scholarly journals Impact of spatial configuration of training data on the performance of Amazonian tree species distribution models

2022 ◽  
Vol 504 ◽  
pp. 119838
Author(s):  
Pablo Pérez Chaves ◽  
Kalle Ruokolainen ◽  
Jasper Van doninck ◽  
Hanna Tuomisto
Keyword(s):  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Spatial Configuration ◽  
Training Data ◽  
Distribution Models

Do species distribution models explain spatial structure within tree species ranges?

2009 ◽  
Vol 18 (6) ◽  
pp. 662-673 ◽  
Author(s):  
Daniel Montoya ◽  
Drew W. Purves ◽  
Itziar R. Urbieta ◽  
Miguel A. Zavala
Keyword(s):  
Spatial Structure ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Species Ranges

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

2011 ◽  
Vol 4 (4) ◽  
pp. 390-401 ◽  
Author(s):  
Gary N. Ervin ◽  
D. Christopher Holly
Keyword(s):  
Invasive Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Imperata Cylindrica ◽  
Environmental Data ◽  
Training Data ◽  
Distribution Data ◽  
Strongly Correlated ◽  
Distribution Models ◽  
Tree Canopy Cover

AbstractSpecies distribution modeling is a tool that is gaining widespread use in the projection of future distributions of invasive species and has important potential as a tool for monitoring invasive species spread. However, the transferability of models from one area to another has been inadequately investigated. This study aimed to determine the degree to which species distribution models (SDMs) for cogongrass, developed with distribution data from Mississippi (USA), could be applied to a similar area in neighboring Alabama. Cogongrass distribution data collected in Mississippi were used to train an SDM that was then tested for accuracy and transferability with cogongrass distribution data collected by a forest management company in Alabama. Analyses indicated the SDM had a relatively high predictive ability within the region of the training data but had poor transferability to the Alabama data. Analysis of the Alabama data, via independent SDM development, indicated that predicted cogongrass distribution in Alabama was more strongly correlated with soil variables than was the case in Mississippi, where the SDM was most strongly correlated with tree canopy cover. Results suggest that model transferability is influenced strongly by (1) data collection methods, (2) landscape context of the survey data, and (3) variations in qualitative aspects of environmental data used in model development.

Hotspots of change in major tree species under climate warming

2020 ◽  
Author(s):  
Flurin Babst ◽  
Richard L. Peters ◽  
Rafel O. Wüest ◽  
Margaret E.K. Evans ◽  
Ulf Büntgen ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Sensitivity ◽  
Tree Growth ◽  
Species Distribution ◽  
Tree Species ◽  
Habitat Suitability ◽  
Species Distribution Models ◽  
Sensitivity Index ◽  
Distribution Models ◽  
Bioclimatic Variables

<p>Warming alters the variability and trajectories of tree growth around the world by intensifying or alleviating energy and water limitation. This insight from regional to global-scale research emphasizes the susceptibility of forest ecosystems and resources to climate change. However, globally-derived trends are not necessarily meaningful for local nature conservation or management considerations, if they lack specific information on present or prospective tree species. This is particularly the case towards the edge of their distribution, where shifts in growth trajectories may be imminent or already occurring.</p><p>Importantly, the geographic and bioclimatic space (or “niche”) occupied by a tree species is not only constrained by climate, but often reflects biotic pressure such as competition for resources with other species. This aspect is underrepresented in many species distribution models that define the niche as a climatic envelope, which is then allowed to shift in response to changes in ambient conditions. Hence, distinguishing climatic from competitive niche boundaries becomes a central challenge to identifying areas where tree species are most susceptible to climate change.</p><p>Here we employ a novel concept to characterize each position within a species’ bioclimatic niche based on two criteria: a climate sensitivity index (CSI) and a habitat suitability index (HSI). The CSI is derived from step-wise multiple linear regression models that explain variability in annual radial tree growth as a function of monthly climate anomalies. The HSI is based on an ensemble of five species distribution models calculated from a combination of observed species occurrences and twenty-five bioclimatic variables. We calculated these two indices for 11 major tree species across the Northern Hemisphere.</p><p>The combination of climate sensitivity and habitat suitability indicated hotspots of change, where tree growth is mainly limited by competition (low HSI and low CSI), as well as areas that are particularly sensitive to climate variability (low HSI and high CSI). In the former, we expect that forest management geared towards adjusting the competitive balance between several candidate species will be most effective under changing environmental conditions. In the latter areas, selecting particularly drought-tolerant accessions of a given species may reduce forest susceptibility to the predicted warming and drying.</p>

