Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China
Authors
Keywords
-
Journal
Forests
Volume 11, Issue 3, Pages 302
Publisher
MDPI AG
Online
2020-03-09
DOI
10.3390/f11030302
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms
- (2019) Li et al. Forests
- Maxent modeling for predicting the potential geographical distribution of two peony species under climate change
- (2018) Keliang Zhang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas
- (2017) Stephen E. Fick et al. INTERNATIONAL JOURNAL OF CLIMATOLOGY
- Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China
- (2017) Aili Qin et al. Global Ecology and Conservation
- Climate change impacts on endemic, high-elevation lichens in a biodiversity hotspot
- (2016) Jessica L. Allen et al. BIODIVERSITY AND CONSERVATION
- Climate Warming and Seasonal Precipitation Change Interact to Limit Species Distribution Shifts across Western North America
- (2016) Melanie A. Harsch et al. PLoS One
- Activity-specific ecological niche models for planning reintroductions of California condors ( Gymnogyps californianus )
- (2015) Jesse D’Elia et al. BIOLOGICAL CONSERVATION
- Mapping the potential distribution of the Critically Endangered Himalayan Quail Ophrysia superciliosa using proxy species and species distribution modelling
- (2015) JONATHON C. DUNN et al. BIRD CONSERVATION INTERNATIONAL
- Characteristics of the top-cited papers in species distribution predictive models
- (2015) Fabiana G. Barbosa et al. ECOLOGICAL MODELLING
- Forecasted coral reef decline in marine biodiversity hotspots under climate change
- (2015) Patrice Descombes et al. GLOBAL CHANGE BIOLOGY
- No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation
- (2015) Huijie Qiao et al. Methods in Ecology and Evolution
- A Process-Based Approach to Estimate Chinese Fir (Cunninghamia lanceolata) Distribution and Productivity in Southern China under Climate Change
- (2015) Yuhao Lu et al. Forests
- Potential distribution of invasive alien species in the upper Ili river basin: determination and mechanism of bioclimatic variables under climate change
- (2014) Zhonglin Xu Environmental Earth Sciences
- Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change
- (2014) Luciana L. Porfirio et al. PLoS One
- ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity forMaxentecological niche models
- (2014) Robert Muscarella et al. Methods in Ecology and Evolution
- Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern
- (2013) Dan L. Warren et al. DIVERSITY AND DISTRIBUTIONS
- Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills
- (2013) Xue-Qing Yang et al. ECOLOGICAL ENGINEERING
- Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes
- (2013) Mariya Shcheglovitova et al. ECOLOGICAL MODELLING
- Comparing mechanistic and empirical model projections of crop suitability and productivity: implications for ecological forecasting
- (2013) L. D. Estes et al. GLOBAL ECOLOGY AND BIOGEOGRAPHY
- Testing species distribution models across space and time: high latitude butterflies and recent warming
- (2013) Anne Eskildsen et al. GLOBAL ECOLOGY AND BIOGEOGRAPHY
- Process, correlation and parameter fitting in species distribution models: a response to Kriticoset al
- (2013) Stanislaus J. Schymanski et al. JOURNAL OF BIOGEOGRAPHY
- Extreme climatic event drives range contraction of a habitat-forming species
- (2013) D. A. Smale et al. PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
- How does selection of climate variables affect predictions of species distributions? A case study of three new weeds in New Zealand
- (2013) C S Sheppard WEED RESEARCH
- Correction of location errors for presence-only species distribution models
- (2013) Trevor J. Hefley et al. Methods in Ecology and Evolution
- Global carbon budgets simulated by the Beijing Climate Center Climate System Model for the last century
- (2013) Tongwen Wu et al. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
- Modeling the potential area of occupancy at fine resolution may reduce uncertainty in species range estimates
- (2012) Borja Jiménez-Alfaro et al. BIOLOGICAL CONSERVATION
- Assessing habitat suitability based on geographic information system (GIS) and fuzzy: A case study of Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China
