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

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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Hotspots of change in major tree species under climate warming

10.5194/egusphere-egu2020-22325 ◽

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

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.Importantly, the geographic and bioclimatic space (or &#8220;niche&#8221;) 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.Here we employ a novel concept to characterize each position within a species&#8217; 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.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.

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

Global Ecology and Biogeography ◽

10.1111/j.1466-8238.2009.00478.x ◽

2009 ◽

Vol 18 (6) ◽

pp. 662-673 ◽

Cited By ~ 21

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

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Spatial species distribution models: Using Bayes inference with INLA and SPDE to improve the tree species choice for important European tree species

Forest Ecology and Management ◽

10.1016/j.foreco.2021.119983 ◽

2022 ◽

Vol 507 ◽

pp. 119983

Author(s):

Markus Engel ◽

Tobias Mette ◽

Wolfgang Falk

Keyword(s):

Species Distribution ◽

Tree Species ◽

Species Distribution Models ◽

Distribution Models ◽

Bayes Inference

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Comparing species distribution models constructed with different subsets of environmental predictors

Diversity and Distributions ◽

10.1111/ddi.12247 ◽

2014 ◽

Vol 21 (1) ◽

pp. 23-35 ◽

Cited By ~ 73

Author(s):

David N. Bucklin ◽

Mathieu Basille ◽

Allison M. Benscoter ◽

Laura A. Brandt ◽

Frank J. Mazzotti ◽

...

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Environmental Predictors ◽

Distribution Models

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Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models

Ecological Informatics ◽

10.1016/j.ecoinf.2013.02.003 ◽

2013 ◽

Vol 15 ◽

pp. 8-13 ◽

Cited By ~ 11

Author(s):

Brice B. Hanberry

Keyword(s):

Grain Size ◽

Species Distribution ◽

Species Distribution Models ◽

Environmental Predictors ◽

Distribution Models

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

Forest Ecology and Management ◽

10.1016/j.foreco.2021.119838 ◽

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

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Multispecies and Watershed Approaches to Freshwater Fish Conservation

Multispecies and Watershed Approaches to Freshwater Fish Conservation ◽

10.47886/9781934874578.ch17 ◽

2019 ◽

Keyword(s):

Climate Change ◽

Land Use ◽

Fisheries Management ◽

Species Distribution ◽

Large Scale ◽

Species Distribution Models ◽

Distribution Models ◽

Climate Conditions ◽

Resource Sustainability ◽

Riparian Protection

Abstract.—Increasingly, fisheries managers must make important decisions in complex environments where rapidly changing landscape and climate conditions interact with historical impacts to influence resource sustainability. Successful fisheries management in this setting will require that we adapt traditional management approaches to incorporate information on these complex interacting factors—a process referred to as resilient fisheries management. Large-scale species distribution data and predictive models have the potential to enhance the management of freshwater fishes through improved understanding of how past, present, and future natural and anthropogenic factors combine to determine species vulnerability and resiliency. Here we describe a resilient fisheries management framework that provides guidance on how and when these models can be incorporated into traditional approaches to meet specific goals and objectives for resource sustainability. In addition to elucidating complex drivers of distributional patterns and change, species distribution models can inform the prioritization, application, and implementation of management activities such as restoration (e.g., instream habitat and riparian), protection (e.g., areas where additional land use would result in a change in species distribution), and regulations (e.g., harvest restriction) in a way that informs resiliency to land use and climate change. Although considerable progress has been made with respect to applying species distribution models to the management of Brook Trout Salvelinus fontinalis and other aquatic species, there are several areas where a more unified research and management effort could increase the ability of distribution models to inform resilient management. Future efforts should aim to improve (1) data availability, consistency (sampling methodology), and quality (accounting for detection); (2) partnerships among researchers, agencies, and managers; and (3) model accessibility and understanding of limitations and potential benefits to managers (e.g., incorporation into publicly available decision support systems). The information and recommendations provided herein can be used to promote and advance the use of models in resilient fisheries management in the face of continued large-scale land use and climate change.

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Bounding species distribution models

Current Zoology ◽

10.1093/czoolo/57.5.642 ◽

2011 ◽

Vol 57 (5) ◽

pp. 642-647 ◽

Cited By ~ 21

Author(s):

Thomas J. Stohlgren ◽

Catherine S. Jarnevich ◽

Wayne E. Esaias ◽

Jeffrey T. Morisette

Keyword(s):

Species Distribution ◽

Honey Bees ◽

Best Practice ◽

Species Distribution Models ◽

Regression Tree ◽

Classification And Regression Tree ◽

Suitable Habitat ◽

Environmental Predictors ◽

Distribution Models ◽

Africanized Honey Bees

Abstract Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for “clamping” model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used.

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