWorldwide, in 34 biodiversity hotspots. MethodsWe quantify (1) the exposure of hotspots to climate change, by estimating the novelty of future climates and the disappearance of extant climates using climate dissimilarity analyses, (2) each hotspot's vulnerability to land modification and degradation by quantifying changes in land-cover variables over the entire habitat, and (3) the future suitability of distribution ranges of 100 of the world's worst invasive alien species', by characterizing the combined effects of climate and land-use changes on the future distribution ranges of these species. ResultsOur findings show that hotspots may experience an average loss of 31% of their area under analogue climate, with some hotspots more affected than others (e.g. Polynesia-Micronesia). The greatest climate change was projected in low-latitude hotspots. The hotspots were on average suitable for 17% of the considered invasive species. Hotspots that are mainly islands or groups of islands were disproportionally suitable for a high number of invasive species both currently and in the future. We also showed that hotspots will increase their area of pasture in the future. Finally, combining the three threats, we identified the Atlantic forest, Cape Floristic Region and Polynesia-Micronesia as particularly vulnerable to global changes. Main conclusionsGiven our estimates of hotspot vulnerability to changes, close monitoring is now required to evaluate the biodiversity responses to future changes and to test our projections against observations
Closely related species (e.g., sister taxa) often occupy very different ecological niches and can exhibit large differences in geographic distributions despite their shared evolutionary history. Budding speciation is one process that may partially explain how differences in niche and distribution characteristics may rapidly evolve. Budding speciation is the process through which new species form as initially small colonizing populations that acquire reproductive isolation. This mode of species formation predicts that, at the time of speciation, sister species should have highly asymmetrical distributions. We tested this hypothesis in North American monkeyflowers, a diverse clade with a robust phylogeny, using data on geographical ranges, climate, and plant community attributes. We found that recently diverged sister pairs have highly asymmetrical ranges and niche breadths, relative to older sister pairs. Additionally, we found that sister species occupy distinct environmental niche positions, and that 80% of sister species have completely or partially overlapping distributions (i.e., are broadly sympatric). Together, these results suggest that budding speciation has occurred frequently in Mimulus, that it has likely taken place both inside the range and on the range periphery, and that observed divergences in habitat and resource use could be associated with speciation in small populations. K E Y W O R D S :Climate niche, isolation, Mimulus, natural selection, phylogenetic, reproductive, sister pairs.
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