Hasil untuk "Ecology"

Maxime Clenet, Mohammed-Younes Gueddari

Ecological interaction networks are rarely homogeneous: species naturally form communities with distinct interaction structures, resulting in block-structured variance and correlation profiles in the interaction matrix. We study the equilibrium properties of generalized Lotka-Volterra systems whose interaction matrices are random and non-symmetric with variance and correlation profiles. Based on recent advances in approximate message passing (AMP) for heterogeneous and correlated random matrices, we derive a set of self-consistent fixed-point equations that, in the large-$n$ limit, characterize the equilibrium abundance distribution. In particular, we show that this limiting distribution is an explicit mixture of truncated Gaussian, driven by the variance and correlation profiles. We then illustrate the ecological implications of this result through three applications involving two interacting communities. First, we show that local changes in the correlation profile within a single community induce system-wide responses in species persistence, revealing the non-local nature of persistence dynamics. Second, we find that communities dominated by mutualistic or competitive interactions are more robust to increasing inter-community coupling, whereas communities structured by predator-prey interactions are more prone to collapse. Third, we demonstrate that asymmetric interaction variance alone, in the complete absence of correlation, can generate feedback loop between communities.

Qijie Shen, Yuanchen Bei, Zihong Huang et al.

Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. This ''rich-get-richer'' phenomenon hinders the growth of potentially valuable cold items and harms the platform's ecosystem. Existing cold-start models primarily focus on improving initial recommendation performance for cold items but fail to address users' natural preference for popular content. In this paper, we introduce AliBoost, Alibaba's ecological boosting framework, designed to complement user-oriented natural recommendations and foster a healthier ecosystem. AliBoost incorporates a tiered boosting structure and boosting principles to ensure high-potential items quickly gain exposure while minimizing disruption to low-potential items. To achieve this, we propose the Stacking Fine-Tuning Cold Predictor to enhance the foundation CTR model's performance on cold items for accurate CTR and potential prediction. AliBoost then employs an Item-oriented Bidding Boosting mechanism to deliver cold items to the most suitable users while balancing boosting speed with user-personalized preferences. Over the past six months, AliBoost has been deployed across Alibaba's mainstream platforms, successfully cold-starting over a billion new items and increasing both clicks and GMV of cold items by over 60% within 180 days. Extensive online analysis and A/B testing demonstrate the effectiveness of AliBoost in addressing ecological challenges, offering new insights into the design of billion-scale recommender systems.

Qi Cheng, Licheng Liu, Qing Zhu et al.

Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, capturing carbon-nitrogen dynamics, and monitoring hydrological cycles. Traditional numerical metrics (e.g., R-squared, root mean square error) have been widely used to quantify the similarity between modeled and observed ecosystem variables, but they often fail to capture domain-specific temporal patterns critical to ecological processes. As a result, these methods are often accompanied by expert visual inspection, which requires substantial human labor and limits the applicability to large-scale evaluation. To address these challenges, we propose a novel framework that integrates metric learning with large language model (LLM)-based natural language policy extraction to develop interpretable evaluation criteria. The proposed method processes pairwise annotations and implements a policy optimization mechanism to generate and combine different assessment metrics. The results obtained on multiple datasets for evaluating the predictions of crop gross primary production and carbon dioxide flux have confirmed the effectiveness of the proposed method in capturing target assessment preferences, including both synthetically generated and expert-annotated model comparisons. The proposed framework bridges the gap between numerical metrics and expert knowledge while providing interpretable evaluation policies that accommodate the diverse needs of different ecosystem modeling studies.

Ada Altieri

The study of ecological systems is gaining momentum in modern scientific research, driven by an abundance of empirical data and advancements in bioengineering techniques. However, a full understanding of their dynamical and thermodynamical properties, also in light of the ongoing biodiversity crisis, remains a formidable endeavor. From a theoretical standpoint, modeling the interactions within these complex systems -- such as bacteria in microbial communities, plant-pollinator networks in forests, or starling murmurations -- presents a significant challenge. Given the characteristic high dimensionality of the datasets, alternative elegant approaches employ random matrix formalism and techniques from disordered systems. In these lectures, we will explore two cornerstone models in theoretical ecology: the MacArthur/Resource-Consumer model, and the Generalized Lotka-Volterra model, with a special focus on systems composed of a large number of interacting species. In the second part, we will highlight timely directions, particularly to bridge the gap with empirical observations and detect macroecological patterns.

