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Optimising Species Distribution Models: Sample size, positional error, and sampling bias matter
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Why georeferencing matters: Introducing a practical protocol to prepare species occurrence records for spatial analysis
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Comparing sample bias correction methods for species distribution modeling using virtual species
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The GEO Handbook on Biodiversity Observation Networks
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Biodiversity Modelling as Part of an Observation System
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Effects of sample size, data quality, and species response in environmental space on modeling species distributions
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Evaluating how species niche modelling is affected by partial distributions with an empirical case
inferredcites →https://doi.org/10.1016/j.biocon.2019.05.053openalex-snapshot
Gaps in butterfly inventory data: A global analysis
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Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
inferredcites →https://doi.org/10.1016/j.ecoinf.2020.101051openalex-snapshot
Quality and usability challenges of global marine biodiversity databases: An example for marine mammal data
inferredcites →https://doi.org/10.1016/j.ecoinf.2021.101369openalex-snapshot
Is geographic sampling bias representative of environmental space?
inferredcites →https://doi.org/10.1016/j.ecoinf.2023.102106openalex-snapshot
Small to train, small to test: Dealing with low sample size in model evaluation
inferredcites →https://doi.org/10.1016/j.ecolind.2016.11.006openalex-snapshot
Which breeding bird categories should we use in models of species distribution?
inferredcites →https://doi.org/10.1016/j.ecolind.2020.106289openalex-snapshot
Sample size for the evaluation of presence-absence models
inferredcites →https://doi.org/10.1016/j.ecolmodel.2004.12.012openalex-snapshot
Species-specific traits associated to prediction errors in bird habitat suitability modelling
inferredcites →https://doi.org/10.1016/j.ecolmodel.2006.05.024openalex-snapshot
The derivation of species response curves with Gaussian logistic regression is sensitive to sampling intensity and curve characteristics
inferredcites →https://doi.org/10.1016/j.ecolmodel.2007.11.013openalex-snapshot
Sensitivity of species-distribution models to error, bias, and model design: An application to resource selection functions for woodland caribou
inferredcites →https://doi.org/10.1016/j.ecolmodel.2011.01.018openalex-snapshot
What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods
inferredcites →https://doi.org/10.1016/j.ecolmodel.2011.06.015openalex-snapshot
Incorporating low-resolution historic species location data decreases performance of distribution models
inferredcites →https://doi.org/10.1016/j.ecolmodel.2011.12.001openalex-snapshot
Sample sizes and model comparison metrics for species distribution models
inferredcites →https://doi.org/10.1016/j.ecolmodel.2015.05.035openalex-snapshot
Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data
inferredcites →https://doi.org/10.1016/j.ecolmodel.2016.03.012openalex-snapshot
On the dangers of model complexity without ecological justification in species distribution modeling
inferredcites →https://doi.org/10.1016/j.ecolmodel.2017.06.006openalex-snapshot
Accounting for uncertainty in predictions of a marine species: Integrating population genetics to verify past distributions
inferredcites →https://doi.org/10.1016/j.ecolmodel.2018.07.018openalex-snapshot
Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions
inferredcites →https://doi.org/10.1016/j.ecolmodel.2021.109671openalex-snapshot
Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling
inferredcites →https://doi.org/10.1016/j.foreco.2017.12.046openalex-snapshot
Optimization of forest sampling strategies for woody plant species distribution modelling at the landscape scale
inferredcites →https://doi.org/10.1016/j.tree.2004.07.006openalex-snapshot
New developments in museum-based informatics and applications in biodiversity analysis
inferredcites →https://doi.org/10.1016/s0304-3800(01)00388-xopenalex-snapshot
Effects of sample size on accuracy of species distribution models
inferredcites →https://doi.org/10.1016/s0304-3800(02)00202-8openalex-snapshot
Mapping epistemic uncertainties and vague concepts in predictions of species distribution
inferredcites →https://doi.org/10.1016/s0304-3800(02)00203-xopenalex-snapshot
Which is the optimal sampling strategy for habitat suitability modelling
inferredcites →https://doi.org/10.1016/s0304-3800(02)00205-3openalex-snapshot
Spatial prediction of species distribution: an interface between ecological theory and statistical modelling
inferredcites →https://doi.org/10.1029/2021gl093455openalex-snapshot
Testing the Skill of a Species Distribution Model Using a 21st Century Virtual Ecosystem
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Predicting distributions of known and unknown reptile species in Madagascar
inferredcites →https://doi.org/10.1038/s41467-018-07217-7openalex-snapshot
Mapping knowledge gaps in marine diversity reveals a latitudinal gradient of missing species richness
