Comparison of a species distribution model and a process model from a hierarchical perspective to quantify effects of projected climate change on tree species

2015 ◽  
Vol 30 (10) ◽  
pp. 1879-1892 ◽  
Author(s):  
Jeffrey E. Schneiderman ◽  
Hong S. He ◽  
Frank R. Thompson ◽  
William D. Dijak ◽  
Jacob S. Fraser
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Tree Species ◽  
Process Model ◽  
Species Distribution Model ◽  
Distribution Model

Climate change in the Missouri central hardwood region : consequences for forest landscapes, and management strategies

2015 ◽  
Author(s):  
◽  
Jeffrey Eric Schneiderman
Keyword(s):  
Climate Change ◽  
Tree Species ◽  
Process Model ◽  
Species Distribution Model ◽  
Management Strategies ◽  
Economic Value ◽  
Climate Change Impacts ◽  
Timber Harvest ◽  
Distribution Model ◽  
The Impact

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Climate change may result in a change in the tree species present within forests. The Missouri Central Hardwood Region represents one area where these changes may occur. Due to the natural diversity of species and economic value of this area, it is beneficial to understand how climate change might affect trees currently present. Computer models (a human creation to help understand real world systems in a simplified manner) can be used to study the impact of climate change on forests. My objectives were to 1) understand how different forest impact models studying climate change compared to each other, 2) determine whether climate change or current timber harvest practices was more likely to change the characteristics of the forest, and 3) analyze land management alternatives to determine which was best at creating beneficial qualities for forests under climate change. For my first objective, I used a species distribution model and a process model, two models that use different analysis approaches, to assess climate change impacts on tree species, and compared the results. On a broad level, both models agreed, but when looking at the study area at smaller resolution, the models did not agree as well. For my second objective, I coupled a process model and forest landscape model. Although results showed there was variation based on species regarding whether climate or harvest had the greater impact, both usually had significant impacts on tree species. Results for my third objective indicated that multiple management approaches are necessary to manage future forests in a beneficial manner. My results have implications for future forest sustainability. Uncertainty exists regarding climate change's full impact, but with proper forest management and research these challenges can be reduced.

scholarly journals Species Favourability Shift in Europe due to Climate Change: A Case Study for Fagus sylvatica L. and Picea abies (L.) Karst. Based on an Ensemble of Climate Models

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Wolfgang Falk ◽  
Nils Hempelmann
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Climate Models ◽  
Species Distribution Model ◽  
Regional Climate ◽  
European Beech ◽  
Regional Climate Models ◽  
Distribution Model ◽  
Continental Scale ◽  
The North

Climate is the main environmental driver determining the spatial distribution of most tree species at the continental scale. We investigated the distribution change of European beech and Norway spruce due to climate change. We applied a species distribution model (SDM), driven by an ensemble of 21 regional climate models in order to study the shift of the favourability distribution of these species. SDMs were parameterized for 1971–2000, as well as 2021–2050 and 2071–2100 using the SRES scenario A1B and three physiological meaningful climate variables. Growing degree sum and precipitation sum were calculated for the growing season on a basis of daily data. Results show a general north-eastern and altitudinal shift in climatological favourability for both species, although the shift is more marked for spruce. The gain of new favourable sites in the north or in the Alps is stronger for beech compared to spruce. Uncertainty is expressed as the variance of the averaged maps and with a density function. Uncertainty in species distribution increases over time. This study demonstrates the importance of data ensembles and shows how to deal with different outcomes in order to improve impact studies by showing uncertainty of the resulting maps.

scholarly journals Predicting the Current and Future Suitable Habitat Distribution of Swimming Crab (Portunus trituberculatus) using the Species Distribution Model under Climate Change

2021 ◽  
Author(s):  
Xinyu Liu ◽  
Xiaolu Han ◽  
Zhiqiang Han
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Seawater Temperature ◽  
The Yellow Sea ◽  
Distribution Model ◽  
Portunus Trituberculatus ◽  
Suitable Habitat ◽  
Different Seasons ◽  
Rcp 8.5

