Pollen identification is necessary for several subfields of geology, ecology, and evolutionary biology. However, the existing methods for pollen identification are laborious, time-consuming, and require highly skilled scientists. Therefore, there is a pressing need for an automated and accurate system for pollen identification, which can be beneficial for both basic research and applied issues such as identifying airborne allergens. In this study, we propose a deep learning (DL) approach to classify pollen grains in the Great Basin Desert, Nevada, USA. Our dataset consisted of 10,000 images of 40 pollen species. To mitigate the limitations imposed by the small volume of our training dataset, we conducted an in-depth comparative analysis of numerous pre-trained Convolutional Neural Network (CNN) architectures utilizing transfer learning methodologies. Simultaneously, we developed and incorporated an innovative CNN model, serving to augment our exploration and optimization of data modeling strategies. We applied different architectures of well-known pre-trained deep CNN models, including AlexNet, VGG-16, MobileNet-V2, ResNet (18, 34, and 50, 101), ResNeSt (50, 101), SE-ResNeXt, and Vision Transformer (ViT), to uncover the most promising modeling approach for the classification of pollen grains in the Great Basin. To evaluate the performance of the pre-trained deep CNN models, we measured accuracy, precision, F1-Score, and recall. Our results showed that the ResNeSt-110 model achieved the best performance, with an accuracy of 97.24%, precision of 97.89%, F1-Score of 96.86%, and recall of 97.13%. Our results also revealed that transfer learning models can deliver better and faster image classification results compared to traditional CNN models built from scratch. The proposed method can potentially benefit various fields that rely on efficient pollen identification. This study demonstrates that DL approaches can improve the accuracy and efficiency of pollen identification, and it provides a foundation for further research in the field.

The Mojave Desert of the American West is characterized by plant species that reflect a unique mixture of winter precipitation and summer monsoon climate. Currently, the Mojave Desert experiences a strong summer monsoonal pattern with weak winter precipitation. Data from pollen and packrat midden analyses have revealed a history of Mojave Desert vegetation during the transition from the late Pleistocene to late Holocene (~17500 Cal. years B.P. to ~ 1200 Cal. years B.P.) that highlight a summer dominated monsoonal pattern, similar to those in the greater American Southwest. We compare pollen data from a lava tube in the Cima volcanic field, located in south-central region of the Mojave Desert, with plant macrofossil data from several woodrat midden localities in the region. The record for the Cima volcanic field reveals a vegetation history spanning the last ~ 8300 Cal. years B.P., with data from ancient woodrat middens detailing the record from ~17500 Cal. years B.P. to 7,800 Cal. years B.P. A Bryson macro-physical climate model for the transition from the late Pleistocene to the Late Holocene was created and compared to our findings to assess possible relationships between climatic variations and the arrival of diagnostic plant species within the Mojave Desert.

Pollination is a key ecological process sustaining biodiversity and food security, yet global patterns of plant–pollinator specialization remain unresolved. Using a global dataset of quantitative plant–pollinator networks (>3,400 networks, >110,000 interactions), we show that specialization is latitudinally structured, but not as a single directional pattern. We find no consistent monotonic increase or decrease in specialization towards the Equator. Instead, network-level and pollinator specialization peak at low northern latitudes near the tropical–subtropical boundary, whereas plant specialization is hemispherically asymmetric. Climate is most often the best-supported predictor of specialization compared with latitude, biodiversity and environmental productivity. Network-level specialization declines with increasing mean annual temperature, with strong group-specific differences. Precipitation exerts nonlinear effects: plant specialization peaks at intermediate precipitation but is lower at the wettest sites, whereas pollinator specialization is lower at intermediate precipitation, although often in interaction with temperature. Pollinator groups diverge strongly, especially in contrasting precipitation-related patterns between birds and insects. These findings show that global plant–pollinator specialization is structured by latitude and climate in complex and taxon-specific ways, with implications for uneven responses of pollination networks to future climatic change.

Environmental change is reshaping ecological communities, yet conservation efforts often continue to focus on protecting species rather than the interactions that sustain biodiversity. Butterfly–plant interaction networks include well-studied systems that are an important component of biodiversity and can be foundational for examining how ecological relationships respond to climate change, habitat alteration, species introductions, restoration, and conservation management. Most studies of these interactions rely on observational records, plant use data, pollen load evidence, and historical specimens to reconstruct how butterflies use plant communities and how these relationships vary across environmental contexts. However, these studies remain scattered across disparate ecosystems, and network data have not been integrated to explain how butterfly–plant interactions are reorganized under environmental change. Here, we synthesize the literature to evaluate how butterfly–plant interactions are reshaped by environmental change in the United States. The evidence indicates that the most impactful environmental change drivers include: increases in temperature, drought, extreme weather, habitat fragmentation, land use change, species introductions, and conservation interventions. These environmental change parameters alter butterfly–plant interactions through partner shifts, temporal mismatch, reduced plant resource diversity, altered plant availability, weakening of specialized links, and increased reliance on generalist plant species. Strong shifts in butterfly–plant interactions may occur even when butterfly and plant species remain present and even abundant, making interaction loss an early signal of ecological change. A network perspective is useful because it can reveal whether specialized or rare butterfly–plant links persist or whether environmental stress shifts communities toward interactions in which butterflies utilize fewer widely used plant resources or plants are reliant on fewer species of butterflies. By integrating observational evidence, historical specimen-based pollen records, restoration contexts, and network analysis, this review provides an interaction-centered framework for understanding and conserving butterfly–plant relationships in changing environments, with implications for ecological sustainability.