Hasil untuk "Ecology"

J. Metz, R. Nisbet, S. Geritz

Eric Bazin, S. Glémin, N. Galtier

L. Bracken, J. Croke

Chen-Quan Gu, Chen-Quan Gu, Chen-Quan Gu et al.

Malus sieversii, a Tertiary relict and primary progenitor of the cultivated apple, is experiencing severe habitat degradation in China’s Tianshan Mountains. To understand how soil ecosystem functions respond to tree vigor decline, we monitored surface soils beneath the canopy of wild apple trees monthly from April to October. Trees were classified into three vigor classes based on the percentage of dead branches: Vigor Class I (<20%), Vigor Class II (40–60%), and Vigor Class III (>80%). Soil multifunctionality (SMF) and temporal variability of nutrients (TVN) were derived from seven key nutrient indicators. Soils under Vigor Class II trees exhibited the lowest SMF and highest TVN, indicating maximal functional instability during intermediate degradation. While SMF peaked and TVN reached its seasonal minimum in October, Vigor Class II showed a consistent decline in TVN over time, unlike the irregular fluctuations in Vigor Classes I and III. A significant negative SMF–TVN correlation in Vigor Classes II and III suggests a trade-off between functionality and stability. Partial least squares path modeling revealed that soil organic carbon, total nitrogen, and total phosphorus were the dominant direct driver of both SMF and TVN, with climate exerting no significant direct effects once tree vigor and soil conditions were accounted for. These results suggest that Vigor Class II represents a critical early-warning stage: soil functional capacity begins to deteriorate before visible signs of severe tree decline or mortality. Targeted ecological restoration of Vigor Class II trees is essential to prevent irreversible ecosystem degradation. Therefore, while continued protection of healthy Vigor Class I trees remains essential, conservation efforts should place greater emphasis on restoring Vigor Class II trees to disrupt degradation feedbacks before irreversible ecosystem decline occurs.

J. P. Uchima-Tamayo, R. Angeloni, M. Jaque Arancibia et al.

Light pollution, a rapidly escalating anthropogenic phenomenon driven by the excessive and often inefficient use of artificial lighting, has profound implications for astronomy, ecology, and human health. This study presents the first comprehensive characterization of night sky quality in Colombia, focusing on sites of astronomical and ecological significance. The selected locations include the Astronomical Observatory of UTP, the Tatacoa Desert, the Bogotá Botanical Garden, and Cerro Guadalupe. Utilizing the Sky Quality Camera, we collected all-sky data to measure surface brightness and correlated color temperature of the night sky. Our findings reveal a significant loss of natural sky visibility in urban areas and demonstrate the detrimental effects of artificial lighting on critical astronomical sites such as La Tatacoa. This study provides a crucial foundation for future research and informs the development of public policies aimed at preserving the night sky.

Wenxia Wang, Guojun Zhou, Wei Zhang et al.

ABSTRACT Resource availability should have consequences for life‐history functions and trade‐offs among them because it influences the amounts of resources allocated to different functions. Nutritional status during a key developmental window (sexual maturation) may also have an important impact on life‐history functions and such trade‐offs. However, less is known about whether and how they interact to influence the resource allocation of individuals. Here, we simultaneously manipulated female nutritional status during sexual maturation and resource availability during breeding in a burying beetle Nicrophorus vespilloides. We then monitored the main and interactive effects of these two factors on somatic maintenance and reproductive performance of burying beetle females. We found that variation in nutritional status during sexual maturation affects the resource allocation of burying beetle females only at the pre‐hatching stage. Poor‐fed females compensated for the initial differences in energy reserves by feeding from the carcass or engaged in terminal investment strategy and invested heavily at the post‐hatching stage. Specifically, poor‐fed females allocated more into somatic maintenance (gained more weight) and less into reproduction (provided less pre‐hatching care) than well‐fed females, whereas they provided a similar amount and duration of post‐hatching care. In addition, burying beetles with different nutritional statuses vary in their response to resource availability. Poor‐fed females allocated more into both somatic maintenance (gained more weight) and reproduction (provided more pre‐hatching care) when bred on large versus small carcasses, whereas well‐fed females tend to work near their maximum capacity and thus show no response to resource availability. Finally, our findings suggest that poor‐fed females did not suffer a future cost in offspring performance. Meanwhile, a large carcass allowed females to produce more and heavier offspring. These findings enhance our understanding of how important nutritional status during a key developmental window and resource availability during breeding is for the expression of resource allocation.