inferredcites →https://doi.org/10.1038/s41559-019-0972-5openalex-snapshot
A checklist for maximizing reproducibility of ecological niche models
inferredcites →https://doi.org/10.1038/s41559-022-01778-zopenalex-snapshot
Stop ignoring map uncertainty in biodiversity science and conservation policy
inferredcites →https://doi.org/10.1038/s41559-023-02047-3openalex-snapshot
Mass production of unvouchered records fails to represent global biodiversity patterns
inferredcites →https://doi.org/10.1038/s41598-017-09084-6openalex-snapshot
Taxonomic bias in biodiversity data and societal preferences
inferredcites →https://doi.org/10.1038/s44185-023-00014-6openalex-snapshot
A quixotic view of spatial bias in modelling the distribution of species and their diversity
inferredcites →https://doi.org/10.1080/01490419.2013.877105openalex-snapshot
Quantification of Spatial and Thematic Uncertainty in the Application of Underwater Video for Benthic Habitat Mapping
inferredcites →https://doi.org/10.1080/13658810412331280211openalex-snapshot
The point-radius method for georeferencing locality descriptions and calculating associated uncertainty
inferredcites →https://doi.org/10.1080/13658816.2010.531020openalex-snapshot
Effects of geographical data sampling bias on habitat models of species distributions: a case study with steppe birds in southern Portugal
inferredcites →https://doi.org/10.1080/13658816.2012.721553openalex-snapshot
Influence of positional accuracy, sample size and scale on modelling species distributions: a review
inferredcites →https://doi.org/10.1080/13658816.2014.891222openalex-snapshot
How many predictors in species distribution models at the landscape scale? Land use versus LiDAR-derived canopy height
inferredcites →https://doi.org/10.1080/13658816.2014.902062openalex-snapshot
Spatial structure analysis of a reptile community with airborne LiDAR data
inferredcites →https://doi.org/10.1080/13658816.2019.1615070openalex-snapshot
How do species and data characteristics affect species distribution models and when to use environmental filtering?
inferredcites →https://doi.org/10.1080/13658816.2020.1798968openalex-snapshot
Common mistakes in ecological niche models
inferredcites →https://doi.org/10.1080/14772000.2012.705357openalex-snapshot
Sampling bias in geographic and environmental space and its effect on the predictive power of species distribution models
inferredcites →https://doi.org/10.1098/rstbopenalex-snapshot
Philosophical Transactions of the Royal Society B: biological sciences
inferredcites →https://doi.org/10.1098/rstb.2010.0084openalex-snapshot
Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data
inferredcites →https://doi.org/10.1101/2023.07.31.551237openalex-snapshot
Assessing the impact of binary land cover variables on species distribution models: A North American study on water birds
inferredcites →https://doi.org/10.1111/(issn)1095-8312openalex-snapshot
Biological Journal of the Linnean Society
inferredcites →https://doi.org/10.1111/2041-210x.12144openalex-snapshot
Correction of location errors for presence‐only species distribution models
inferredcites →https://doi.org/10.1111/2041-210x.12203openalex-snapshot
Measuring the relative effect of factors affecting species distribution model predictions
inferredcites →https://doi.org/10.1111/2041-210x.12645openalex-snapshot
Sensitivity of fine‐scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes
inferredcites →https://doi.org/10.1111/2041-210x.13142openalex-snapshot
Environmental filtering improves ecological niche models across multiple scales
inferredcites →https://doi.org/10.1111/2041-210x.13956openalex-snapshot
Positional errors in species distribution modelling are not overcome by the coarser grains of analysis
inferredcites →https://doi.org/10.1111/2041-210x.14209openalex-snapshot
USE it: Uniformly sampling pseudo‐absences within the environmental space for applications in habitat suitability models
inferredcites →https://doi.org/10.1111/acv.12698openalex-snapshot
Small‐scale species distribution model identifies restricted breeding habitat for an endemic island bird
inferredcites →https://doi.org/10.1111/bij.12532openalex-snapshot
Bias and information in biological records
inferredcites →https://doi.org/10.1111/ddi.12096openalex-snapshot
The importance of correcting for sampling bias in MaxEnt species distribution models
inferredcites →https://doi.org/10.1111/ddi.12199openalex-snapshot
Uncertainty, priors, autocorrelation and disparate data in downscaling of species distributions
inferredcites →https://doi.org/10.1111/ddi.12236openalex-snapshot
Uncertainty associated with survey design in Species Distribution Models
inferredcites →https://doi.org/10.1111/ddi.12477openalex-snapshot
What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements
inferredcites →https://doi.org/10.1111/ddi.13030openalex-snapshot
Deciphering ecology from statistical artefacts: Competing influence of sample size, prevalence and habitat specialization on species distribution models and how small evaluation datasets can inflate metrics of performance
inferredcites →https://doi.org/10.1111/ddi.13252openalex-snapshot
Assessing the reliability of species distribution projections in climate change research
inferredcites →https://doi.org/10.1111/ecog.00845openalex-snapshot
What do we gain from simplicity versus complexity in species distribution models?
