What Matters for Predicting the Occurrences of Trees: Techniques, Data, or Species' Characteristics?

Guisan, Antoine; Zimmermann, Niklaus E.; Elith, Jane; Graham, Catherine H.; Phillips, Steven J.; Peterson, A. Townsend

doi:10.1890/06-1060.1

Cited by 333 publications

(281 citation statements)

References 49 publications

Supporting

Mentioning

258

Contrasting

Unclassified

Order By: Relevance

“…The advantage of ME is that it can be fitted with a low number of presence-only observations and that it is able to model possibly non-linearly relationships. Another advantage of ME compared to RF, which was also reported by Guisan et al [78], was that the accuracy was not affected applying cross-validation. This indicated that RF was more prone to overfitting than ME.…”

Section: Discussionmentioning

confidence: 56%

See 1 more Smart Citation

Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data

Ortiz¹,

Breidenbach²,

Kändler³

2013

Remote Sensing

View full text Add to dashboard Cite

Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management decisions to prevent larger damages. For this reason, a field-based bark beetle monitoring program is currently implemented in Germany. The combination of remote sensing and field data may help minimizing the reaction time and reducing costs of monitoring programs covering large forested areas. In this case study, RapidEye and TerraSAR-X data were analyzed separately and in combination to detect bark beetle green attack. The remote sensing data were acquired in May 2009 for a study site in south-west Germany. In order to distinguish healthy areas and areas affected by bark beetle green attack, three statistical approaches were compared: generalized linear models (GLM), maximum entropy (ME) and random forest (RF). The spatial scale (minimum mapping unit) was 78.5 m 2 .TerraSAR-X data resulted in fair classification accuracy with a cross-validated Cohen's Kappa Coefficient (kappa) of 0.23. RapidEye data resulted in moderate classification accuracy with a kappa of 0.51. The highest classification accuracy was obtained by combining the TerraSAR-X and RapidEye data, resulting in a kappa of 0.74. The accuracy of ME models was considerably higher than the accuracy of GLM and RF models. OPEN ACCESSRemote Sens. 2013, 5 1913

show abstract

Section: Discussionmentioning

confidence: 56%

“…The accuracy of GLM and RF are negatively influenced by small sample sizes (e.g., [23,78,79]). The advantage of ME is that it can be fitted with a low number of presence-only observations and that it is able to model possibly non-linearly relationships.…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data

Ortiz¹,

Breidenbach²,

Kändler³

2013

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Here, the indicator species analysis provides no information on drought susceptibility. These species (and also P. sylvestris [planted]) as elements of early-successional forest phases generally are considered as difficult to model with respect to their climate dependence (Guisan et al 2007) and are ranked low in terms of favorability (see table 3) with the exception of P. avium.…”

Section: Comparison Of the Different Drought Tolerance Rankingsmentioning

confidence: 99%

Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany

et al. 2017

View full text Add to dashboard Cite

Ch., Sutcliffe L., Leuschner Ch., 2017. Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany. Ann. For. Res. 60(1): 101-126. Abstract.We compared results derived using three different approaches to assess the suitability of common tree species on the Franconian Plateau in southern Germany under projected warmer and drier climate conditions in the period 2061-2080. The study area is currently a relatively warm and dry region of Germany. We calculated species distribution models (SDMs) using information on species' climate envelopes to predict regional species spectra under 63 different climate change scenarios. We complemented this with fine-scale ecological niche analysis using data from 51 vegetation surveys in seven forest reserves in the study area, and tree-ring analysis (TRA) from local populations of five tree species to quantify their sensitivity to climatic extreme years. The SDMs showed that predicted future climate change in the region remains within the climate envelope of certain species (e.g. Quercus petraea), whilst for e.g. Fagus sylvatica, future climate conditions in one third of the scenarios are too warm and dry. This was confirmed by the TRA: sensitivity to drought periods is lower for Q. petraea than for F. sylvatica. The niche analysis shows that the local ecological niches of Quercus robur and Fraxinus excelsior are mainly characterized by soils providing favorable water supply than by climate, and Pinus sylvestris (planted) is strongly influenced by light availability. The best adapted species for a warmer and potentially drier climate in the study region are Acer campestre, Sorbus torminalis, S. aria, Ulmus minor, and Tilia platyphyllos, which should therefore play a more prominent role in future climate-resilient mixed forest ecosystems.

show abstract

“…via its interaction with the use of pseudo-absences. Th ere are several approaches for generating pseudo-absence points (Pearce and Boyce 2006), including selecting points randomly (McPherson et al 2004), randomly with caseweighting to reduce the eff ective sample size of pseudo-absences (Guisan et al 2007), or via environmentally weighted random sampling (Zaniewski et al 2002). Th is selection of approach can aff ect the outcomes of the models (Engler et al 2004), but the pros and cons of diff erent approaches remain open to debate (Chefaoui and Lobo 2008).…”

Section: Mainmentioning

confidence: 99%

“…Many recent studies have adopted a strategy of selecting a set of pseudo-absences from the overall set of assumed absence data points to be used in the model calibration (e.g. McPherson et al 2004, Guisan et al 2007. Th e pseudo-absence approach may be a particularly attractive option when the modelling is based on atlases, museum data and databases.…”

Section: Introductionmentioning

confidence: 99%

Predicting distribution patterns and recent northward range shift of an invasive aquatic plant: Elodea canadensis in Europe

Heikkinen¹,

Leikola²,

Fronzek³

et al. 2009

View full text Add to dashboard Cite

Climate data and distribution data for the Canadian waterweed Elodea canadensis Michx. from North America, whole Europe and Finland were used to investigate the ability of bioclimatic envelope models to predict the distribution range and recent northward range shift of the species in Europe. Four diff erent main types of models were developed using the North American data, including either three 'baseline' climate variables (growing degrees days, temperature of the coldest month, water balance) or an extended set of seven climate variables, both averaged either over a 30 year time slice or a longer 90 year time slice. Ten diff erent random selections of pseudo-absences were generated from the North American data, on the basis of which ten separate generalized additive models (GAMs) were developed for each main model type. All the 40 developed GAMs were applied fi rst to North America and then transferred to whole Europe and Finland. All the models showed a statistically highly signifi cant accuracy in the three study areas. Although the diff erences among the four main model types were only minor, the two extended model types showed on average statistically better performance than the two baseline models based on Bayesian information criterion (BIC) values, the amount of deviance explained by the models, resubstitution validation and four-fold cross-validation in North America. Th ey also provided slightly more accurate predictions of climatically suitable area for Elodea canadensis in Finland both in 1961-1984 and 1985-2006. However, the projections from the individual extended models were more variable than projections from the baseline models. Th us model predictions based on a variety of predictor variables but only one selection of pseudo- Heikkinen et al. / BioRisk 2: 1-32 (2009) 2 absences may be subject to biases, and outputs from multiple models should be investigated to better account for uncertainties in modelling. Overall, our results suggest that more attention should be paid to the careful selection of predictor variables and the use of multiple pseudo-absence sets in the ecological niche modelling in order to increase the reliability of the projections of the range shifts of invasive species. RESEARCH ARTICLE A peer-reviewed open-access journal Biodiversity & Ecosystem Risk Assessment

show abstract

What Matters for Predicting the Occurrences of Trees: Techniques, Data, or Species' Characteristics?

Cited by 333 publications

References 49 publications

Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data

Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data

Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany

Predicting distribution patterns and recent northward range shift of an invasive aquatic plant: Elodea canadensis in Europe

Contact Info

Product

Resources

About