Alice Doimo, Giorgio Nicoletti, Davide Bernardi et al.

Spatial metapopulation models are fundamental to theoretical ecology, enabling to study how landscape structure influences global species dynamics. Traditional models, including recent generalizations, often rely on the deterministic limit of stochastic processes, assuming large population sizes. However, stochasticity - arising from dispersal events and population fluctuations - profoundly shapes ecological dynamics. In this work, we extend the classical metapopulation framework to account for finite populations, examining the impact of stochasticity on species persistence and dynamics. Specifically, we analyze how the limited capacity of local habitats influences survival, deriving analytical expressions for the finite-size scaling of the survival probability near the critical transition between survival and extinction. Crucially, we demonstrate that the deterministic metapopulation capacity plays a fundamental role in the statistics of survival probability and extinction time moments. These results provide a robust foundation for integrating demographic stochasticity into classical metapopulation models and their extensions.

Benjamin M. Bolker

Information-theoretic (IT) and multi-model averaging (MMA) statistical approaches are widely used but suboptimal tools for pursuing a multifactorial approach (also known as the method of multiple working hypotheses) in ecology. (1) Conceptually, IT encourages ecologists to perform tests on sets of artificially simplified models. (2) MMA improves on IT model selection by implementing a simple form of shrinkage estimation (a way to make accurate predictions from a model with many parameters relative to the amount of data, by “shrinking” parameter estimates toward zero). However, other shrinkage estimators such as penalized regression or Bayesian hierarchical models with regularizing priors are more computationally efficient and better supported theoretically. (3) In general, the procedures for extracting confidence intervals from MMA are overconfident, providing overly narrow intervals. If researchers want to use limited data sets to accurately estimate the strength of multiple competing ecological processes along with reliable confidence intervals, the current best approach is to use full (maximal) statistical models (possibly with Bayesian priors) after making principled, a priori decisions about model complexity.

Bahati Samuel Kandolo, Kowiyou Yessoufou, Mahlatse Kganyago

Abstract Globally, we are in the midst of a biodiversity crisis and megadiverse countries become key targets for conservation. South Africa, the only country in the world hosting three biodiversity hotspots within its borders, harbours a tremendous diversity of at‐risk species deserving to be protected. However, the lengthy risk assessment process and the lack of required data to complete assessments is a serious limitation to conservation since several species may slide into extinction while awaiting risk assessment. Here, we employed a deep neural network model integrating species climatic and geographic features to predict the conservation status of 116 unassessed plant species. Our analysis involved in total of 1072 plant species and 96,938 occurrence points. The best‐performing model exhibits high accuracy, reaching up to 83.6% at the binary classification and 56.8% at the detailed classification. Our best‐performing model at the binary classification predicts that 32% (25 species) and 8% (3 species) of Data Deficient and Not‐Evaluated species respectively, are likely threatened, amounting to a proportion of 24.1% of unassessed species facing a risk of extinction. Interestingly, all unassessed species predicted to be threatened are in protected areas, revealing the effectiveness of South Africa's network of protected areas in conservation, although these likely threatened species are more abundant outside protected areas. Considering the limitation in assessing only species with available data, there remains a possibility of a higher proportion of unassessed species being imperilled.

Yi Zou, Yimei Wang, Yanhu He et al.