Elia Moretti, Michel Loreau, Michael Benzaquen

The intensification of European agriculture, characterized by increasing farm sizes, landscape simplification and reliance on synthetic pesticides, remains a key driver of biodiversity decline. While many studies have investigated this phenomenon, they often focus on isolated elements, resulting in a lack of holistic understanding and leaving policymakers and farmers with unclear priorities. This study addresses this gap by developing a spatially explicit ecological economic model designed to dissect the complex interplay between landscape structure and pesticide application, and their combined effects on natural enemy populations and farmers' economic returns. In particular, the model investigates how these relationships are modulated by farm size (a crucial aspect frequently overlooked in prior research). By calibrating on the European agricultural sector, we explore the ecological and economic consequences of various policy scenarios. We show that the effectiveness of ecological restoration strategies is strongly contingent upon farm size. Small to medium-sized farms can experience economic benefits from reduced pesticide use when coupled with hedgerow restoration, owing to enhanced natural pest control. In contrast, large farms encounter challenges in achieving comparable economic gains due to inherent landscape characteristics. This highlights the need to account for farm size in agri-environmental policies in order to promote biodiversity conservation and agricultural sustainability.

Kwaku Peprah Adjei, Rob Cooke, Nick Isaac et al.

1. Temporal trends in species distributions are necessary for monitoring changes in biodiversity, which aids policymakers and conservationists in making informed decisions. Dynamic species distribution models are often fitted to ecological time series data using Markov Chain Monte Carlo algorithms to produce these temporal trends. However, the fitted models can be time-consuming to produce and run, making it inefficient to refit them as new observations become available. 2. We propose an algorithm that updates model parameters and the latent state distribution (e.g. true occupancy) using the saved information from a previously fitted model. This algorithm capitalises on the strength of importance sampling to generate new posterior samples of interest by updating the model output. The algorithm was validated with simulation studies on linear Gaussian state space models and occupancy models, and we applied the framework to Crested Tits in Switzerland and Yellow Meadow Ants in the UK. 3. We found that models updated with the proposed algorithm captured the true model parameters and latent state values as good as the models refitted to the expanded dataset. Moreover, the updated models were much faster to run and preserved the trajectory of the derived quantities. 4. The proposed approach serves as an alternative to conventional methods for updating state-space models (SSMs), and it is most beneficial when the fitted SSMs have a long run time. Overall, we provide a Monte Carlo algorithm to efficiently update complex models, a key issue in developing biodiversity models and indicators.

Ranabir Devgupta, Raj Abhijit Dandekar, Rajat Dandekar et al.

In this study, we apply two pillars of Scientific Machine Learning: Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs) to the Lotka Volterra Predator Prey Model, a fundamental ecological model describing the dynamic interactions between predator and prey populations. The Lotka-Volterra model is critical for understanding ecological dynamics, population control, and species interactions, as it is represented by a system of differential equations. In this work, we aim to uncover the underlying differential equations without prior knowledge of the system, relying solely on training data and neural networks. Using robust modeling in the Julia programming language, we demonstrate that both Neural ODEs and UDEs can be effectively utilized for prediction and forecasting of the Lotka-Volterra system. More importantly, we introduce the forecasting breakdown point: the time at which forecasting fails for both Neural ODEs and UDEs. We observe how UDEs outperform Neural ODEs by effectively recovering the underlying dynamics and achieving accurate forecasting with significantly less training data. Additionally, we introduce Gaussian noise of varying magnitudes (from mild to high) to simulate real-world data perturbations and show that UDEs exhibit superior robustness, effectively recovering the underlying dynamics even in the presence of noisy data, while Neural ODEs struggle with high levels of noise. Through extensive hyperparameter optimization, we offer insights into neural network architectures, activation functions, and optimizers that yield the best results. This study opens the door to applying Scientific Machine Learning frameworks for forecasting tasks across a wide range of ecological and scientific domains.

Jack H. Buckner, Zechariah D. Meunier, Jorge Arroyo-Esquivel et al.

Ecological systems often exhibit complex nonlinear dynamics like oscillations, chaos, and regime shifts. Universal dynamic equations have shown promise in modeling complex dynamics by combining known functional forms with neural networks that represent unknown relationships. However, these methods do not yet accommodate the forms of uncertainty common to ecological datasets. To address this limitation, we developed state-space universal dynamic equations by combining universal differential equations with a state-space modeling framework, accounting for uncertainty. We tested this framework on two simulated and two empirical case studies and found that this method can recover nonlinear biological interactions that produce complex behaviors, including chaos and regime shifts. Their forecasting performance is context-dependent, with the best performance being achieved on chaotic and oscillating time series. This new approach leveraging both ecological theory and data-driven machine learning offers a promising new way to make accurate and useful predictions of ecosystem change.

