Modeling the time-varying 3D appearance of plants during growth poses unique challenges: unlike most dynamic scenes, plants continuously generate new geometry as they expand, branch, and differentiate. Existing dynamic scene representations are ill-suited to this setting: deformation fields provide insufficient constraints to yield physically plausible scene dynamics, and 4D Gaussian splatting represents the same physical structures with different Gaussian primitives at different times, breaking temporal consistency. We introduce GrowFlow, a dynamic representation that couples 3D Gaussian primitives with a neural ordinary differential equation to model plant growth as a continuous flow field over geometric parameters (position, scale, and orientation). Our representation enables consistent appearance rendering and models nonlinear, continuous-time growth dynamics with full temporal correspondences for every primitive. To initialize a sufficient set of Gaussian primitives, we first reconstruct the mature plant and then learn a reverse-growth process, effectively simulating the plant's developmental history in reverse. GrowFlow achieves superior image quality and geometric coherence compared to prior methods on a new, multi-view timelapse dataset of plant growth, and provides the first temporally coherent representation for appearance modeling of growing 3D structures.
This study aims to analyse the current development of the Forestry Sector regarding the impact of forest degradation in Indonesia on Gross Domestic Product (GDP), income, and employment, and also the consequences on the relationship between employment status and the welfare of forestry sector workers. The main findings show that forest degradation at around IDR 683 billion leads to an estimated loss of IDR 882 billion in GDP, reduction of IDR 261 billion in income, and the loss of approximately 4735 workforce in 2022. On the micro level, the study finds that informal workers are less likely to have access to financial services and are more likely to experience food insecurity, but it is not show a statistically significant access to healthcare.
Kevin B. Rice, Christopher Bergh, Erik J. Bergmann
et al.
Brown marmorated stink bug, Halyomorpha halys Stal, is an invasive, herbivorous insect species that was accidentally introduced to the United States from Asia. First discovered in Allentown, PA, in 1996, H. halys has now been reported from at least 40 states in the United States. Additional invasions have been detected in Canada, Switzerland, France, Germany, Italy, and Lichtenstein, suggesting this invasive species could emerge as a cosmopolitan pest species. In its native range, H. halys is classified as an outbreak pest; however, in North America, H. halys has become a major agricultural pest across a wide range of commodities. H. halys is a generalist herbivore, capable of consuming >100 different species of host plants, often resulting in substantial economic damage; its feeding damage resulted in US$37 million of losses in apple in 2010, but this stink bug species also attacks other fruit, vegetable, field crop, and ornamental plant species. H. halys has disrupted integrated pest management programs for multiple cropping systems. Pesticide applications, including broad-spectrum insecticides, have increased in response to H. halys infestations, potentially negatively influencing populations of beneficial arthropods and increasing secondary pest outbreaks. H. halys is also challenging because it affects homeowners as a nuisance pest; the bug tends to overwinter in homes and outbuildings. Although more research is required to better understand the ecology and biology of H. halys , we present its life history, host plant damage, and the management options available for this invasive pest species.
Sai Nath Chowdary Medikonduru, Hongpeng Jin, Yanzhao Wu
Plant diseases pose a significant threat to global agriculture, causing over $220 billion in annual economic losses and jeopardizing food security. The timely and accurate detection of these diseases from plant leaf images is critical to mitigating their adverse effects. Deep neural network Ensembles (Deep Ensembles) have emerged as a powerful approach to enhancing prediction accuracy by leveraging the strengths of diverse Deep Neural Networks (DNNs). However, selecting high-performing ensemble member models is challenging due to the inherent difficulty in measuring ensemble diversity. In this paper, we introduce the Synergistic Diversity (SQ) framework to enhance plant disease detection accuracy. First, we conduct a comprehensive analysis of the limitations of existing ensemble diversity metrics (denoted as Q metrics), which often fail to identify optimal ensemble teams. Second, we present the SQ metric, a novel measure that captures the synergy between ensemble members and consistently aligns with ensemble accuracy. Third, we validate our SQ approach through extensive experiments on a plant leaf image dataset, which demonstrates that our SQ metric substantially improves ensemble selection and enhances detection accuracy. Our findings pave the way for a more reliable and efficient image-based plant disease detection.