inferredcites →https://doi.org/10.1111/ecog.01205openalex-snapshot
Using measurement error models to account for georeferencing error in species distribution models
inferredcites →https://doi.org/10.1111/ecog.01509openalex-snapshot
Minimum required number of specimen records to develop accurate species distribution models
inferredcites →https://doi.org/10.1111/ecog.02414openalex-snapshot
Performance tradeoffs in target‐group bias correction for species distribution models
inferredcites →https://doi.org/10.1111/ecog.02671openalex-snapshot
ecospat: an R package to support spatial analyses and modeling of species niches and distributions
inferredcites →https://doi.org/10.1111/ecog.02871openalex-snapshot
Disentangling scale dependencies in species environmental niches and distributions
inferredcites →https://doi.org/10.1111/ecog.03188openalex-snapshot
The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo‐absences or background sites
inferredcites →https://doi.org/10.1111/ecog.04687openalex-snapshot
The effect of positional error on fine scale species distribution models increases for specialist species
inferredcites →https://doi.org/10.1111/ecog.04960openalex-snapshot
A standard protocol for reporting species distribution models
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sampbias , a method for quantifying geographic sampling biases in species distribution data
inferredcites →https://doi.org/10.1111/ecog.05317openalex-snapshot
Testing the ability of species distribution models to infer variable importance
inferredcites →https://doi.org/10.1111/ecog.05793openalex-snapshot
Using maps of biogeographical ignorance to reveal the uncertainty in distributional data hidden in species distribution models
inferredcites →https://doi.org/10.1111/ecog.05926openalex-snapshot
Sampling biases shape our view of the natural world
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Uncertainty matters: ascertaining where specimens in natural history collections come from and its implications for predicting species distributions
inferredcites →https://doi.org/10.1111/ecog.06358openalex-snapshot
Species distribution models affected by positional uncertainty in species occurrences can still be ecologically interpretable
inferredcites →https://doi.org/10.1111/ecog.06721openalex-snapshot
Ecological and biological indicators of the accuracy of species distribution models: lessons from European bryophytes
inferredcites →https://doi.org/10.1111/ecog.07115openalex-snapshot
Big data, big problems? How to circumvent problems in biodiversity mapping and ensure meaningful results
inferredcites →https://doi.org/10.1111/ele.12189openalex-snapshot
Predicting species distributions for conservation decisions
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Predicting changes in the distribution and abundance of species under environmental change
inferredcites →https://doi.org/10.1111/ele.14312openalex-snapshot
Microclimate reveals the true thermal niche of forest plant species
inferredcites →https://doi.org/10.1111/geb.12118openalex-snapshot
Where are the wild things? Why we need better data on species distribution
inferredcites →https://doi.org/10.1111/geb.12268openalex-snapshot
Is my species distribution model fit for purpose? Matching data and models to applications
inferredcites →https://doi.org/10.1111/geb.12684openalex-snapshot
Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics
inferredcites →https://doi.org/10.1111/geb.13346openalex-snapshot
sPlotOpen – An environmentally balanced, open‐access, global dataset of vegetation plots
inferredcites →https://doi.org/10.1111/geb.13383openalex-snapshot
Novel methods to correct for observer and sampling bias in presence‐only species distribution models
inferredcites →https://doi.org/10.1111/geb.13491openalex-snapshot
Correlations between spatial sampling biases and environmental niches affect species distribution models
inferredcites →https://doi.org/10.1111/geb.13628openalex-snapshot
Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth
inferredcites →https://doi.org/10.1111/geb.13725openalex-snapshot
Sampling strategy matters to accurately estimate response curves' parameters in species distribution models
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An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo‐absence data
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The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?
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Historical bias in biodiversity inventories affects the observed environmental niche of the species
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The effect of sample size and species characteristics on performance of different species distribution modeling methods
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Effects of species? ecology on the accuracy of distribution models
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Error and uncertainty in habitat models
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The influence of spatial errors in species occurrence data used in distribution models
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An evaluation of methods for modelling species distributions
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Improving generalized regression analysis for the spatial prediction of forest communities
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Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?