Sérgio da Costa Dias, Andressa Lima de Brida, Maguintontz Cedney Jean-Baptiste et al.

The spotted-wing drosophila, <i>Drosophila suzukii</i> (Matsumura) (Diptera: Drosophilidae), is a pest that reduces the productivity of small fruits. Entomopathogenic nematodes (EPNs) and chemical insecticides can suppress this pest, but the compatibility of the two approaches together requires further examination. This laboratory study evaluated the compatibility of <i>Steinernema brazilense</i> IBCBn 06, <i>S. carpocapsae</i> IBCBn 02, <i>Heterorhabditis amazonensis</i> IBCBn 24, and <i>H. bacteriophora</i> HB with ten chemical insecticides registered for managing <i>D. suzukii</i> pupae. In the first study, most insecticides at the recommended rate did not reduce the viability (% of living infective juveniles (IJs)) of <i>S. braziliense</i> and both <i>Heterorhabditis</i> species. The viability of <i>S. carpocapsae</i> was lowered by exposure to spinetoram, malathion, abamectin, azadirachtin, deltamethrin, lambda-cyhalothrin, malathion, and spinetoram after 48 h. During infectivity bioassays, phosmet was compatible with all the EPNs, causing minimal changes in infectivity (% pupal mortality) and efficiency relative to EPN-only controls, whereas lambda-cyhalothrin generally reduced infectivity of EPNs on <i>D. suzukii</i> pupae the most, with a 53, 75, 57, and 13% reduction in infectivity efficiency among <i>H. bacteriophora, H. amazonensis, S. carpocapsae</i>, and <i>S. brazilense</i>, respectively. The second study compared pupal mortality caused by the two most compatible nematode species and five insecticides in various combinations. Both <i>Heterorhabditis</i> species caused 78–79% mortality among <i>D. suzukii</i> pupae when used alone, and were tested in combination with spinetoram, malathion, azadirachtin, phosmet, or novaluron at a one-quarter rate. Notably, <i>H. bacteriophora</i> caused 79% mortality on <i>D. suzukii</i> pupae when used alone, and 89% mortality when combined with spinetoram, showing an additive effect. Novaluron drastically reduced the number of progeny IJs when combined with <i>H. amazonensis</i> by 270 IJs and <i>H. bacteriophora</i> by 218. Any adult flies that emerged from EPN–insecticide-treated pupae had a shorter lifespan than from untreated pupae. The combined use of <i>Heterorhabditis</i> and compatible chemical insecticides was promising, except for novaluron.

Jorge Arroyo-Esquivel, Christopher A Klausmeier, Elena Litchman

Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modeling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural Ordinary Differential Equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data \cite{chen_neural_2018}. Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than ARIMA models. {We also find that untuned NODEs have a similar forecasting accuracy as untuned Long-Short Term Memory neural networks (LSTMs) and both are outperformed in accuracy and precision by EDM models. However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy.} We also discuss ways to improve the forecasting performance {of NODEs}. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities.

Frontiers Production Office

Pedro Peñaherrera‐R., Diego F. Cisneros‐Heredia

Abstract We describe a new species of giant crab spider of the genus Sadala Simon, 1880 collected in Lowland Evergreen rainforests at the Tiputini Biodiversity Station, Yasuni Biosphere Reserve, Amazonian Ecuador. This new species corresponds to the first record of the genus from Ecuador. Females of the new species of Sadala are similar to S. punicea and S. nanay, by having the epigyne with a median septum diamond‐shaped posteriorly. The new species is easily distinguished from S. punicea and S. nanay by having relatively straight anterior lateral margins of the median septum. This study increases to 10 the number of described species of Sadala.

Jessica Garzke, Ian Forster, Sean C. Godwin et al.

Migrating marine taxa encounter diverse habitats that differ environmentally and in foraging conditions over a range of spatial scales. We examined body (RNA/DNA, length-weight residuals) and nutritional (fatty acid composition) condition of juvenile sockeye salmon (Oncorhynchus nerka) in British Columbia, while migrating through oceanographically variable waters. Fish were sampled in the stratified northern Strait of Georgia (NSoG); the highly mixed Johnstone Strait (JS); and the transitional zone of Queen Charlotte Strait (QCS). In 2015, body and nutritional condition were high in the NSoG but rapidly declined to reach lowest levels in JS where prey availability was low, before showing signs of compensatory growth in QCS. In 2016, juvenile salmon had significantly lower condition in the NSoG than in 2015, although zooplankton biomass was similar, condition remained low in JS, and no compensatory growth was observed in QCS. We provide evidence that differences in juvenile salmon condition between the two years were due to changes in the food quality available to juvenile fish. We propose that existing hypotheses about fish survival need to be extended to incorporate food quality in addition to quantity to understand changes in fish condition and survival between years.

