Trade-offs between specificity and regional generality in habitat association models: a case study of two species of African vulture

Filling the data gaps: Transferring models from data-rich to data-poor deep-sea areas to support spatial management

Spatial management of the deep sea is challenging due to limited available data on the distribution of species and habitats to support decision making. In the well-studied North Atlantic, predictive models of species distribution and habitat suitability have been used to fill data gaps and support sustainable management. In the South Atlantic and other poorly studied regions, this is not possible due to a massive lack of data. In this study, we investigated whether models constructed in data-rich areas can be used to inform data-poor regions (with otherwise similar environmental conditions). We used a novel model transfer approach to identify to what extent a habitat suitability model for Desmophyllum pertusum reef, built in a data-rich basin (North Atlantic), could be transferred usefully to a data-poor basin (South Atlantic). The transferred model was built using the Maximum Entropy algorithm and constructed with 227 presence and 3064 pseudo-absence points, and 200 m resolution environmental grids. Performance in the transferred region was validated using an independent dataset of D. pertusum presences and absences, with assessments made using both threshold-dependent and -independent metrics. We found that a model for D. pertusum reef fitted to North Atlantic data transferred reasonably well to the South Atlantic basin, with an area under the curve of 0.70. Suitable habitat for D. pertusum reef was predicted on 20 of the assessed 27 features including seamounts. Nationally managed Marine Protected Areas provide significant protection for D. pertusum reef habitat in the region, affording full protection from bottom trawling to 14 of the 20 suitable features. In areas beyond national jurisdiction (ABNJ), we found four seamounts that provided suitable habitat for D. pertusum reef to be at least partially protected from bottom trawling, whilst two did not fall within fisheries closures. There are factors to consider when developing models for transfer including data resolution and predictor type. Nevertheless, the promising results of this application demonstrate that model transfer approaches stand to provide significant contributions to spatial planning processes through provision of new, best available data. This is particularly true for ABNJ and areas that have previously undergone little scientific exploration such as the global south.

Tuning the pore structure of templated mesoporous poly(melamine-co-formaldehyde) particles toward diclofenac removal

The increasing demand and implementation of pharmaceutics poses severe risk to different aquatic species as detectable contaminant in almost every surface water worldwide. Diclofenac (DCF) as one of the most common used analgesics was investigated as contaminant to be removed by adsorption onto nanoporous poly(melamine-co-formaldehyde) (PMF) particles featuring a very high amount of nitrogen functionalities. To achieve a high specific surface area (up to 416 m²/g) and a tunable pore system by hard templating, four different SiO₂ nanoparticles were used as template. Differences in the pore formation and achieved pore structure were elucidated. For the first time, the adsorption of DCF onto PMF was tested. In batch adsorption experiments, impactful adsorption capacities as high as 76 μmol/g were achieved and complete removal at initial concentrations of 2 mg/L DCF. Differences in the connectivity and the micropore structure were decisive for uptake in low concentrations and the achieved adsorption capacity, respectively. As the presented PMF particles can be easily synthesized with the monomers formaldehyde and melamine combined with colloidal silica as sacrificial template and water as green solvent, this material presents a viable adsorbent for the removal of DCF at a larger scale. Our study further indicates a high potential for the removal of other pharmaceuticals.

Old World Vultures Reflect Effects of Environmental Pollutants Through Human Encroachment

African wildlife face challenges from many stressors including current and emerging contaminants, habitat and resource loss, poaching, intentional and unintentional poisoning, and climate-related environmental change. The plight of African vultures exemplifies these challenges due to environmental contaminants and other stressors acting on individuals and populations that are already threatened or endangered. Many of these threats emanate from increasing human population size and settlement density, habitat loss from changing land use for agriculture, residential areas, and industry, and climate-related changes in resource availability. Environmental chemicals that are hazardous include legacy chemicals, emerging chemicals of concern, and high-volume-use chemicals that are employed as weed killers and in other agricultural applications. Furthermore, there are differences in risk for species living in close proximity to humans or in areas affected by habitat loss, climate, and industry. Monitoring programs are essential to track the status of nesting pairs, offspring survival, longevity, and lifetime productivity. This is important for long-lived birds, such as vultures, that may be especially vulnerable to chronic exposure to chemicals as obligate scavengers. Furthermore, their position in the food web may increase risk due to biomagnification of chemicals. We review the primary chemical hazards to Old World vultures and the interacting stressors affecting these and other birds. Habitat is a major consideration for vultures, with tree-nesters and cliff-nesters potentially experiencing different risks of exposure to environmental chemicals. The present review provides information from long-term monitoring programs and discusses a range of these threats and their effects on vulture populations. Environ Toxicol Chem 2022;41:1586-1603. © 2022 SETAC.