- (2012) Chun Yan Lu et al. ECOLOGICAL MODELLING
- Using an ensemble of downscaled climate model projections to assess impacts of climate change on the potential distribution of spruce and Douglas-fir forests in British Columbia
- (2012) Aquila Flower et al. ENVIRONMENTAL SCIENCE & POLICY
- Do species distribution models predict species richness in urban and natural green spaces? A case study using amphibians
- (2012) Joseph R. Milanovich et al. LANDSCAPE AND URBAN PLANNING
- Significant Mean and Extreme Climate Sensitivity of Norway Spruce and Silver Fir at Mid-Elevation Mesic Sites in the Alps
- (2012) Marco Carrer et al. PLoS One
- Interdisciplinary approaches to understanding disease emergence: The past, present, and future drivers of Nipah virus emergence
- (2012) P. Daszak et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Vulnerability of 208 endemic or endangered species in China to the effects of climate change
- (2012) Xinhai Li et al. Regional Environmental Change
- Predicting the invasion risk by the alien bee-hawking Yellow-legged hornet Vespa velutina nigrithorax across Europe and other continents with niche models
- (2011) Claire Villemant et al. BIOLOGICAL CONSERVATION
- Use of niche models in invasive species risk assessments
- (2011) A. Jiménez-Valverde et al. BIOLOGICAL INVASIONS
- Does the interpolation accuracy of species distribution models come at the expense of transferability?
- (2011) Risto K. Heikkinen et al. ECOGRAPHY
- Climate change threatens European conservation areas
- (2011) Miguel B. Araújo et al. ECOLOGY LETTERS
- Incorporating uncertainty in predictive species distribution modelling
- (2011) C. M. Beale et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
- Predicted impact of exotic vines on an endangered ecological community under future climate change
- (2010) Rachael V. Gallagher et al. BIOLOGICAL INVASIONS
- Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau
- (2010) Lei Zhong et al. CLIMATIC CHANGE
- A statistical explanation of MaxEnt for ecologists
- (2010) Jane Elith et al. DIVERSITY AND DISTRIBUTIONS
- Measuring and comparing the accuracy of species distribution models with presence-absence data
- (2010) Canran Liu et al. ECOGRAPHY
- Predicting species distributions based on incomplete survey data: the trade-off between precision and scale
- (2010) Veronika Braunisch et al. ECOGRAPHY
- Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria
- (2010) Dan L. Warren et al. ECOLOGICAL APPLICATIONS
- Species Distribution Models: Ecological Explanation and Prediction Across Space and Time
- (2009) Jane Elith et al. Annual Review of Ecology Evolution and Systematics
- Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models
- (2009) Jane Elith et al. ECOGRAPHY
- Forest inventory in China: status and challenges
- (2009) X.D Lei et al. INTERNATIONAL FORESTRY REVIEW
- Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models
- (2009) Samuel D. Veloz JOURNAL OF BIOGEOGRAPHY
- Niches, models, and climate change: Assessing the assumptions and uncertainties
- (2009) J. A. Wiens et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Climatic extremes improve predictions of spatial patterns of tree species
- (2009) N. E. Zimmermann et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Biodiversity and Climate Change
- (2009) K. J. Willis et al. SCIENCE
- Effects of sample size on the performance of species distribution models
- (2008) M. S. Wisz et al. DIVERSITY AND DISTRIBUTIONS
- Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
- (2008) Steven J. Phillips et al. ECOGRAPHY
- Climate change, plant migration, and range collapse in a global biodiversity hotspot: the Banksia (Proteaceae) of Western Australia
- (2008) MATTHEW C. FITZPATRICK et al. GLOBAL CHANGE BIOLOGY
- A Significant Upward Shift in Plant Species Optimum Elevation During the 20th Century
- (2008) J. Lenoir et al. SCIENCE
- Integrating GIS-based environmental data into evolutionary biology
- (2008) Kenneth H. Kozak et al. TRENDS IN ECOLOGY & EVOLUTION
- AUC: a misleading measure of the performance of predictive distribution models
- (2007) Jorge M. Lobo et al. GLOBAL ECOLOGY AND BIOGEOGRAPHY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started