Predicting current and future disease outbreaks of Diplodia sapinea shoot blight in Italy: species distribution models as a tool for forest management planning

Bosso, Luciano; Luchi, Nicola; Maresi, G.; Cristinzio, Gennaro; Smeraldo, Sonia; Russo, Danilo

doi:10.1016/j.foreco.2017.06.044

Cited by 73 publications

(59 citation statements)

References 67 publications

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“…We performed a Spearman correlation test in order to get the least collinear predictor subset using Infostat 5.1 (Di Rienzo et al., ). We chose variables with a correlation value lower than 0.7 and confirmed the selection of the variables with the knowledge about the natural history of the species (Bellini et al., ; Giraudo, ; Giraudo et al., ; Scrocchi et al., ) (Supporting Information ). This to ensure a controlled collinearity between predictors and to avoid biased results (Acevedo, Jiménez‐Valverde, Lobo, & Real, ).…”

Section: Methodsmentioning

confidence: 92%

“…We defined the ED of B. alternatus (Figure b) mainly based on both collected data with intensive spatial and temporal sample coverage and our knowledge (Bellini et al., ; Giraudo, ; Giraudo & Arzamendia, ; Giraudo et al., ) as well as by delimiting areas inhabited and not inhabited by the species. We included some areas without data in the inhabited territory of B. alternatus because we know that, they can be occupied by the species (Hortal et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…Soberón and Nakamura () said that grid resolution should be established by biological considerations of the size, mobility, and ecology of the species. In this case, B. alternatus 's home‐range is usually not very wide and there are areas within its distribution (the Pampean hills in Buenos Aires province) where the species presents environmental differences in the presence/absence at 1 km resolution in its distribution (Bellini et al., ; Giraudo, ; Giraudo & Arzamendia, ; Giraudo et al., ; Scrocchi et al., ). We performed a Spearman correlation test in order to get the least collinear predictor subset using Infostat 5.1 (Di Rienzo et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…As a model for this study, we selected B. alternatus , a poisonous snake species with public health importance. Thorough and continuous efforts have been made for decades to perform unbiased samplings throughout its distribution area in Argentina (e.g., Arzamendia & Giraudo, ; Bellini, Giraudo, Arzamendia, & Etchepare, ; Giraudo, ; Giraudo & Arzamendia, ; Giraudo et al., ; Nori, Carrasco, & Leynaud, ). Besides, B. alternatus : (a) presents a well‐understood distribution; (b) is easily detectable (it lives in relatively wet mesophilic open areas including grasslands, savannas, wetlands, and open forests in the Espinal, where exhaustive surveys are possible; (c) has a large and conspicuous size and is relatively abundant throughout its spatial distribution); (d) offers plenty of information about its natural history such as diet, habitat use, and reproduction (Bellini et al., ; Giraudo, ; Giraudo et al., ; Scrocchi, Moreta, & Kretzschmar, ); (e) presents peculiarities in its distribution which constitute real challenges in modeling; for example, it has not been found in humid forests to the northeast of its distribution (Giraudo, ) and two disjunctive and isolated populations occur in coastal areas and the Pampean hills in Buenos Aires province (South distribution).…”

Section: Introductionmentioning

confidence: 99%

“…SDMs relate species occurrence data with a set of variables selected under the assumption that they could be related to the distribution of the species (Guisan & Zimmermann, 2000). They are being increasingly used to assess conservation applications and climate change studies, and predict both ecological ranges and the potential of invasive species and explicit predictions about species environmental suitability (Bosso et al, 2017;Chen, Zhang, Jiang, Nielsen, & He, 2017;Law et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Species distribution models and empirical test: Comparing predictions with well‐understood geographical distribution of Bothrops alternatus in Argentina

Sarquis

Cristaldi

Arzamendia

et al. 2018

Ecology and Evolution

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Species distribution models (SDMs) estimate the geographical distribution of species although with several limitations due to sources of inaccuracy and biases. Empirical tests arose as the most important steps in scientific knowledge to assess the efficiency of model predictions, which are poorly rigorous in SDMs. A good approach to the empirical distribution (ED) of a species can be obtained from comprehensive empirical knowledge, that is, well‐understood distributions gathered from large amount of data generated with appropriate spatial and temporal sample coverage. The aims of this study were to (a) compare different SDMs predictions with an ED; and (b) evaluate if fuzzy global matching (FGM) could be used as an index to compare SDMs predictions and ED. Six algorithms with 5 and 20 variables were used to assess their accuracy in predicting the ED of the venomous snake Bothrops alternatus (Viperidae). Its entire distribution is known, thanks to thorough field surveys across Argentina, with 1,767 records. ED was compared with SDMs predictions using Map Comparison Kit. SDMs predictions showed important biases in all methods used, from 70% sub‐estimation to 40% over‐estimation of ED. BIOCLIM predicted ≈31% of B. alternatus ED. DOMAIN predicted 99% of ED, but over‐estimated 40% of the area. GLM with five variables calculated 75% of ED, while Genetic Algorithm for Rule‐set Prediction showed ≈60% of ED; the last two presenting overpredictions in areas with favorable climatic conditions but not inhabited by the species. MaxEnt and RF were the only methods to detect isolated populations in the southern distribution of B. alternatus. Although SDMs proved useful in making predictions about species distribution, predictions need validation with expert maps knowledge and ED. Moreover, FGM showed a good performance as an index with values similar to True Skill Statistic, so that it could be used to relate ED and SDMs predictions.

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