Molecular communication (MC) studies biological signals that are found in nature. Most MC literature focuses on particle properties, even though many natural phenomena exhibit wave-like behavior. One such signal is sound waves. Understanding how sound waves are used in nature can help us better utilize this signal in our interactions with our environment. To take a step in this direction, in this paper, we examine how plants process incoming sound waves and take informed actions. Indeed, plants respond to sound, yet no quantitative communication-theoretic model currently explains this behavior. This study develops the first end-to-end acoustic communication framework for plants. The model is formed following the biological steps of the incoming signal, and a mathematical description is constructed at each step following basic biological models. The resulting end-to-end communication-theoretic model is analyzed using MATLAB. Simulations show that a $200$ $Hz$, $20$ $mu Pa$ stimulus elevates cytosolic $Ca^{2+}$ from $150$ $nM$ to $230 \pm 10$ $nM$ within $50$ seconds which can cause root bending in plants in the long run. This work establishes quantitative phytoacoustics, enabling bio-inspired acoustic connections for precision agriculture and plant signaling research.
Jabulani Nyengere, Precious Masuku, Weston Mwase
et al.
Deforestation remains a prominent issue in Malawi's forest reserves and village forest areas. The Kapirimutu village forest has exhibited a significant increase in forest cover over the previous decade due to community involvement in natural forest regeneration (NFR) initiatives. This study investigates the determinants of household participation in the successful NFR initiatives in the Kapirimutu village forest, a model forest in central Malawi, which has witnessed a substantial increase in forest cover (12 % to > 65 % between 2003 and 2023). Analyzing socio-demographic, institutional, and economic factors using a logistic regression model, the findings reveal that institutional support, particularly NFR incentives and group membership, are strong positive predictors of participation. Environmental awareness and social capital also significantly enhance engagement. Conversely, higher education levels and income diversification negatively influence participation, suggesting potential trade-offs with alternative livelihood strategies. Economic factors such as livestock ownership and forest dependency positively correlate with participation, while greater market distance and perceived opportunity costs act as deterrents. Notably, the sex of the household head and household size were not significant predictors. These results underscore the critical role of institutional mechanisms and economic context in driving community-led forest restoration, highlighting the need for tailored incentives and strategies to engage diverse household profiles and ensure the long-term sustainability of NFR efforts in the area.
The study of ecological networks is crucial for modern conservation biology, addressing habitat fragmentation and biodiversity loss, especially in complex regions. These networks, including corridors, sources, and nodes, are key for species movement and ecosystem functioning. The Periphery Analysis Model (PAM) is introduced as a new approach to study the periphery of these networks, focusing on peripheral nodes' role in environmental change response and network resilience. PAM, drawing from graph theory, complex network analysis, and landscape ecology, uses the Periphery Uniqueness Index (PuI) and the Periphery Balance Index (PbI) to measure peripheral nodes' attributes and balance. It also offers derived indices for a detailed understanding of the periphery's influence. By revealing the periphery's defining characteristics, PAM enhances knowledge of ecological networks' structural features, providing insights for biodiversity, connectivity, and ecosystem health. The research encourages integrating PAM into conservation strategies to inform policy for ecosystem preservation amid environmental challenges.
Feedbacks between evolution and ecology are ubiquitous, with ecological interactions determining which mutants are successful, and these mutants in turn modifying community structure. We study the evolutionary dynamics of several ecological models with overlapping niches, including consumer resource and Lotka-Volterra models. Evolution is assumed slow and extinctions are permanent, with ecological dynamics reaching a stable fixed point between introductions of invaders or mutants. When new strains are slowly added to the community, the ecosystem converges, after an initial evolutionary transient, to a diverse eco-evolutionary steady state. In this "Red Queen" phase of continual evolution, the biodiversity continues to turn over without the invasion probability of new variants getting any smaller. For resource-mediated interactions, the Red Queen phase obtains for any amount of asymmetry in the interactions between strains, and is robust to "general fitness" differences in the intrinsic growth rates of strains. Via a dynamical mean field theory framework valid for high-dimensional phenotype space, we analytically characterize the Red Queen eco-evolutionary steady state in a particular limit of model parameters. Scaling arguments enable a more general understanding of the steady state and evolutionary transients toward it. This work therefore establishes simple models of continual evolution in an ecological context without host-pathogen arms races, and points to the generality of Red Queen evolution. However, we also find other eco-evolutionary phases in simple models: For generalized Lotka-Volterra models with weakly asymmetric interactions an "oligarch" phase emerges in which the evolutionary dynamics continually slow down and a substantial fraction of the community's abundance condenses into a handful of slowly turning-over strains.
Accurate predictions and representations of plant growth patterns in simulated and controlled environments are important for addressing various challenges in plant phenomics research. This review explores various works on state-of-the-art predictive pattern recognition techniques, focusing on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide a comprehensive examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback. This review surveys advances in 2D and 3D structured data representations through functional-structural plant models and conditional generative models. We offer a perspective on opportunities for future works, emphasizing the integration of domain-specific knowledge to data-driven methods, improvements to available datasets, and the implementation of these techniques toward real-world applications.
