Niche models for British plants and lichens obtained using an ensemble approach
Peter A. HenrysSimon M. SmartE.C. RoweSusan G. JarvisZhou FangChris EvansBridget A. EmmettAdam Butler
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Abstract:
Site-occupancy models that predict habitat suitability for plant species in relation to measurable environmental factors can be useful for conservation planning. Such models can be derived from large-scale presence–absence datasets on the basis of environmental observations or, where only floristic data are available, using plant trait values averaged across a plot. However, the estimated modelled relationship between species presence and environmental variables depends on the type of statistical model adopted and hence can introduce additional uncertainty. Authors used an ensemble-modelling approach to constrain and quantify the uncertainty because of the choice of statistical model, applying generalised linear models (GLM), generalised additive models (GAM), and multivariate adaptive regression splines (MARS). Niche models were derived for over 1000 species of vascular plants, bryophytes and lichens, representing a large proportion of the British flora and many species occurring in continental Europe. Each model predicts habitat suitability for a species in response to climate variables and trait-based scores (evaluated excluding the species being modelled) for soil pH, fertility, wetness and canopy height. An R package containing the fitted models for each species is presented which allows the user to predict the habitat suitability of a given set of conditions for a particular species. Further functions within the package are included so that these habitat suitability scores can be plotted in relation to individual explanatory variables. A simple case study shows how the R package (MultiMOVE) can be used quickly and efficiently to answer questions of scientific interest, specifically whether climate change will counteract any benefits of sheep-grazing for a particular plant community. The package itself is freely available via http://doi.org/10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9.Keywords:
Environmental niche modelling
Occupancy
Species distribution
Trait
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Species distribution models are useful for estimating the distribution and environmental preferences of rare species, but these same species are challenging to model on account of sparse data. We contrast a traditional single‐species approach (generalized linear models, GLMs) with two promising frameworks for modeling rare species: ensembles of small models (ESMs), which average across simple models; and multi‐species distribution models (MSDMs), which allow rarer species to benefit from statistical ‘borrowing of strength' from more common species. Using a virtual species within a community of real species, we evaluated how model accuracy was influenced by the number of occurrences of the rare species (N = 2–64), niche breadth, and similarity to more numerous species' niches. For discriminating between presence and absence, ESMs with just linear terms (ESM‐L) performed best for N ≤ 4, whereas for GLMs and ESMs with polynomial terms (ESM‐P) were best for N ≥ 8. For calibrating the species' response to influential variables, the MSDM hierarchical modeling of species communities (HMSC) and ESM‐P were best for species with niches similar to those of other species. For species with dissimilar niches, ESM‐P did best for N ≥ 8, but no model was well calibrated for smaller sample sizes. For identifying uninfluential variables, ESM‐L and species archetype models (SAMs), a type of MSDM, did well for ≤ 4, and ESM‐L for N ≥ 8. Models of species with narrow niches dissimilar to others had the highest discrimination capacity compared to models for generalist species and/or species with niches similar to other species' niches. ‘Borrowing of strength' in MSDMs can assist with some inference tasks, but does not necessarily improve predictions for rare species; simpler, single‐species models may be better at a given task. The best algorithm depends on modeling goal (discrimination versus calibration), sample size, and niche breadth and similarity. Keywords: borrowing of strength, calibration, data‐deficient species, discrimination, presence–absence, rare species
Environmental niche modelling
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Rare species
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Species distribution
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Growing interest in biodiversity mapping has spurred the development of species distribution atlases, often mainly based on citizen-science projects. Atlas data have been frequently exploited to model species' ecological niches and distributions on a species-by-species basis. However, spatial autocorrelation and phylogenetic relatedness among species complicate the statistical description of species' niches. Also, the effects of species' traits and co-occurrences on species-habitat relationship are commonly disregarded. In this work, we build a hierarchical multi-species model based on a major citizen-science project (the third Spanish breeding bird atlas) that simultaneously accounts for spatial, phylogenetic and trait-based dependencies. We predict the distributions of species niches, species richness and community traits along regional ecological gradients. Climate, habitat associations and species' traits all contribute (in this order) to structuring species' distributions. Species richness increases towards intermediate climatic conditions and with aquatic habitat cover and decreases with increasing forest and woody agricultural land cover. Species were distributed along regional climate gradients in accordance with their global thermal niches. Forest habitats favoured assemblages dominated by generalist, small-sized and cold-dwelling species with limited migratory behaviour. Increasing sampling effort augmented the model performance. Model performance was weaker for rare species and those with decreasing population sizes, likely due to their low niche saturation. Overall, we show that ecological relationships generalize from local to large scales and may be eludicated from atlases based on citizen-science mapping efforts.
Environmental niche modelling
Species distribution
Breeding bird survey
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Environmental niche modelling
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Species distribution modeling is widely applied to predict invasive species distributions and species range shifts under climate change. Accurate predictions depend upon meeting the assumption that ecological niches are conserved, i.e., spatially or temporally transferable. Here we present a multi-taxon comparative analysis of niche conservatism using biological invasion events well documented in natural history museum collections. Our goal is to assess spatial transferability of the climatic niche of a range of noxious terrestrial invasive species using two complementary approaches. First we compare species' native versus invasive ranges in environmental space using two distinct methods, Principal Components Analysis and Mahalanobis distance. Second we compare species' native versus invaded ranges in geographic space as estimated using the species distribution modeling technique Maxent and the comparative index Hellinger's I. We find that species exhibit a range of responses, from almost complete transferability, in which the invaded niches completely overlap with the native niches, to a complete dissociation between native and invaded ranges. Intermediate responses included expansion of dimension attributable to either temperature or precipitation derived variables, as well as niche expansion in multiple dimensions. We conclude that the ecological niche in the native range is generally a poor predictor of invaded range and, by analogy, the ecological niche may be a poor predictor of range shifts under climate change. We suggest that assessing dimensions of niche transferability prior to standard species distribution modeling may improve the understanding of species' dynamics in the invaded range.
