Terrestrial net primary production (NPP) is sensitive to a number of controls, including aspects of climate, topography, soils, plant and microbial characteristics, disturbance, and anthropogenic impacts. Yet, at least at the global scale, models based on very different types and numbers of parameters yield similar results. Part of the reason for this is that the major NPP controls influence each other, resulting, under current conditions, in broad correlations among controls. NPP models that include richer suites of controlling parameters should be more sensitive to conditions that disrupt the broad correlations, but the current paucity of global data limits the power of complex models. Improved data sets will facilitate applications of complex models, but many of the critical data are very difficult to produce, especially for applications dealing with the past or future. It may be possible to overcome some of the challenges of data availability through increased understanding and modeling of ecological processes that adjust plant physiology and architecture in relation to resources. The CASA (Carnegie, Stanford, Ames Approach) model introduced by Potter et al. (1993) and expanded here uses a combination of ecological principles, satellite data, and surface data to predict terrestrial NPP on a monthly time step. CASA calculates NPP as a product of absorbed photosynthetically active radiation, APAR, and an efficiency of radiation use, ϵ. The underlying postulate is that some of the limitations on NPP appear in each. CASA estimates annual terrestrial NPP to be 48 Pg and the maximum efficiency of PAR utilization (ϵ∗) to be 0.39 g C MJ−1 PAR. Spatial and temporal variation in APAR is more than fivefold greater than variation in ϵ.
AI-guided classification of ecological families, genera, and species underpins global sustainability efforts such as biodiversity monitoring, conservation planning, and policy-making. Progress toward this goal is hindered by long-tailed taxonomic distributions from class imbalance, along with fine-grained taxonomic variations, test-time spatiotemporal domain shifts, and closed-set assumptions that can only recognize previously seen taxa. We introduce the Open-World Ecological Taxonomy Classification, a unified framework that captures the co-occurrence of these challenges in realistic ecological settings. To address them, we propose TaxoNet, an embedding-based encoder with a dual-margin penalization loss that strengthens learning signals from rare underrepresented taxa while mitigating the dominance of overrepresented ones, directly confronting interrelated challenges. We evaluate our method on diverse ecological domains: Google Auto-Arborist (urban trees), iNat-Plantae (Plantae observations from various ecosystems in iNaturalist-2019), and NAFlora-Mini (a curated herbarium collection). Our model consistently outperforms baselines, particularly for rare taxa, establishing a strong foundation for open-world plant taxonomic monitoring. Our findings further show that general-purpose multimodal foundation models remain constrained in plant-domain applications.
While a characterization of plant-model mismatch is necessary for robust control, the mismatch usually can not be described accurately due to the lack of knowledge about the plant model or the complexity of nonlinear plants. Hence, this paper considers this problem in a data-driven way, where the mismatch is captured by parametric forms of integral quadratic constraints (IQCs) and the parameters contained in the IQC equalities are learned from sampled trajectories from the plant. To this end, a one-class support vector machine (OC-SVM) formulation is proposed, and its generalization performance is analyzed based on the statistical learning theory. The proposed approach is demonstrated by a single-input-single-output time delay mismatch and a nonlinear two-phase reactor with a linear nominal model, showing accurate recovery of frequency-domain uncertainties.
Simon Ravé, Jean-Christophe Lombardo, Pejman Rasti
et al.
We present a zero-shot segmentation approach for agricultural imagery that leverages Plantnet, a large-scale plant classification model, in conjunction with its DinoV2 backbone and the Segment Anything Model (SAM). Rather than collecting and annotating new datasets, our method exploits Plantnet's specialized plant representations to identify plant regions and produce coarse segmentation masks. These masks are then refined by SAM to yield detailed segmentations. We evaluate on four publicly available datasets of various complexity in terms of contrast including some where the limited size of the training data and complex field conditions often hinder purely supervised methods. Our results show consistent performance gains when using Plantnet-fine-tuned DinoV2 over the base DinoV2 model, as measured by the Jaccard Index (IoU). These findings highlight the potential of combining foundation models with specialized plant-centric models to alleviate the annotation bottleneck and enable effective segmentation in diverse agricultural scenarios.
Lucy Deba Enomah, Joni Downs, Michael Acheampong
et al.
Using LULC change detection analysis, it is possible to identify changes due to urbanization, deforestation, or a natural disaster in an area. As population growth and urbanization increase, real-time solutions for the effects of urbanization on land use are required to assess its implications for food security and livelihood. This study seeks to identify and quantify recent LULC changes in Limbe, Cameroon, and to measure rates of conversion between agricultural, forest, and urban lands between 1986 and 2020 using remote sensing and GIS. Also, there is a deficiency of research employing these data to evaluate the efficiency of LULC satellite data and a lack of awareness by local stakeholders regarding the impact on LULC change. The changes were identified in four classes utilizing maximum supervised classification in ENVI and ArcGIS environments. The classification result reveals that the 2020 image has the highest overall accuracy of 94.6 while the 2002 image has an overall accuracy of 89.2%. The overall gain for agriculture was approximately 4.6 km<sup>2</sup>, urban had an overall gain of nearly 12.7 km<sup>2</sup>, while the overall loss for forest was −16.9 km<sup>2</sup> during this period. Much of the land area previously occupied by forest is declining as pressures for urban areas and new settlements increase. This study’s findings have significant policy implications for sustainable land use and food security. It also provides a spatial method for monitoring LULC variations that can be used as a framework by stakeholders who are interested in environmentally conscious development and sustainable land use practices.