The Pleistocene Epoch, marked by significant climatic fluctuations and glaciations, profoundly impacted plant populations. However, our understanding of the influences of last glaciations on tropical-subtropical flora and vegetation remains limited due to insufficient data. Here, we present mummified wood of Magnolia insignis (Wall.) Bl. from the Upper Pleistocene (33–30 ka cal. BP) of Maoming, South China, providing direct evidence of a broader historical range for this species during the period prior to the LGM in the last glaciation. Combining these findings with results from MaxEnt modeling, we demonstrate an expanded range of M. insignis into lower latitudes during last glaciation with subsequent interglacial contraction. This represents the second documented case of such a scenario for a cold-tolerant high-elevation plant species at low latitudes. The results of MaxEnt modeling and a comparison of climatic data across different time periods indicate that the contraction of M. insignis from the Maoming and other low latitude regions of East Asia was driven by the increase in summer temperatures during Holocene. This study not only sheds light on the responses of cold-adapted mountainous species at low latitudes of East Asia to last glaciation, but also justifies the importance of their protection in the view of nowadays and future climate changes.
Biochar has the potential to become a more promising carbon source with a broad range of applications in soil agroecosystems. The depletion of soil minerals is a big problem due to soil erosion and leaching of nutrients. Biochar is a soil conditioner used in crop production to improve soil profile, increase fertilizer use efficiency, plant growth and development, carbon sequestration, and reduce greenhouse gas emissions. It is persistent in the environment and retains water, nutrients, and contaminants. It can also be applied in environmental rehabilitation for remediating contaminated soil. Applied biochar significantly enhanced the nutrients in the rhizospheric soil and reduced the bioavailability and uptake of heavy metals. The enhancement of soil nutrient values was the major functional mechanism for enhancing plant protection and production. Overall, the results of this mini-review are significant because they provide the strategies and technological direction for using biochar in sustainable agricultural systems.
ABSTRACT Competition in mesic sites and drought stress combined with short growing seasons in drier sites are key environmental factors along macroclimatic aridity gradients. They impose a triangular trade‐off for local adaptation. However, as experiments have rarely disentangled their effects on plant fitness, uncertainty remained whether mesic populations are indeed better competitors and drier populations better adapted to drought stress and short season length. Aridity differs also at microclimatic scale between north (more mesic) and south (more arid) exposed hill‐slopes. Little is known whether local adaptation occurs among exposures and whether south exposures harbor conspecifics better adapted to drier climates that could provide adaptive reservoirs under climate change. We sampled two Mediterranean annuals (Brachypodium hybridum, Hedypnois rhagadioloides) in 15 sites along a macroclimatic aridity gradient (89–926 mm rainfall) on corresponding north and south exposures. In a large greenhouse experiment, we measured their fitness under drought stress, competition, and short vs. long growing seasons. Along the macroclimatic gradient, mesic populations were better competitors under benign conditions. Drier populations performed no better under drought stress per se but coped better with the short growing seasons typical for drier macroclimates. At microclimatic scale, north exposure plants were slightly better competitors in H. rhagadioloides; in B. hybridum, south exposure plants coped better with drought under short season length. We demonstrate that local adaptation to drier macroclimates is trading‐off with competitive ability under benign conditions and vice‐versa. Drought escape via short life‐cycles was the primary adaptation to drier macroclimates, suggesting that intensified drought stress within the growing season under climate change challenges arid and mesic populations alike. Moreover, the drier microclimates at south exposures exhibited some potential as nearby reservoirs of drier‐adapted genotypes. This potential needs further investigation, yet may assist populations to persist under climate change and lessen the need for long‐distance migration.
Harrie G.J.M. van der Hagen, Erik Lammers, Frank van der Meulen
et al.
The vegetation of coastal sand dunes is characterized by high species diversity and comprises some of the rarest vegetation types in North-Western Europe. Among them are dune grassland communities whose species richness relies on grazing. Those communities are assessed as a priority habitat type under the Natura 2000 legislation. In autumn 1990, Galloway cows and Nordic Fjord horses were introduced in the coastal dunes of Meijendel near The Hague (52°7'N, 4°20'E), The Netherlands, to reduce encroachment of tall grasses and shrubs, to develop bare sand patches, and as such facilitating diverse vegetation structures in the dune grasslands. In the 1950s, decades before the introduction of livestock, 41 permanent plots were installed. On average, they were examined every four years. Our study hypothesised that the livestock grazing in the set densities would halt progressive succession and facilitate regressive succession. Up to 1990, we observed an equilibrium between progressive and regressive succession. After 1990, however, our data showed a pronounced progressive succession contradicting the hypothesized effect of the livestock grazing. We relate the main observed patterns with two factors linked to rabbit populations: (i) the myxomatosis outbreak in 1954 and (ii) the rabbit Viral Haemorrhagic Disease (rVHD-1) outbreak in 1989. In addition to livestock grazing, rabbits block progressive succession by feeding on seedlings of shrub and tree species and digging burrows, creating small-scale mosaics of bare sand and initiate blowout development when collapsing. We state that the substantial decrease in rabbit numbers due to the viral diseases likely caused the observed increase of shrubs and trees in the study area's permanent plots. Climate change might have contributed to the observed increase in autonomous blowout development since 2001, as well as a decrease in atmospheric nitrogen deposition since 1990, after a strong increase the decades before.