The economics of land use reveals a selection bias in tree species distribution models

2016 ◽  
Vol 26 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Jean-Sauveur Ay ◽  
Joannès Guillemot ◽  
Nicolas Martin-StPaul ◽  
Luc Doyen ◽  
Paul Leadley
Keyword(s):  
Land Use ◽  
Selection Bias ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Distribution Models

scholarly journals Species distribution models of rare tree species as an evaluation tool for synergistic human impacts in the Amazon rainforest

2017 ◽  
Vol 40 (4) ◽  
pp. 963-971 ◽  
Author(s):  
Tamilis R. Silva ◽  
Marcelo B. Medeiros ◽  
Sérgio E. Noronha ◽  
José Roberto R. Pinto
Keyword(s):  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Human Impacts ◽  
Amazon Rainforest ◽  
Evaluation Tool ◽  
Distribution Models

scholarly journals SPATIAL SCALE EFFECTS OF SAMPLING ON THE INTERPOLATION OF SPECIES DISTRIBUTION MODELS IN THE SOUTHWESTERN AMAZON

2016 ◽  
Vol 40 (4) ◽  
pp. 617-625 ◽  
Author(s):  
Symone Maria de Melo Figueiredo ◽  
Eduardo Martins Venticinque ◽  
Evandro Orfanó Figueiredo
Keyword(s):  
Forest Management ◽  
Spatial Scale ◽  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Scale Effects ◽  
Projection Area ◽  
Distribution Models ◽  
Forest Inventories ◽  
Occurrence Data

ABSTRACT Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.

Hypothesis-driven species distribution models for tree species in the Bavarian Alps

2011 ◽  
Vol 22 (4) ◽  
pp. 635-646 ◽  
Author(s):  
K. H. Mellert ◽  
V. Fensterer ◽  
H. Küchenhoff ◽  
B. Reger ◽  
C. Kölling ◽  
...  
Keyword(s):  
Species Distribution ◽  
Tree Species ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Bavarian Alps

scholarly journals Continent-Wide Tree Species Distribution Models May Mislead Regional Management Decisions: A Case Study in the Transboundary Biosphere Reserve Mura-Drava-Danube

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 330
Author(s):  
Markus Sallmannshofer ◽  
Debojyoti Chakraborty ◽  
Harald Vacik ◽  
Gábor Illés ◽  
Markus Löw ◽  
...  
Keyword(s):  
Species Distribution ◽  
Tree Species ◽  
Large Scale ◽  
Species Distribution Models ◽  
Riparian Forest ◽  
Forest Composition ◽  
Occurrence Probability ◽  
Environmental Predictors ◽  
Distribution Models ◽  
Bioclimatic Variables

The understanding of spatial distribution patterns of native riparian tree species in Europe lacks accurate species distribution models (SDMs), since riparian forest habitats have a limited spatial extent and are strongly related to the associated watercourses, which needs to be represented in the environmental predictors. However, SDMs are urgently needed for adapting forest management to climate change, as well as for conservation and restoration of riparian forest ecosystems. For such an operative use, standard large-scale bioclimatic models alone are too coarse and frequently exclude relevant predictors. In this study, we compare a bioclimatic continent-wide model and a regional model based on climate, soil, and river data for central to south-eastern Europe, targeting seven riparian foundation species—Alnus glutinosa, Fraxinus angustifolia, F. excelsior, Populus nigra, Quercus robur, Ulmus laevis, and U. minor. The results emphasize the high importance of precise occurrence data and environmental predictors. Soil predictors were more important than bioclimatic variables, and river variables were partly of the same importance. In both models, five of the seven species were found to decrease in terms of future occurrence probability within the study area, whereas the results for two species were ambiguous. Nevertheless, both models predicted a dangerous loss of occurrence probability for economically and ecologically important tree species, likely leading to significant effects on forest composition and structure, as well as on provided ecosystem services.

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