Estimates of species’ ranges can inform many aspects biodiversity research and conservation-management decisions. Many practical applications need high-precision range estimates that are sufficiently reliable to use as input data in downstream applications. One solution has involved expert-generated maps reflect on-the-ground field information implicitly capture various processes may limit a ge...

In an era of global changes, developing reliable biodiversity models has become important research area. Species distribution are the common tools to understand and predict distributions species across space time. However, they fail explicitly account for interactions. To this aim, joint were introduced tease apart effect environment from that interactions, improve rare modeling, functional tra...

Species distribution models (SDMs) are increasingly used for extrapolation, or predicting suitable regions for species under new geographic or temporal scenarios. However, SDM predictions may be prone to errors if species are not at equilibrium with climatic conditions in the current range and if training samples are not representative. Here the controversial "Pleistocene rewilding" proposal wa...

Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other factors can also be essential, particularly for with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wet...

Mechanistic species distribution models (SDMs) are ideally suited for predicting the nonnative distributions of invasive species, but require accurate parameterization of key functional traits. Importantly, any ability of the invader to acclimate or adapt rapidly to local conditions must be incorporated. Our field and laboratory studies measured phenotypic variation and tested for plasticity in...

Species distribution modeling is widely used for evaluating invasion risk, and prioritizing areas the control management of invasive species. However, selecting a tool that accurately predicts species risk requires systematic approach. In this study, five models (SDMs), namely, artificial neural network (ANN), generalized linear model (GLM), multivariate adaptive regression splines (MARS), maxi...

Local increases in sea level caused by global climate change pose a significant threat to the persistence of many coastal plant species through exacerbating inundation, flooding, and erosion. In addition to sea level rise (SLR), climate changes in the form of air temperature and precipitation regimes will also alter habitats of coastal plant species. Although numerous studies have analyzed the ...

Sound environmental management decisions - in accordance with the EU WFD for aquatic ecosystems – mainly depend on reliable species presence- and distribution- data. Here we present a workflow from sampling strategies to results decision making using eDNA metabarcoding analyses fish, amphibians, mussels habitat landscape scales focus "big data" marine freshwater Sweden. The project LifeDNAquati...

Understanding how biodiversity will respond to future climate change is a major conservation and societal challenge. Climate change is predicted to force many species to shift their ranges in pursuit of suitable conditions. This study aims to use landscape genetics, the study of the effects of environmental heterogeneity on the spatial distribution of genetic variation, as a predictive tool to ...

The concept of social networks in conjunction with concepts from economics has attracted considerable attention in recent years. In this paper we propose the Stochastic Diffusion Market Search (SDMS), a novel contextual advertising method for mutual advertisement hosting among participating entities, where each owns a web site. In the scenario considered each participating agent/web-site buys o...