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1. Utilization of Community Science Data to Explore Habitat Suitability of Basal Termite Genera

   https://doi.org/10.1093/isd/ixac019  

   Goodman, Aaron; Allen, Jonah; Brim, Jinna; Codella, Alessa; Hahn, Brittney; Jojo, Hassan; BondocGawa Mafla-Mills, Zoila; Bondoc Mafla, Salka’Tuwa; Oduro, Agnes; Wilson, Megan; et al (July 2022, Insect Systematics and Diversity)

   Lozier, Jeffrey (Ed.)

   Abstract The advent of community-science databases in conjunction with museum specimen locality information has exponentially increased the power and accuracy of ecological niche modeling (ENM). Increased occurrence data has provided colossal potential to understand the distributions of lesser known or endangered species, including arthropods. Although niche modeling of termites has been conducted in the context of invasive and pest species, few studies have been performed to understand the distribution of basal termite genera. Using specimen records from the American Museum of Natural History (AMNH) as well as locality databases, we generated ecological niche models for 12 basal termite species belonging to six genera and three families. We extracted environmental data from the Worldclim 19 bioclimatic dataset v2, along with SoilGrids datasets and generated models using MaxEnt. We chose Optimal models based on partial Receiving Operating characteristic (pROC) and omission rate criterion and determined variable importance using permutation analysis. We also calculated response curves to understand changes in suitability with changes in environmental variables. Optimal models for our 12 termite species ranged in complexity, but no discernible pattern was noted among genera, families, or geographic range. Permutation analysis revealed that habitat suitability is affected predominantly by seasonal or monthly temperature and precipitation variation. Our findings not only highlight the efficacy of largely community-science and museum-based datasets, but our models provide a baseline for predictions of future abundance of lesser-known arthropod species in the face of habitat destruction and climate change. 

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2. Spatiotemporal trends of black walnut forest stocking under climate change

   https://doi.org/10.3389/ffgc.2022.970379  

   Ebrahimi, Aziz; Abbasi, Akane O.; Liang, Jingjing; Jacobs, Douglass F. (September 2022, Frontiers in Forests and Global Change)

   Basal area is a key measure of forest stocking and an important proxy of forest productivity in the face of climate change. Black walnut ( Juglans nigra ) is one of the most valuable timber species in North America. However, little is known about how the stocking of black walnut would change with differed bioclimatic conditions under climate change. In this study, we projected the current and future basal area of black walnut. We trained different machine learning models using more than 1.4 million tree records from 10,162 Forest Inventory and Analysis (FIA) sample plots and 42 spatially explicit bioclimate and other environmental attributes. We selected random forests (RF) as the final model to estimate the basal area of black walnut under climate change because RF had a higher coefficient of determination ( R 2 ), lower root mean square error (RMSE), and lower mean absolute error (MAE) than the other two models (XGBoost and linear regression). The most important variables to predict basal area were the mean annual temperature and precipitation, potential evapotranspiration, topology, and human footprint. Under two emission scenarios (Representative Concentration Pathway 4.5 and 8.5), the RF model projected that black walnut stocking would increase in the northern part of the current range in the USA by 2080, with a potential shift of species distribution range although uncertainty still exists due to unpredictable events, including extreme abiotic (heat, drought) and biotic (pests, disease) occurrences. Our models can be adapted to other hardwood tree species to predict tree changes in basal area based on future climate scenarios. 

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3. Suitability of foraging habitat for Eubalaena glacialis under future climate scenarios in the Northwest Atlantic

   https://doi.org/10.1525/elementa.2024.00044  

   Johnson, Omi H; Plourde, Stéphane; Lehoux, Caroline; Ross, Camille H; Tupper, Benjamin; Orphanides, Christopher D; Walsh, Harvey J; Record, Nicholas R (May 2025, Elem Sci Anth)