Justin Dossou, Adigla A. Wédjangnon, Christine A.I.N. Ouinsavi
Understanding the impact of leaf harvesting of fodder trees among other threats to forests is an important debate in ecology with implications for biodiversity conservation. This study assessed the impacts of leaf harvesting on the population structure of Afzelia africana Smith ex Pers., Khaya senegalensis (Desr.) A. Juss. and Pterocarpus erinaceus Poir.. The study assessed the density, abundance, frequency and regeneration dynamics of the three species in three categories of forests by installing fifty-four experimental plots of 1 ha in nine forest reserves in two ecological zones of Benin. The regeneration dynamics were monitored for 3 years (2021, 2022 and 2023). Data on regeneration density and large trees, diameter at breast height of large trees and regeneration diameter were collected in three forest categories according to anthropogenic disturbance. Mean tree diameter, mean density per hectare, abundance and regeneration dynamics for each of the three species revealed a significant difference (p< 0.05) according to anthropogenic disturbances and ecological zone. Mean tree density of the three species was higher (p< 0.05) for all three species in control forests. The highest mean tree diameter for all three species was found in forests subjected only to tree leaf harvesting. All three species have low abundance and high frequency of occurrence according to different anthropogenic disturbances. Regeneration density of the three fodder species changed slightly in forests where tree were logged and tree leaf harvested. Particularly Pterocarpus erinaceus was found to adapt better to different anthropogenic disturbances than Afzelia africana and Khaya senegalensis. Our results highlight the direct negative influence of leaf harvesting and timber exploitation on the population dynamics of woody forage species in terms of structure and density. Limiting leaf harvesting intensity to 50 % per tree may lead to better population dynamics of forage species.
Niranjana Krishnan, Cassandra Gorman, Jillian Stewart
et al.
Abstract Previously, we reported final-instar lepidopteran larvae exposed to low doses of imidacloprid, clothianidin, and thiamethoxam had arrest in pupal ecdysis, which is a novel adverse outcome for neonicotinoid insecticides. Since neonicotinoids disrupt acetylcholine signaling, we hypothesized that the excitatory neurotransmitter acetylcholine plays a critical role in regulation of pupal ecdysis, likely by modulating the release of peptides from crustacean cardioactive peptide (CCAP) neurons. In this paper, using two lepidopteran species, we undertook studies with five additional nicotinic acetylcholine receptor (nAChR) agonists and three muscarinic acetylcholine receptor (mAChR) agonists to hypothesize the putative nAChR subunits that mediate pupal ecdysis. We also explored the potential role of mAChRs in regulation of pupal ecdysis. These findings, along with toxicokinetic analyses, suggest that pupal ecdysis may be mediated by the α1, β1, and β2 subunits of nAChRs without involvement of mAChRs. An analysis of ecdysis movements showed that neonicotinoid-treated lepidopteran larvae exhibited similar disruptions as observed in CCAP neuron-knockout Drosophila larvae. Based on findings to date, we hypothesize that acetylcholine regulates lepidopteran pupal ecdysis directly through CCAP neurons or by activating their upstream efferent inhibitory (likely GABA-releasing) neurons. Further studies are needed to elucidate the interplay between neuroendocrine hormones and neurotransmitters in lepidopteran pupal ecdysis.
SUMMARY Stress often induces plant trait plasticity, and microbial communities also alter plant traits. Therefore, it is unclear how much plasticity results from direct plant responses to stress versus indirect responses due to stress-induced changes to soil microbial communities. To test how microbes and microbial community responses to stress affect the ecology and potentially the evolution of plant plasticity, I grew plants in four stress environments (salt, herbicide, herbivory, no stress) with microbes that had responded to these same environments or with sterile inoculant. Plants delayed flowering under stress only in the presence of live microbial communities, and this plasticity was maladaptive. However, microbial communities responded to stress in ways that accelerated flowering across all environments. Microbes also affected the expression of genetic variation for plant flowering time and specific leaf area, as well as genetic variation for plasticity of both traits, and disrupted a positive genetic correlation for plasticity in response to herbicide and herbivory stress, suggesting that microbes may affect the pace of plant evolution. Together, these results highlight an important role for soil microbes in plant plastic responses to stress and suggest that microbes may alter the evolution of plant plasticity.
Mozzamil Mohammed, Mohammed AY Mohammed, Abdallah Alsammani
et al.
Carnivores interact with herbivores to indirectly impact plant populations, creating trophic cascades within plant-herbivore-carnivore systems. We developed and analyzed a food chain model to gain a mechanistic understanding of the critical roles carnivores play in ecosystems where plants face intense herbivory. Our model incorporates key factors such as seed production rates, seed germination probabilities, local plant interactions, herbivory rates, and carnivore predation rates. In the absence of carnivores, herbivores significantly reduce plant densities, often driving plants to extinction under high herbivory rates. However, the presence of carnivores suppresses herbivore populations, allowing plants to recover from herbivore pressure. We found that plant densities increase with carnivore predation rates, highlighting top-down effects and underscoring the importance of conserving carnivores in ecosystems where plants are at high risk of extinction from herbivory. Our results also show that carnivore density increases with seed-production rates, while herbivore density remains constant, indicating that plants benefit carnivores more than herbivores. This increase in carnivore density driven by high seed-production rates reflects bottom-up effects in the system. Overall, our study demonstrates that plants, herbivores, and carnivores can coexist even under intense predation stress. It suggests that carnivores play a crucial role in regulating plant and herbivore populations, with significant potential for maintaining biodiversity within ecosystems.