Species have shown their habital variations in responding to climate change, especially during the spring and summer spawning seasons. The species distribution model (SDM) is considered the most favorable tool to study the potential effects of climate change on species distribution. Therefore, we developed the ensemble SDM to predict the changes in species distribution of Portunus trituberculatus among different seasons in 2050 and 2100 under the climate scenarios RCP4.5 and RCP8.5. The results of SDM indicate that the distribution of this species will move northward and have obviouse seasonal variations. Meanwhile, the suitable habitat for the species will be significantly reduced in summer, with loses rates ranging from 45.23% (RCP4.5) to 88.26% (RCP.8.5) by 2100s. Habitat reduction will mainly occur in the East China Sea and southern part of the Yellow Sea, while there will be a small increase in the northern Bohai Sea. These findings will be important to manage the ecosystem and fishery, provide an information forecast of this species in the future, and maintain species diversity if the seawater temperature rises.

scholarly journals Colonization and extinction dynamics and their link to the distribution of European trees at continental scale

2019 ◽  
Author(s):  
Arnaud Guyennon ◽  
Björn Reineking ◽  
Jonas Dahlgren ◽  
Aleksi Lehtonen ◽  
Sophia Ratcliffe ◽  
...  
Keyword(s):  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Metapopulation Dynamics ◽  
Occurrence Probability ◽  
Distribution Models ◽  
Continental Scale ◽  
Forest Inventories ◽  
National Forest Inventories

AbstractAimProcesses driving current tree species distribution are still largely debated. Attempts to relate species distribution and population demography metrics have shown mixed results. In this context, we would like to test the hypotheses that the metapopulation processes of colonization and extinction are linked to species distribution models.LocationEurope: Spain, France, Germany, Finland, and Sweden.TaxonAngiosperms and Gymnosperms.MethodsFor the 17 tree species analyzed we fitted species distribution model (SDM) relating environmental variables to presence absence data across Europe. Then using independent data from national forest inventories across Europe we tested whether colonization and extinction probabilities are related to occurrence probability estimated by the SDMs. Finally, we tested how colonization and extinction respectively drive probability of presence at the metapopulation equilibrium.ResultsWe found that for most species at least one process (colonization/extinction) is related to the occurrence probability, but rarely both.Main conclusionsOur study supports the view that metapopulation dynamics are partly related to SDM occurrence probability through one of the metapopulation probabilities. However these links are relatively weak and the metapopulation models tend to overestimate the occurrence probability. Our results call for caution in model extrapolating SDM models to metapopulation dynamics.

scholarly journals Limitations of Species Distribution Models Based on Available Climate Change Data: A Case Study in the Azorean Forest

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 575 ◽  
Author(s):  
Lara Dutra Silva ◽  
Eduardo Brito de Azevedo ◽  
Francisco Vieira Reis ◽  
Rui Bento Elias ◽  
Luís Silva
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Interactions ◽  
Species Distribution Model ◽  
Selection Process ◽  
Additive Models ◽  
Machine Learning Techniques ◽  
Integrated Modelling ◽  
Distribution Model ◽  
Careful Study

Climate change is gaining attention as a major threat to biodiversity. It is expected to further expand the risk of plant invasion through ecosystem disturbance. Particularly, island ecosystems are under pressure, and climate change may threaten forest-dependent species. However, scientific and societal unknowns make it difficult to predict how climate change and biological invasions will affect species interactions and ecosystem processes. The purpose of this study was to identify possible limitations when making species distribution model projections based on predicted climate change. We aimed to know if climatic variables alone were good predictors of habitat suitability, ensuring reliable projections. In particular, we compared the performance of generalized linear models, generalized additive models, and a selection of machine learning techniques (BIOMOD 2) when modelling the distribution of forest species in the Azores, according to the climatic changes predicted to 2100. Some limitations seem to exist when modelling the effect of climate change on species distributions, since the best models also included topographic variables, making modelling based on climate alone less reliable, with model fit varying among modelling approaches, and random forest often providing the best results. Our results emphasize the adoption of a careful study design and algorithm selection process. The uncertainties associated with climate change effect on plant communities as a whole, including their indigenous and invasive components, highlight a pressing need for integrated modelling, monitoring, and experimental work to better realize the consequences of climate change, in order to ensure the resilience of forest ecosystems in a changing world.

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