Alexandros Karatopouzis, Alexey A. Voinov, Ida Kubiszewski et al.

In the efforts to ensure the health of the Australian population during the COVID pandemic, social, economic, and environmental aspects of people’s life were impacted. In addressing the pandemic risks, a number of governments prioritized people’s health and well-being over GDP growth. The Genuine Progress Indicator (GPI) is used to account for factors that influence well-being. We used the GPI to assess the pandemic’s impact on well-being and we examined our results in relation to the GDP. We estimated the GPI for the first 6 months of 2019 and the same period in 2020, during which the first stages of the COVID pandemic and the first nationwide lockdown in Australia took place. We examined two scenarios, in the first we found that in Q1 the GDP growth (1.4%) was accompanied by a significant GPI growth (5.3%), showing a positive relation to the GDP; but in Q2 the significant drop (-6.3%) in the GDP was not followed by the GPI, instead the GPI growth remained almost steady with even a relatively small increase (0.33%), indicating a negative relation to the GDP growth.  Whereas in the second scenario, the GPI growths (7.12%) in Q1 and (-2.60%) Q2 were positively related to the GDP growths (4.6%) in Q1 and (−0.25%) Q2.We discuss the reasons for the divergence between the two indicators and one of the limitations of the GPI as a measure of well-being. Lastly, we discuss the behavioural and policy lessons of the lockdown and their relevance to what is proposed by degrowth economists.

Stephen Carpenter, Val Byfield, Stacey L. Felgate et al.

Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R² = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R² = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km² of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m² resolution to underpin evidence-based management and conservation of seagrass.

Laura Bosco, Laura Bosco, Damaris Siegenthaler et al.

Alternative farming methods must be deployed to mitigate the detrimental impacts of intensive agriculture on climate, biodiversity, and ecosystem services. Organic and biodynamic farming are environmental-friendly practices that progressively replace conventional agriculture. While potential biodiversity benefits of organic vs. conventional farming have been studied repeatedly, the effects of biodynamic farming on biodiversity remain ill-understood. We investigated the effects of these three main management regimes, and their interaction with ground vegetation cover, on vineyard invertebrate communities in SW Switzerland. Invertebrates were sampled three times during the vegetation season in 2016, focusing on ground-dwelling (pitfall traps) and epiphytic (sweep-netting) invertebrates, and their abundance was modelled for single, additive, and interactive influences of management and ground vegetation cover. Overall, organic and, but to a lesser degree, biodynamic vineyards provided better conditions for invertebrate abundance than conventional vineyards. On the one hand, there was a significant interaction between management and ground vegetation cover for epiphytic invertebrates with a positive linear increase in abundance in organic, a positive curvilinear relationship in biodynamic but a negative curvilinear response to vegetation cover in conventional vineyards. The abundance of ground-dwelling invertebrates was primarily affected by the management regime alone, i.e. without any interaction with ground vegetation characteristics, leading to much higher abundances in organic compared to conventional vineyards, while biodynamic did not differ from the other two regimes. We interpret the patterns as follows: organic grape production offers more suitable habitat conditions for invertebrates due to a spatially more heterogenous but also less often disturbed (compared to biodynamic management) or destroyed (compared to conventional) ground vegetation cover, in line with the predictions of the intermediate disturbance hypothesis. Biodynamic and conventional viticultural management regimes often provide a habitat that is either too mineral (conventional: ground vegetation widely eliminated) or subject to soil disturbance happening frequently through ploughing (biodynamic). We conclude that alternative farming methods do promote biodiversity in vineyard agro-ecosystems, especially so organic management.

Alexey Ryabov, Bernd Blasius, Helmut Hillebrand et al.

The twin crises of climate change and biodiversity loss define a strong need for functional diversity monitoring. While the availability of high-quality ecological monitoring data is increasing, the quantification of functional diversity so far requires the identification of species traits, for which data is harder to obtain. However, the traits that are relevant for the ecological function of a species also shape its performance in the environment and hence should be reflected indirectly in its spatio-temporal distribution. Thus it may be possible to reconstruct these traits from a sufficiently extensive monitoring dataset. Here we use diffusion maps, a deterministic and de-facto parameter-free analysis method, to reconstruct a proxy representation of the species' traits directly from monitoring data and use it to estimate functional diversity. We demonstrate this approach both with simulated data and real-world phytoplankton monitoring data from the Baltic sea. We anticipate that wider application of this approach to existing data could greatly advance the analysis of changes in functional biodiversity.