inferredcites →https://doi.org/10.1111/j.1365-2699.2009.02174.xopenalex-snapshot
Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models
inferredcites →https://doi.org/10.1111/j.1365-2699.2011.02523.xopenalex-snapshot
Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling
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Impacts of imperfect reference data on the apparent accuracy of species presence-absence models and their predictions
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A comparative evaluation of presence‐only methods for modelling species distribution
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Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents
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Effects of sample size on the performance of species distribution models
inferredcites →https://doi.org/10.1111/j.1472-4642.2009.00572.xopenalex-snapshot
Effects of species and habitat positional errors on the performance and interpretation of species distribution models
inferredcites →https://doi.org/10.1111/j.1472-4642.2011.00813.xopenalex-snapshot
Keep collecting: accurate species distribution modelling requires more collections than previously thought
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Predicting Species Distributions from Samples Collected along Roadsides
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Local Indicators of Spatial Association—LISA
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The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models
inferredcites →https://doi.org/10.1111/j.1600-0587.2013.00205.xopenalex-snapshot
Where is positional uncertainty a problem for species distribution modelling?
inferredcites →https://doi.org/10.1111/j.1600-0587.2013.00321.xopenalex-snapshot
Inference from presence‐only data; the ongoing controversy
inferredcites →https://doi.org/10.1111/j.1600-0587.2013.00441.xopenalex-snapshot
Environmental filters reduce the effects of sampling bias and improve predictions of ecological niche models
inferredcites →https://doi.org/10.1111/j.1654-1103.2010.01198.xopenalex-snapshot
Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity
inferredcites →https://doi.org/10.1111/jbi.12552openalex-snapshot
Modelling species distributions with simulated virtual species
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Phytogeography of New Guinean orchids: patterns of species richness and turnover
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Macroecology in the age of Big Data – Where to go from here?
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Effects of input data sources on species distribution model predictions across species with different distributional ranges
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Ecological niche models improve home range estimations
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A heuristic‐based approach to mitigating positional errors in patrol data for species distribution modeling
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Standards for distribution models in biodiversity assessments
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Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity
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Species Distribution Models: Ecological Explanation and Prediction Across Space and Time
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Annual Review of Ecology, Evolution, and Systematics
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Methods and uncertainties in bioclimatic envelope modelling under climate change
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Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models
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Accounting for uncertainty when mapping species distributions: The need for maps of ignorance
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Scale mismatches between predictor and response variables in species distribution modelling: A review of practices for appropriate grain selection
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Distorted Views of Biodiversity: Spatial and Temporal Bias in Species Occurrence Data
inferredcites →https://doi.org/10.1371/journal.pone.0055158openalex-snapshot
The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models
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Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias
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The effect of prevalence and its interaction with sample size on the reliability of species distribution models
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LOCALITY UNCERTAINTY AND THE DIFFERENTIAL PERFORMANCE OF FOUR COMMON NICHE-BASED MODELING TECHNIQUES
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ECOLOGICAL-NICHE FACTOR ANALYSIS: HOW TO COMPUTE HABITAT-SUITABILITY MAPS WITHOUT ABSENCE DATA?
inferredcites →https://doi.org/10.1890/02-5364openalex-snapshot
EFFECT OF ROADSIDE BIAS ON THE ACCURACY OF PREDICTIVE MAPS PRODUCED BY BIOCLIMATIC MODELS
inferredcites →https://doi.org/10.1890/06-1060.1openalex-snapshot
WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS?
inferredcites →https://doi.org/10.1890/07-2153.1openalex-snapshot
Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data
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A SYSTEMATIC ANALYSIS OF FACTORS AFFECTING THE PERFORMANCE OF CLIMATIC ENVELOPE MODELS
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How much is enough? Influence of number of presence observations on the performance of species distribution models
inferredcites →https://doi.org/10.32942/osf.io/68atsopenalex-snapshot
Causal inference and large-scale expert validation shed light on the drivers of SDM accuracy and variance
inferredcites →https://doi.org/10.3354/meps11378openalex-snapshot
Spatial scale and geographic context in benthic habitat mapping: review and future directions
inferredcites →https://doi.org/10.3389/fevo.2022.944116openalex-snapshot
Accounting for niche truncation to improve spatial and temporal predictions of species distributions
inferredcites →https://doi.org/10.48550/arxiv.2308.05121openalex-snapshot
The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe
inferredcites →https://doi.org/10.7287/peerj.preprints.856openalex-snapshot
Global priorities for an effective information basis of biodiversity distributions
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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An updated checklist of the liverworts and hornworts of southern Africa
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Geodiversity and biogeomorphology: Predicting species distribution, biodiversity changes, and climate adaptation
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Spatially‐nested hierarchical species distribution models to overcome niche truncation in national‐scale studies
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Rising Temperatures, Falling Leaves: Predicting the Fate of Cyprus’s Endemic Oak under Climate and Land Use Change
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Modeling the Distribution and Richness of Mammalian Species in the Nyerere National Park, Tanzania
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