ABSTRACT The microbial process of denitrification is the primary source of the greenhouse gas nitrous oxide (N2O) from terrestrial ecosystems. Fungal denitrifiers, unlike many bacteria, lack the N2O reductase, and thereby are sources of N2O. Still, their diversity, global distribution, and environmental determinants, as well as their relative importance, compared to bacterial and archaeal denitrifiers, remain unresolved. Employing a phylogenetically informed approach to analyze 1,980 global soil and rhizosphere metagenomes for the denitrification marker gene nirK, which codes for the copper dependent nitrite reductase in denitrification, we show that fungal denitrifiers are sparse, yet cosmopolitan and that they are dominated by saprotrophs and pathogens. Few showed biome-specific distribution patterns, although members of the Fusarium oxysporum species complex, which are known to produce substantial amounts of N2O, were proportionally more abundant and diverse in the rhizosphere than in other biomes. Fungal denitrifiers were most frequently detected in croplands, but they were most abundant in forest soils when normalized to metagenome size. Nevertheless, the overwhelming dominance of bacterial and archaeal denitrifiers suggests a much lower fungal contribution to N2O emissions than was previously estimated. In relative terms, they could play a role in soils that are characterized by a high carbon to nitrogen ratio and a low pH, especially in the tundra as well as in boreal and temperate coniferous forests. Because global warming predicts the proliferation of fungal pathogens, the prevalence of potential plant pathogens among fungal denitrifiers and the cosmopolitan distribution of these organisms suggest that fungal denitrifier abundance may increase in terrestrial ecosystems. IMPORTANCE Fungal denitrifiers, in contrast to their bacterial counterparts, are a poorly studied functional group within the nitrogen cycle, even though they produce the greenhouse gas N2O. To curb soil N2O emissions, a better understanding of their ecology and distribution in soils from different ecosystems is needed. Here, we probed a massive amount of DNA sequences and corresponding soil data from a large number of samples that represented the major soil environments for a broad understanding of fungal denitrifier diversity at the global scale. We show that fungal denitrifiers are predominantly cosmopolitan saprotrophs and opportunistic pathogens. Fungal denitrifiers constituted, on average, 1% of the total denitrifier community. This suggests that earlier estimations of fungal denitrifier abundance, and, thereby, it is also likely that the contributions of fungal denitrifiers to N2O emissions have been overestimated. Nevertheless, with many fungal denitrifiers being plant pathogens, they could become increasingly relevant, as soilborne pathogenic fungi are predicted to increase with ongoing climate change.
We developed a simulator to quantify the effect of changes in environmental parameters on plant growth in precision farming. Our approach combines the processing of plant images with deep convolutional neural networks (CNN), growth curve modeling, and machine learning. As a result, our system is able to predict growth rates based on environmental variables, which opens the door for the development of versatile reinforcement learning agents.
Vlado Kušec, Stjepan Sito , Goran Fabijanić
et al.
The paper deals with the review of various technological procedures and the possibility of applying additional equipment on sprayers in the protection of field crops and from the aspect of rational application of pesticides such as herbicides, insecticides and fungicides. The use of accessories such as extended hoses in the application of herbicides, a nozzle adjustment assembly for insecticides and fungicides and a device (bio-collector) for collecting insects, can contribute the same effect in protection with lower consumption and less treatment of non-target surfaces.
<i>Ceratophyllum</i> L. is a cosmopolitan genus of perennial aquatic herbs that occur in quiet freshwaters. Fossils of this genus have been widely reported from the Northern Hemisphere, most of them occurring in the temperate zone. Here, we describe two species of fossil fruits discovered from subtropical areas of China. The fossil fruit discovered from the upper Eocene Huangniuling Formation of the Maoming Basin is designated as <i>C</i>. cf. <i>muricatum</i> Chamisso, and fruits discovered from the Miocene Erzitang Formation of the Guiping Basin are assigned to the extant species <i>C</i>. <i>demersum</i> L. The discovery of these two fossil species indicates that <i>Ceratophyllum</i> had spread to South China by the late Eocene and their distribution expanded in subtropical China during the Miocene.