Previous research has primarily focused on soil erosion issues in arid and semi-arid regions, with a limited understanding of soil erosion mechanisms in tropical areas. Additionally, there is a lack of a holistic perspective to determine the spatial attribution of soil erosion. The conversion of tropical rainforests into economically driven plantations, like rubber and pulpwood, has resulted in distinct soil erosion characteristics in specific regions. To enhance our knowledge of soil erosion patterns and mechanisms in tropical regions, it is necessary to examine soil erosion in the three major watersheds of Hainan Island from 1991 to 2021, which encompass significant geographical features such as tropical island water sources and tropical rainforest national parks. The study employed the China Soil Loss Equation (CSLE) model, slope trend analysis, Pearson correlation analysis, land-use transfer matrix, and spatial attribution analysis to examine soil erosion under different scenarios. The research results indicate that scenarios driven by the combination of natural and human factors have the greatest impact on soil erosion changes in the entire study area. Co-driven increases affected 53.56% of the area, while co-driven decreases affected 21.74%. The 31-year soil erosion showed an overall increasing trend. Human factors were identified as the primary drivers of increased soil erosion in the Nandu River basin, while a combination of climate and anthropogenic factors influenced the decrease in soil erosion. In the Changhua River basin, climate and human activities contributed to the soil erosion increase, while human activities primarily caused the decrease in soil erosion. In the Wanquan River basin, climate intensified soil erosion, whereas human activities mitigated it. This study underscores the significant combined impact of human activities and natural factors on soil erosion in tropical regions. It emphasizes the importance of considering human-induced factors when implementing soil erosion control measures in tropical regions.

Emily Dolson, Alexander Lalejini

Fitness landscapes have historically been a powerful tool for analyzing the search space explored by evolutionary algorithms. In particular, they facilitate understanding how easily reachable an optimal solution is from a given starting point. However, simple fitness landscapes are inappropriate for analyzing the search space seen by selection schemes like lexicase selection in which the outcome of selection depends heavily on the current contents of the population (i.e. selection schemes with complex ecological dynamics). Here, we propose borrowing a tool from ecology to solve this problem: community assembly graphs. We demonstrate a simple proof-of-concept for this approach on an NK Landscape where we have perfect information. We then demonstrate that this approach can be successfully applied to a complex genetic programming problem. While further research is necessary to understand how to best use this tool, we believe it will be a valuable addition to our toolkit and facilitate analyses that were previously impossible.

Mario Figueira, David Conesa, Antonio López-Quílez et al.

Continuous space species distribution models (SDMs) have a long-standing history as a valuable tool in ecological statistical analysis. Geostatistical and preferential models are both common models in ecology. Geostatistical models are employed when the process under study is independent of the sampling locations, while preferential models are employed when sampling locations are dependent on the process under study. But, what if we have both types of data collectd over the same process? Can we combine them? If so, how should we combine them? This study investigated the suitability of both geostatistical and preferential models, as well as a mixture model that accounts for the different sampling schemes. Results suggest that in general the preferential and mixture models have satisfactory and close results in most cases, while the geostatistical models presents systematically worse estimates at higher spatial complexity, smaller number of samples and lower proportion of completely random samples.

Violette Chiara, Sin‐Yeon Kim

Abstract Computer programs for video tracking of animal movement are evolving increasingly efficient, using complex algorithms or artificial intelligence systems. Despite the consequent progress in this field, researchers still face some fundamental problems in the use of such programs. For example, the best‐performing programs are often uneasy to use, and user‐friendly programs require source videos recorded under strict conditions (e.g. homogenous environments, constant lighting and high resolution), which may be difficult to meet in both laboratory and field studies. We present here AnimalTA, a new program that tracks and analyses animal movement in diverse environments. This program aims to be accessible to everyone, including those without knowledge of coding and image analysis. AnimalTA allows to process rapidly a high number of videos and manage multi‐arena tracking. It is adapted to follow the movement of targets in variable conditions, including heterogenous and complex environments, or in low‐quality videos. AnimalTA provides tools for editing videos and correcting problems caused by camera tremors, light changes or perspective deformation. AnimalTA also allows the user to easily correct tracking errors and repeat the tracking in a subsample of the video. The target's identity can be personalized to facilitate video analysis. The tracking results can be analysed in AnimalTA to obtain many different variables related to the trajectory of each target, such as average speed, total distance travelled, latency to reach defined areas, distance to a defined point, distance to other individuals, number of contacts with others, explored surface, among others. Users can set and control different parameters for these analyses and directly view the results.

