Assessing the performance and accuracy of invasive plant habitat suitability models in detecting new observations in Wisconsin

Land managers require tools that improve understanding of suitable habitat for invasive plants and be incorporated into survey efforts to improve efficiency. Habitat suitability models contain attributes that can meet these requirements, but it is not known how well they perform as they are rarely field tested for accuracy. We developed ensemble habitat suitability models in the state of Wisconsin for 15 species using five algorithms (boosted regression trees, generalized linear models, multivariate regression splines, MaxEnt, and random forests), evaluated performance, determined variables that drive suitability, and tested accuracy. All models had good model performance during the development phase (AUC>0.7 and TSS>0.4). While variable importance and directionality was species specific, the most important predictor variables across all of the species’ models were mean winter minimum temperatures, total summer precipitation and tree canopy cover. Post model development we obtained 5,005 new occurrence records from community science observations for all 15 focal species to test the models’ abilities to accurately predict results. Using a correct classification rate of 80%, just 8 of the 15 species correctly predicted suitable habitat (α≤0.05). Exploratory analyses found the number of reporters of these new data and the total number of new occurrences reported per species contributed to increasing correct classification. Results suggest that while some models perform well on evaluation metrics, relying on these metrics alone is not sufficient and can lead to errors when utilized for surveying. We recommend any model should be tested for accuracy in the field prior to use to avoid this potential issue.

Occurrence of jaguar (Panthera onca) in the Chinantla region, southern Mexico

The jaguar (Panthera onca) has been experiencing a considerable range reduction due to habitat loss and poaching. Habitat suitability models have identified areas likely to maintain populations, but field data are scarce for several of them. Between 2012 and 2017, we investigated the jaguar occurrence in 35 communities of the Chinantla region, southern Mexico, throughout camera trapping in non-systematic surveys. We recorded 124 independent events of 23 jaguars in thirteen communities. Jaguars recorded over the years, couples and pregnant females are highlighted in the Chinantla region as a stronghold to the jaguar.

Diel patterns of foraging and microhabitat use by sympatric rainbow trout and bull trout: implications for adaptive differentiation and instream flow assessment

Salmonids make flexible and adaptive trade-offs between foraging efficiency and predation risk that result in variable patterns of diel activity and habitat use. However, it remains unclear: (1) how patterns differ among salmonid species; and (2) how this affects the interpretation of habitat suitability models that inform instream flow management. We combined snorkel observations with experimental additions of cover to investigate how predation risk, cover, and bioenergetics affect diel activity and habitat use patterns by sympatric rainbow trout and bull trout in the Skagit River, BC, Canada. Both species foraged primarily at dusk, supporting the well-described trade-off between foraging efficiency and predation risk. However, only rainbow trout responded to cover additions, suggesting that risk tolerance and the nature of foraging-predation risk trade-offs differ between species. Diel shifts in activity and habitat use also substantially altered predictions of habitat suitability models, with potentially large consequences for flow management.

Potential Impacts of Climate Change on Shrimps Distribution of Commercial Importance in the Gulf of California

The Gulf of California is the most productive fishing region in Mexico; its ecosystems contain a vast diversity of species with exploitation potential, some of them potentially vulnerable to climate change. This research was conducted to analyze, through habitat suitability models, the possible alterations in the distribution of the three shrimp species of the most importance for commercial fishing in the region: Litopenaeus stylirostris, Litopenaeus vannamei, and Farfantepenaeus californiensis. Habitat suitability models were built using the MaxEnt software, primary productivity data, temperature, salinity, bathymetry, substratum, coastal type, and geo-referenced occurrence records of the three species. Of the data, 70% was used on training, while the remaining 30% was used for validation. To make estimates of climate change impact on this fishery, projections on distribution of the three species from environmental forecasts generated by the intergovernmental panel on climate change until 2100 were made. The used model, that is in full development and expansion, could be considered as an applicable tool to other problems and showed efficiency rates above 90%. The species will maintain most of their historical distribution, but L. stylirostris and L. vannamei will have a new distribution area within the zones of the Magdalena-Almejas Bay and the Gulf of Ulloa, with an increase of 80% and 148% respectively; all species will have loss areas in the proportion of 16%, 2%, and 11%, respectively, along the southern Gulf of California.

Assessing Habitat Suitability Models for the Deep Sea: Is Our Ability to Predict the Distributions of Seafloor Fauna Improving?

Methods that predict the distributions of species and habitats by developing statistical relationships between observed occurrences and environmental gradients have become common tools in environmental research, resource management, and conservation. The uptake of model predictions in practical applications remains limited, however, because validation against independent sample data is rarely practical, especially at larger spatial scales and in poorly sampled environments. Here, we use a quantitative dataset of benthic invertebrate faunal distributions from seabed photographic surveys of an important fisheries area in New Zealand as independent data against which to assess the usefulness of 47 habitat suitability models from eight published studies in the region. When assessed against the independent data, model performance was lower than in published cross-validation values, a trend of increasing performance over time seen in published metrics was not supported, and while 74% of the models were potentially useful for predicting presence or absence, correlations with prevalence and density were weak. We investigate the reasons underlying these results, using recently proposed standards to identify areas in which improvements can best be made. We conclude that commonly used cross-validation methods can yield inflated values of prediction success even when spatial structure in the input data is allowed for, and that the main impediments to prediction success are likely to include unquantified uncertainty in available predictor variables, lack of some ecologically important variables, lack of confirmed absence data for most taxa, and modeling at coarse taxonomic resolution.