David John Galloway

Paleoclimate reconstructions can provide a window into the environmental conditions in Earth history when atmospheric carbon dioxide concentrations were higher than today. In the eastern USA, paleoclimate reconstructions are sparse, because terrestrial sedimentary deposits are rare. Despite this, the eastern USA has the largest population and population density in North America, and understanding the effects of current and future climate change is of vital importance. Here, we provide terrestrial paleoclimate reconstructions of the eastern USA from Miocene fossil floras. Additionally, we compare proxy paleoclimate reconstructions from the warmest period in the Miocene, the Miocene Climatic Optimum (MCO), to those of an MCO Earth System Model. Reconstructed Miocene temperatures and precipitation north of 35°N are higher than modern. In contrast, south of 35°N, temperatures and precipitation are similar to today, suggesting a poleward amplification effect in eastern North America. Reconstructed Miocene rainfall seasonality was predominantly higher than modern, regardless of latitude, indicating greater variability in intra-annual moisture transport. Reconstructed climates are almost uniformly in the temperate seasonal forest biome, but heterogeneity of specific forest types is evident. Reconstructed Miocene terrestrial temperatures from the eastern USA are lower than modeled temperatures and coeval Atlantic sea surface temperatures. However, reconstructed rainfall is consistent with modeled rainfall. Our results show that during the Miocene, climate was most different from modern in the northeastern states, and may suggest a drastic reduction in the meridional temperature gradient along the North American east coast compared to today.

Climate change is one of the most serious threats to global crops production at present and it will continue to be the largest threat in the future worldwide. Knowing how climate change affects crop productivity might help sustainability and crop improvement efforts. Under existing and projected climate change scenarios (2050s and 2070s in Ethiopia), the effect of global warming on the distribution of V. radiata and V. unguiculata was investigated. MaxEnt models were used to predict the current and future distribution pattern changes of these crops in Ethiopia using different climate change scenarios (i.e., lowest (RCP 2.6), moderate (RCP 4.5), and extreme (RCP 8.5)) for the years 2050s and 2070s. The study includes 81 and 68 occurrence points for V. radiata and V. unguiculata, respectively, along with 22 environmental variables. The suitability maps indicate that the Beneshangul Gumuz, Oromia, Amhara, SNNPR, and Tigray regions are the major Ethiopian regions with the potential to produce V. radiata, while Amhara, Gambella, Oromia, SNNPR, and Tigray are suitable for producing V. unguiculata. The model prediction for V. radiata habitat ranges distribution in Ethiopia indicated that 1.69%, 4.27%, 11.25% and 82.79% are estimated to be highly suitable, moderately suitable, less suitable, and unsuitable, respectively. On the other hand, the distribution of V. unguiculata is predicted to have 1.27%, 3.07%, 5.22%, and 90.44% habitat ranges that are highly suitable, moderately suitable, less suitable, and unsuitable, respectively, under the current climate change scenario by the year (2050s and 2070s) in Ethiopia. Among the environmental variables, precipitation of the wettest quarter (Bio16), solar radiation index (SRI), temperature seasonality (Bio4), and precipitation seasonality (Bio15) are discovered to be the most effective factors for defining habitat suitability for V. radiata, while precipitation of the wettest quarter (Bio16), temperature annual range (Bio7) and precipitation of the driest quarter (Bio17) found to be better habitat suitability indicator for V. unguiculata in Ethiopia. The result indicates that these variables were more relevant in predicting suitable habitat for these crops in Ethiopia. A future projection predicts that the suitable distribution region will become increasingly fragmented. In general, the study provides a scientific basis of suitable agro-ecological habitat for V. radiata and V. unguiculata for long-term crop management and production improvement in Ethiopia. Therefore, projections of current and future climate change impacts on such crops are vital to reduce the risk of crop failure and to identify the potential productive areas in the country.

The northern bog lemming Synaptomys borealis (NBL) is a rare small mammal that is undergoing a federal Species Status Assessment (SSA) under the US Endangered Species Act. Despite a wide North American distribution, very little is known about NBL dietary or habitat needs, both of which are germane to the resiliency of this species to climate change. To quantify diet composition of NBL in Alaska, we used DNA metabarcoding from 59 archived specimens to describe the taxonomic richness and relative abundance of foods in recent diets. DNA analyses revealed a broad diet composed of at least 110 families and 92 genera of bryophytes (mosses and liverworts), graminoids, fungi, forbs, and woody shrubs. Nine bryophyte genera and Carex sedges composed the largest portions of NBL diets. To quantify habitat preference, we intersected 467 georeferenced occurrence records of NBL in Alaska with remotely sensed land cover classes and used a compositional analysis framework that accounts for the relative abundance of land cover types. We did not detect significant habitat preferences for specific land cover types, although NBL frequently occurred in evergreen forest, woody wetlands, and adjacent to water. Our research highlights the importance of bryophytes, among a high diversity of dietary components, and describes NBL as boreal habitat generalists. Results will inform the current federal SSA by quantifying the extent to which ecological constraints are likely to affect NBL in a rapidly changing boreal environment.

Aims Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence–habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location Virtual Southeast Asia. Methods Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results Greater ARC resulted in greater occurrence–habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence–habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without. Main conclusions We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations.

The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…

Mollusca is the largest marine phylum, comprising about 23% of all named marine organisms, Mollusca systematics are still in flux, and an increase in human activities has affected Molluscan reproduction and development, strongly impacting diversity and classification. Therefore, it is necessary to e…

Plant invasion has been proved to threaten biodiversity conservation and ecosystem maintenance at a global scale. It is a challenge to project suitable habitat distributions of invasive plant species (IPS) for invasion risk assessment at large spatial scales. Interaction outcomes between native and …

Ferns and lycophytes are an excellent group for conservation and species distribution studies because they are closely related to environmental changes. In this study, we analyzed collection gaps, sampling biases, richness distribution, and the species conservation effectiveness of protected areas i…

Hotspots are priority marine or terrestrial areas with high biodiversity where delineation is essential for conservation, but equally important is their linkage to the environmental policies of the overall region. In this study, fish diversity presences were linked to abiotic conditions and differen…

Aims: Species distribution models (SDMs) are often used to forecast potential distributions of important invasive or rare species. However, situations where models could be the most valuable ecologically or economically, such as for predicting invasion risk, often pose the greatest challenges to SDM…