• CS standardized data outperform opportunistic for SDMs. Opportunistic fail to identify key areas nightingale distribution. Citizen Science projects aimed at SDM should be designed avoid bias. Environmental gradients considered in project designs. Recording species absences could improve quality.

Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of) species' ecological niches and can be to make inferences about the of suitable habitat for species interest. Because suitability is expected influence population demography, have been estimate a variety parameters, from occurrence genetic diversity. However, critical look at ability predict...

Aim The increasing availability of regional and global climate data presents an opportunity to build better ecological models; however, it is not always clear which dataset most appropriate. aim this study was understand the impacts that alternative datasets have on modelled distribution plant species, develop systematic approaches enhancing their use in species models (SDMs). Location Victoria...

It is important to easily and efficiently obtain high quality species distribution data for predicting the potential distribution of species using species distribution models (SDMs). There is a need for a powerful software tool to automatically or semi-automatically assist in identifying and correcting errors. Here, we use Python to develop a web-based software tool (SDMdata) to easily collect ...

Conservation and natural resource management are frequently hampered by poor understanding of how species distributions have changed over time. Species Distribution Models (SDMs) correlate known occurrences with environmental variables to predict a species’ potential range. These models can then be projected unsurveyed areas or time periods overcome gaps in data on distribution. The eastern quo...

Around the world, SDMs have been widely used to support forest management planning and biodiversity conservation. Beyond prediction of species distribution provided by SDMs, this study aimed analyze spatial tree diversity using SDMs. The area is a Faidherbia albida parkland in Central Senegal. It characterized tree-based farming system dominated albida. Using robust representative dataset 9258 ...

Aim Species distribution models (SDMs) assume that all ecologically relevant predictor variables are included. This assumption is frequently violated in SDMs of plant species, as soil rarely Here, we used in-situ, geochemical along with more commonly topo-climatic and remotely sensed to create understorey species. We evaluated whether the potential importance greater than generally described SD...

Aim Spatial sampling biases in biodiversity data arise because of complex interactions between geography, species characteristics and human behaviour, including preferences for or against particular habitats; are therefore not necessarily independent the environmental niches species. We evaluate when correlations spatial likely to affect distribution models (SDMs) developed both with without at...

Invasions by non-native plants threaten forest health and sustainability. The ability to predict areas at greatest risk invasion is essential for informing both monitoring management of invasive species. Species distribution models (SDMs) are often used identify environmental correlates a species’ occurrence geographic that may be suitable its presence commonly constructed using solely abiotic ...

The understanding of spatial distribution patterns native riparian tree species in Europe lacks accurate models (SDMs), since forest habitats have a limited extent and are strongly related to the associated watercourses, which needs be represented environmental predictors. However, SDMs urgently needed for adapting management climate change, as well conservation restoration ecosystems. For such...