   The critically endangered North Atlantic right whale (Eubalaena glacialis) faces significant anthropogenic mortality. Recent climatic shifts in traditional habitats have caused abrupt changes in right whale distributions, challenging traditional conservation strategies. Tools that can help anticipate new areas where E. glacialis might forage could inform proactive management. In this study, we trained boosted regression tree algorithms with fine-resolution modeled environmental covariates to build prey copepod (Calanus) species-specific models of historical and future distributions of E. glacialis foraging habitat on the Northwest Atlantic Shelf, from the Mid-Atlantic Bight to the Labrador Shelf. We determined foraging suitability using E. glacialis foraging thresholds for Calanus spp. adjusted by a bathymetry-dependent bioenergetic correction factor based on known foraging behavior constraints. Models were then projected to 2046–2065 and 2066–2085 modeled climatologies for representative concentration pathway scenarios RCP 4.5 and RCP 8.5 with the goal of identifying potential shifts in foraging habitat. The models had generally high performance (area under the receiver operating characteristic curve > 0.9) and indicated ocean bottom conditions and bathymetry as important covariates. Historical (1990–2015) projections aligned with known areas of high foraging habitat suitability as well as potential suitable areas on the Labrador Shelf. Future projections suggested that the suitability of potential foraging habitat would decrease in parts of the Gulf of Maine and southwestern Gulf of Saint Lawrence, while potential habitat would be maintained or improved on the western Scotian Shelf, in the Bay of Fundy, on the Newfoundland and Labrador shelves, and at some locations along the continental shelf breaks. Overall, suitable habitat is projected to decline. Directing some survey efforts toward emerging potential foraging habitats can enable conservation management to anticipate the type of distribution shifts that have led to high mortality in the past. 

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4. Climate Change Impact on Human-Rodent Interfaces: Modeling Junin Virus Reservoir Shifts

   https://doi.org/10.1007/s10393-025-01723-z  

   Flores-Pérez, Nuri; Kulkarni, Pranav; Uhart, Marcela; Pandit, Pranav_S (June 2025, EcoHealth)

   Abstract The drylands vesper mouse (Calomys musculinus)is the primary host forJunin mammarenavirus(JUNV), the etiological agent of Argentine hemorrhagic fever in humans. We assessed the potential distribution ofC. musculinusand identified disease transmission hotspots under current climatic conditions and projected future scenarios, including severe (Representative Concentration Pathway 8.5) and intermediate (Representative Concentration Pathway 4.5) climate change scenarios in 2050 and 2070. Utilizing tree-based machine learning algorithms, we modeledC. musculinusdistribution by incorporating bioclimatic and landscape predictors. The model showed strong performance, achievingF-scores between 80.22 and 83.09%. Key predictors indicated thatC. musculinusprefers warm temperatures, moderate annual precipitation, low precipitation variability, and low pasture coverage. Under the severe climate change scenario, suitable areas for the rodent and hotspots for potential disease decreased. The intermediate scenario showed an expansion inC. musculinusdistribution alongside increased potential hotspot zones. Despite the complexity of ecological systems and the limitations of the model, our findings offer a framework for preventive measures and ecological studies in regions prone to the expansion ofC. musculinusand in hotspots for disease transmission driven by climate change. 

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5. Heavy Precipitation Impacts on Nitrogen Loading to the Gulf of Mexico in the 21st Century: Model Projections Under Future Climate Scenarios

   https://doi.org/10.1029/2021EF002141  

   Zhang, Jien; Lu, Chaoqun; Crumpton, William; Jones, Christopher; Tian, Hanqin; Villarini, Gabriele; Schilling, Keith; Green, David (April 2022, Earth's Future)

   Abstract While spatial heterogeneity of riverine nitrogen (N) loading is predominantly driven by the magnitude of basin‐wide anthropogenic N input, the temporal dynamics of N loading are closely related to the amount and timing of precipitation. However, existing studies do not disentangle the contributions of heavy precipitation versus non‐heavy precipitation predicted by future climate scenarios. Here, we explore the potential responses of N loading from the Mississippi Atchafalaya River Basin to precipitation changes using a well‐calibrated hydro‐ecological model and Coupled Model Intercomparison Project Phase 5 climate projections under two representative concentration pathway (RCP) scenarios. With present agricultural production and management practices, N loading could increase up to 30% by the end of the 21st century under future climate scenarios, half of which would be driven by heavy precipitation. Particularly, the RCP8.5 scenario, in which heavy precipitation and drought events become more frequent, would increase N loading disproportionately to projected increases in river discharge. N loading in spring would contribute 41% and 51% of annual N loading increase under the RCP4.5 and RCP8.5 scenarios, respectively, most of which is related to higher N yield due to increases in heavy precipitation. Anthropogenic N inputs would be increasingly susceptible to leaching loss in the Midwest and the Mississippi Alluvial Plain regions. Our results imply that future climate change alone, including more frequent and intense precipitation extremes, would increase N loading and intensify the eutrophication of the Gulf of Mexico over this coming century. More effective nutrient management interventions are needed to reverse this trend. 

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