Wei Li, Ruixin Du, Chuanhui Xia et al.

Gonadotropin-releasing hormone (GnRH), as a vital hypothalamic neuropeptide, was a key regulator for pituitary luteinizing hormone (LH) and follicle-stimulating hormone (FSH) in the vertebrate. However, little is known about the other pituitary actions of GnRH in teleost. In the present study, two GnRH variants (namely, GnRH2 and GnRH3) and four GnRH receptors (namely, GnRHR1, GnRHR2, GnRHR3, and GnRHR4) had been isolated from grass carp. Tissue distribution displayed that GnRHR4 was more highly detected in the pituitary than the other three GnRHRs. Interestingly, ligand–receptor selectivity showed that GnRHR4 displayed a similar and high binding affinity for grass carp GnRH2 and GnRH3. Using primary culture grass carp pituitary cells as model, we found that both GnRH2 and GnRH3 could not only significantly induce pituitary reproductive hormone gene (GtHα, LHβ, FSHβ, INHBa, secretogranin-2) mRNA expression mediated by AC/PKA, PLC/IP3/PKC, and Ca2+/CaM/CaMK-II pathways but also reduce dopamine receptor 2 (DRD2) mRNA expression via the Ca2+/CaM/CaMK-II pathway. Interestingly, GnRH2 and GnRH3 could also stimulate anorexigenic peptide (POMCb, CART2, UTS1, NMBa, and NMBb) mRNA expression via AC/PKA, PLC/IP3/PKC, and Ca2+/CaM/CaMK-II pathways in grass carp pituitary cells. In addition, food intake could significantly induce brain GnRH2 mRNA expression. These results indicated that GnRH should be the coupling factor to integrate the feeding metabolism and reproduction in teleost.

Ju Kang, Shijie Zhang, Yiyuan Niu et al.

Explaining biodiversity is a fundamental issue in ecology. A long-standing puzzle lies in the paradox of the plankton: many species of plankton feeding on a limited variety of resources coexist, apparently flouting the competitive exclusion principle (CEP), which holds that the number of predator (consumer) species cannot exceed that of the resources at a steady state. Here, we present a mechanistic model and demonstrate that intraspecific interference among the consumers enables a plethora of consumer species to coexist at constant population densities with only one or a handful of resource species. This facilitated biodiversity is resistant to stochasticity, either with the stochastic simulation algorithm or individual-based modeling. Our model naturally explains the classical experiments that invalidate the CEP, quantitatively illustrates the universal S-shaped pattern of the rank-abundance curves across a wide range of ecological communities, and can be broadly used to resolve the mystery of biodiversity in many natural ecosystems.

Michael Thorne

Ecological systems are studied using many different approaches and mathematical tools. One approach, based on the Jacobian of Lotka-Volterra type models, has been a staple of mathematical ecology for years, leading to many ideas such as on questions of system stability. Instability in such methods is determined by the presence of an eigenvalue of the community matrix lying in the right half plane. The coefficients of the characteristic polynomial derived from community matrices contain information related to the specific matrix elements that play a greater destabilising role. Yet the destabilising circuits, or cycles, constructed by multiplying these elements together, form only a subset of all the feedback loops comprising a given system. This paper looks at the destabilising feedback loops in predator-prey, mutualistic and competitive systems in terms of sets of the matrix elements to explore how sign structure affects how the elements contribute to instability. This leads to quite rich combinatorial structure among the destabilising cycle sets as set size grows within the coefficients of the characteristic polynomial.

Jim Wu, Pankaj Mehta, David Schwab

Niche and neutral theory are two prevailing, yet much debated, ideas in ecology proposed to explain the patterns of biodiversity. Whereas niche theory emphasizes selective differences between species and interspecific interactions in shaping the community, neutral theory supposes functional equivalence between species and points to stochasticity as the primary driver of ecological dynamics. In this work, we draw a bridge between these two opposing theories. Starting from a Lotka-Volterra (LV) model with demographic noise and random symmetric interactions, we analytically derive the stationary population statistics and species abundance distribution (SAD). Using these results, we demonstrate that the model can exhibit three classes of SADs commonly found in niche and neutral theories and found conditions that allow an ecosystem to transition between these various regimes. Thus, we reconcile how neutral-like statistics may arise from a diverse community with niche differentiation.