Meaghan L. Pimsler, Meaghan L. Pimsler, Meaghan L. Pimsler et al.

Reliability of forensic entomology analyses to produce relevant information to a given case requires an understanding of the underlying arthropod population(s) of interest and the factors contributing to variability. Common traits for analyses are affected by a variety of genetic and environmental factors. One trait of interest in forensic investigations has been species-specific temperature-dependent growth rates. Recent work indicates sexual dimorphism may be important in the analysis of such traits and related genetic markers of age. However, studying sexual dimorphic patterns of gene expression throughout immature development in wild-type insects can be difficult due to a lack of genetic tools, and the limits of most sex-determination mechanisms. Chrysomya rufifacies, however, is a particularly tractable system to address these issues as it has a monogenic sex determination system, meaning females have only a single-sex of offspring throughout their life. Using modified breeding procedures (to ensure single-female egg clutches) and transcriptomics, we investigated sexual dimorphism in development rate and gene expression. Females develop slower than males (9 h difference from egg to eclosion respectively) even at 30°C, with an average egg-to-eclosion time of 225 h for males and 234 h for females. Given that many key genes rely on sex-specific splicing for the development and maintenance of sexually dimorphic traits, we used a transcriptomic approach to identify different expression of gene splice variants. We find that 98.4% of assembled nodes exhibited sex-specific, stage-specific, to sex-by-stage specific patterns of expression. However, the greatest signal in the expression data is differentiation by developmental stage, indicating that sexual dimorphism in gene expression during development may not be investigatively important and that markers of age may be relatively independent of sex. Subtle differences in these gene expression patterns can be detected as early as 4 h post-oviposition, and 12 of these nodes demonstrate homology with key Drosophila sex determination genes, providing clues regarding the distinct sex determination mechanism of C. rufifacies. Finally, we validated the transcriptome analyses through qPCR and have identified five genes that are developmentally informative within and between sexes.

Shengnan Li, Shih-Hsin Ho, Tao Hua et al.

Microbial fuel cells (MFCs) have gained remarkable attention as a novel wastewater treatment that simultaneously generates electricity. The low activity of the oxygen reduction reaction (ORR) remains one of the most critical bottlenecks limiting the development of MFCs. To date, although research on biochar as an electrocatalyst in MFCs has made tremendous progress, further improvements are needed to make it economically practical. Recently, biochars have been considered to be ORR electrocatalysts with developmental potential. In this review, the ORR mechanism and the essential requirements of ORR catalysts in MFC applications are introduced. Moreover, the focus is to highlight the material selection, properties, and preparation of biochar electrocatalysts, as well as the evaluation and measurement of biochar electrodes. Additionally, in order to provide comprehensive information on the specific applications of biochars in the field of MFCs, their applications as electrocatalysts, are then discussed in detail, including the uses of nitrogen-doped biochar and other heteroatom-doped biochars as electrocatalysts, poisoning tests for biochar catalysts, and the cost estimation of biochar catalysts. Finally, profound insights into the current challenges and clear directions for future perspectives and research are concluded.

Dameng Yin, Le Wang, Zhenduo Zhu et al.

Water quality is affected by croplands. The Conservation Reserve Program (CRP), where farmers convert croplands to natural land cover (e.g., trees), is expected to improve water quality. However, whether such improvements are achieved alongside cropland area change has not been verified at river-basin scales, due to challenges in large scale observations. Therefore, aiming to quantify the relationship between CRP enrollment, cropland area, and the downstream water quality, we propose an approach that combines archived survey data, water quality monitoring data (total nitrogen content, TN), and remote sensing observations. By constructing the long-term datasets (1999–2014 annually) in Google Earth Engine and conducting multiple linear regression, we explained 79% variation in TN by the area of total CRP enrollment (CRP_all), area of corn and soybeans croplands, and discharge. Moreover, 78% is explained if we consider only water quality targeted conservation practices (CRP_WQ). Our results indicate significant positive correlation between CRP enrollment (both CRP_all and CRP_WQ) and the downstream water quality. Nevertheless, it should be noted that correlation does not necessarily represent causation. While this pioneer effort of quantifying impacts of the CRP on water quality from large scale observations has achieved some success, we call for more research to expand the spatial and/or temporal scales and consider more water quality variables, so as to further enhance our understanding of the coupled natural-and-human system.