Prediction of habitat suitability for Patrinia sibirica Juss. in the Southern Urals

The paper presents the results of predictions of the habitat persistence for rare relict of the Pleistocene floristic complex Patrinia sibirica (L.) Juss. in the Southern Urals under various forecasted climate change scenarios. Climate variables from CHELSA BIOCLIM, elevation data (GMTED2010) and coarse fragment content in the top level of soil were used as predictors for modeling in the MaxEnt software. The impact of climate change on P. sibirica habitats under the RCP4.5 and RCP8.5 scenarios calculated from an ensemble of four general circulation models has been analyzed. The modeling has shown that the changes in the habitat suitability depend on the altitude. Deterioration of the habitats could be attributed to a temperature increase in mountain forest locations, and to a precipitation of driest quarter increase in mountain forest-steppe locations. In both cases, this leads to the expansion of forest and shrub vegetation. Monitoring of the habitat persistence of P. sibirica and other relict species of the Pleistocene floristic complex can play a major role in predictions, as their massive decline would constitute that climatic changes exceed the ranges of their fluctuations in the Holocene.

Model application niche analysis: Assessing the transferability and generalizability of ecological models

The use of models by ecologists and environmental managers, to inform environmental management and decision-making, has grown exponentially in the past 50 years. Due to logistical, economical, and theoretical benefits, model users frequently transfer preexisting models to new sites where data are scarce. Modelers have made significant progress in understanding how to improve model generalizability during model development. However, models are always imperfect representations of systems and are constrained by the contextual frameworks used during their development. Thus, model users need better ways to evaluate the possibility of unintentional misapplication when transferring models to new sites. We propose a method of describing a model's application niche for use during the model selection process. Using this method, model users synthesize information from databases, past studies, and/or past model transfers to create model performance curves and heat maps. We demonstrated this method using an empirical model developed to predict the ecological condition of plant communities in riverine wetlands of the Appalachian Highland physiographic region, U.S.A. We assessed this model's transferability and generalizability across (1) riverine wetlands in the contiguous U.S.A., (2) wetland types in the Appalachian Highland physiographic region, and (3) wetland types in the contiguous U.S.A. With this methodology and a discussion of its critical steps, we set the stage for further inquiries into the development of consistent and transparent practices for model selection when transferring a model.

Spatially explicit assessment of estuarine fish after Deepwater Horizon oil spill: trade-off in complexity and parsimony

Evaluating long-term contaminant effects on wildlife populations depends on spatial information about habitat quality, heterogeneity in contaminant exposure, and sensitivities and distributions of species integrated into a systems modeling approach. Rarely is this information readily available, making it difficult to determine the applicability of realistic models to quantify population-level risks. To evaluate the trade-offs between data demands and increased specificity of spatially explicit models for population-level risk assessments, we developed a model for a standard toxicity test species, the sheepshead minnow (Cyprinodon variegatus), exposed to oil contamination following the Deepwater Horizon oil spill and compared the output with various levels of model complexity to a standard risk quotient approach. The model uses habitat and fish occupancy data collected over five sampling periods throughout 2008-2010 in Pensacola and Choctawhatchee Bays, Florida, USA, to predict species distribution, field-collected and publically available data on oil distribution and concentration, and chronic toxicity data from laboratory assays applied to a matrix population model. The habitat suitability model established distribution of fish within Barataria Bay, Louisiana, USA, and the population model projected the dynamics of the species in the study area over a 5-yr period (October 2009-September 2014). Vital rates were modified according to estimated contaminant concentrations to simulate oil exposure effects. To evaluate the differences in levels of model complexity, simulations varied from temporally and spatially explicit, including seasonal variation and location-specific oiling, to simple interpretations of a risk quotient derived for the study area. The results of this study indicate that species distribution, as well as spatially and temporally variable contaminant concentrations, can provide a more ecologically relevant evaluation of species recovery from catastrophic environmental impacts but might not be cost-effective or efficient for rapid assessment needs.