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Environmental niche modelling
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Abstract Aim We introduce and evaluate the potential effect of spatially marginal localities (specifically those protruding into unsuitable regions), in overestimating species niches and distributions when using ecological niche models ( ENM s). Location North‐western South America. Methods We built an ENM for the Caribbean spiny pocket mouse ( Heteromys anomalus ) using MaxEnt and climatic variables. This species typically inhabits extensive tropical forests but can also range into drier habitats through patches of mesic vegetation. We ranked occurrence records according to the suitability value they received, and retrieved habitat information from collectors' field notes and the literature to determine whether those receiving lower values correspond to spatially marginal localities protruding into unsuitable regions. We then built a model excluding a subset of such localities and compared its geographic and environmental prediction with that of the original model. Results Models differed substantially in their estimates of suitability. The original model resulted in an overly extensive prediction, considering as suitable hot and dry regions dominated by xerophytic vegetation. Records receiving the lowest suitability values in this model corresponded mainly to captures in patches of mesic forest surrounded by thorn scrub or savannas. The model calibrated without such records restricted suitability mostly to regions characterized by the typical habitat of the species. Main conclusions When it is not possible to use variables that are more proximal or have finer resolutions, we recommend building complementary models that, together, can provide a more realistic estimate of the species' niche and corresponding geographic distribution. Jointly interpreting these models, researchers may better differentiate between areas harbouring typical habitat and those where the species can be found only if locally favourable conditions exist. Such a distinction is of relevance for a wide range of applications relying on inferences obtained from ENM s.
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Environmental niche modelling is an acclaimed method for estimating species' present or future distributions. However, in marine environments the assembly of representative data from reliable and unbiased occurrences is challenging. Here, we aimed to model the environmental niche and distribution of marine, parasitic nematodes from the Pseudoterranova decipiens complex using the software Maxent. The distribution of these potentially zoonotic species is of interest, because they infect the muscle tissue of host species targeted by fisheries. To achieve the best possible model, we used two different approaches. The land distance (LD) model was based on abiotic data, whereas the definitive host distance (DHD) model included species-specific biotic data. To assess whether DHD is a suitable descriptor for Pseudoterranova spp., the niches of the parasites and their respective definitive hosts were analysed using ecospat. The performance of LD and DHD was compared based on the variables' contribution to the model. The DHD-model clearly outperformed the LD-model. While the LD-model gave an estimate of the parasites' niches, it only showed the potential distribution. The DHD-model produced an estimate of the species' realised distribution and indicated that biotic variables can help to improve the modelling of data-poor, marine species.
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Non‐native species can have severe impacts on ecosystems. Therefore, predictions of potentially suitable areas that are at risk of the establishment of non‐native populations are desirable. In recent years, species distribution models ( SDM s) have been widely applied for this purpose. However, the appropriate selection of species records, whether from the native area alone or also from the introduced range, is still a matter of debate. We combined analyses of native and non‐native realized climate niches to understand differences between models based on all locations, as well as on locations from the native range only. Our approach was applied to four estrildid finch species that have been introduced to many regions around the world. Our results showed that SDM s based on location data from native areas alone may underestimate the potential distribution of a given species. The climatic niches of species in their native ranges differed from those of their non‐native ranges. Niche comparisons resulted in low overlap values, indicating considerable niche shifts, at least in the realized niches of these species. All four species have high potential to spread over many tropical and subtropical areas. However, transferring these results to temperate areas has a high degree of uncertainty, and we urge caution when assessing the potential spread of tropical species that have been introduced to higher latitudes.
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Abstract Aim To explore whether the subspecific genetic entities of Acacia saligna occupy different bioclimatic niches in their native and introduced ranges and whether these niches are predictable using species distribution models (SDMs). Location Australia, South Africa and the Mediterranean Basin. Methods Species distribution models were developed in MAXENT using six climatic variables to calculate the climatic suitability of the ranges of A. saligna . We assessed (1) the subspecific niche differences identified by SDMs using measures of niche overlap and model performance; (2) the ability of SDMs to predict the most likely subspecific genetic entities present in South Africa based on comparisons to genetic data; and (3) the ability of SDMs to predict the most likely subspecific genetic entities present in the Mediterranean Basin. All model projections were assessed for sensitivity and modelled prevalence as indicators of model fit and predictability. Results The SDMs identified different subspecific bioclimatic niches in the native range. Sensitivity and modelled prevalence show that none of the models correctly predicted the full range of A. saligna in South Africa or the Mediterranean Basin. Models also show that the South African niche is different to that in the native range. Main conclusions Subspecies of A. saligna occupy quantifiably distinct bioclimatic niches in their native ranges, implying that they should occupy distinct niches in their invasive ranges. However, projections to the introduced range did not correspond with known occurrences. Our SDMs are unable to predict the full introduced niche of A. saligna at a species or subspecies level in either South Africa or the Mediterranean Basin. Range limits in the native and introduced ranges may be determined by additional factors not used in the SDMs developed in this study.
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Mediterranean Basin
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