Sara Beery, Elijah Cole, Joseph Parker et al.

Conservation science depends on an accurate understanding of what's happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found. The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology. Despite its considerable importance, SDM has received relatively little attention from the computer science community. Our goal in this work is to provide computer scientists with the necessary background to read the SDM literature and develop ecologically useful ML-based SDM algorithms. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.

Marc Ohlmann, Catherine Matias, Giovanni Poggiato et al.

Separating environmental effects from those of interspecific interactions on species distributions has always been a central objective of community ecology. Despite years of effort in analysing patterns of species co-occurrences and the developments of sophisticated tools, we are still unable to address this major objective. A key reason is that the wealth of ecological knowledge is not sufficiently harnessed in current statistical models, notably the knowledge on interspecific interactions. Here, we develop ELGRIN, a statistical model that simultaneously combines knowledge on interspecific interactions (i.e., the metanetwork), environmental data and species occurrences to tease apart their relative effects on species distributions. Instead of focusing on single effects of pairwise species interactions, which have little sense in complex communities, ELGRIN contrasts the overall effect of species interactions to that of the environment. Using various simulated and empirical data, we demonstrate the suitability of ELGRIN to address the objectives for various types of interspecific interactions like mutualism, competition and trophic interactions. We then apply the model on vertebrate trophic networks in the European Alps to map the effect of biotic interactions on species distributions.Data on ecological networks are everyday increasing and we believe the time is ripe to mobilize these data to better understand biodiversity patterns. ELGRIN provides this opportunity to unravel how interspecific interactions actually influence species distributions.

Yayu Wang, Yayu Wang, Shuilin Liao et al.

Despite being the world’s third largest ocean, the Indian Ocean is one of the least studied and understood with respect to microbial diversity as well as biogeochemical and ecological functions. In this study, we investigated the microbial community and its metabolic potential for nitrogen (N) acquisition in the oligotrophic surface waters of the Indian Ocean using a metagenomic approach. Proteobacteria and Cyanobacteria dominated the microbial community with an average 37.85 and 23.56% of relative abundance, respectively, followed by Bacteroidetes (3.73%), Actinobacteria (1.69%), Firmicutes (0.76%), Verrucomicrobia (0.36%), and Planctomycetes (0.31%). Overall, only 24.3% of functional genes were common among all sampling stations indicating a high level of gene diversity. However, the presence of 82.6% common KEGG Orthology (KOs) in all samples showed high functional redundancy across the Indian Ocean. Temperature, phosphate, silicate and pH were important environmental factors regulating the microbial distribution in the Indian Ocean. The cyanobacterial genus Prochlorococcus was abundant with an average 17.4% of relative abundance in the surface waters, and while 54 Prochlorococcus genomes were detected, 53 were grouped mainly within HLII clade. In total, 179 of 234 Prochlorococcus sequences extracted from the global ocean dataset were clustered into HL clades and exhibited less divergence, but 55 sequences of LL clades presented more divergence exhibiting different branch length. The genes encoding enzymes related to ammonia metabolism, such as urease, glutamate dehydrogenase, ammonia transporter, and nitrilase presented higher abundances than the genes involved in inorganic N assimilation in both microbial community and metagenomic Prochlorococcus population. Furthermore, genes associated with dissimilatory nitrate reduction, denitrification, nitrogen fixation, nitrification and anammox were absent in metagenome Prochlorococcus population, i.e., nitrogenase and nitrate reductase. Notably, the de novo biosynthesis pathways of six different amino acids were incomplete in the metagenomic Prochlorococcus population and Prochlorococcus genomes, suggesting compensatory uptake of these amino acids from the environment. These results reveal the features of the taxonomic and functional structure of the Indian Ocean microbiome and their adaptive strategies to ambient N deficiency in the oligotrophic ocean.