An overriding question in ecology is how new ecosystems form. This question can be tested by studying colonisation of environments with little to no pre-existing life. Here, we investigated the functional basis of microbial colonisation in the forelands of a maritime Antarctic and an alpine Swiss retreating glacier, by integrating quantitative ecology, metagenomics, and biogeochemical measurements. Habitat generalists and opportunists rapidly colonise both forelands and persist across soil decadal chronosequences serving as proxies for temporal community dynamics. These microbes are metabolically flexible chemotrophic aerobes that overcome oligotrophic conditions by using organic and inorganic compounds, including atmospheric trace gases and sulphur substrates, for energy and carbon acquisition. They co-exist with metabolically flexible early-colonising opportunists and metabolically restricted later-colonising specialists, including Cyanobacteria, ammonia-oxidising archaea, and obligate predatory and symbiotic bacteria, that exhibit narrower habitat distributions. Analysis of 589 species-level metagenome-assembled genomes reveals early colonisation by generalists and opportunists is strongly associated with metabolic flexibility. Field- and laboratory-based biogeochemical measurements reveal the activity of metabolically flexible microbes rapidly commenced in the forelands. Altogether, these findings suggest primary succession in glacial foreland soils is driven by self-sufficient metabolically flexible bacteria that mediate chemosynthetic primary production and likely provide a more hospitable environment for subsequent colonisation.

Rehabilitation efforts were conducted in 2018 and 2020 to restore, maintain, and enhance forest and land functions following the 2015 fire on forest area with special purpose (KHDTK) Tumbang Nusa, Central Kalimantan. This study aims to determine the characteristics of stand structure, quantitative ecological values, aboveground biomass values, and preliminary projections of rehabilitation plant valuation in post-fire peat forest ecosystems. Study area was conducted in two rehabilitation blocks in 5 and 7 years old after rehabilitation. Vegetation analysis employed a nested sampling method, utilizing a plot size of 60 x 60 m, with Block I contain three plots (27 subplots) and Block II containing eight plots (72 subplots). Vegetation inventory and four carbon pool measurements were carried out on the understorey, seedlings, saplings, poles, and trees. The stand density value in Block I is 379 stems ha–1 with a basal area of 21.18 m2 ha–1 , while in Block II it is 503 stems ha–1 and 11.27 m2 ha–1 . The stands have good ecological value and stable vegetation, with a medium-scale species diversity level (H' = 1.53–2.80), a low-scale species dominance level (D = 0.10–0.42), commonly a high species richness value (R = 3.07–6.01), and medium to high species evenness values. The composition of rehabilitation plants are similar, but Block I has a higher proportional basal area of 3.72%. The projection of aboveground carbon biomass is 70.7 ton ha–1 and 77.7 ton ha–1 , respectively. As a preliminary study, the effectiveness of the rehabilitation valuation approach can be assessed by the productivity and quantitative ecology.

Abstract Process‐based, dynamic models are essential for extrapolating beyond current trends and anticipating biodiversity responses to global change. However, their practical adoption for forecasting purposes remains limited due to difficulties in calibrating them against data and structural inaccuracies in their mathematical formulations. While ecological time series could, in principle, be used to directly estimate model parameters and refine model structures, the large noise levels in ecological datasets and the strong nonlinearity of ecological dynamics challenge conventional calibration methods. Here, we present a robust and scalable calibration framework that addresses these challenges by integrating techniques from scientific computing and deep learning. Our approach combines a segmentation strategy where state variables are estimated independently, differentiable programming for efficient gradient computation, parameter transformations to ensure the feasibility and stability of the model simulations, and mini‐batching to accomodate large datasets. Through comprehensive benchmarks using simulated food web dynamics of increasing complexity, we demonstrate that the framework substantially improves the convergence of gradient descent algorithms and Monte Carlo sampling methods, accommodating for realistic scenarios with noisy and partial observations. This yields improved parameter estimation and forecasts within both mode estimation and full posterior distribution contexts. Crucially, we show that the calibration framework scales effectively with both the number of parameters and state variables. The improved convergence and scalability of the calibration framework enables hybrid modelling approaches, where neural networks parameterize complex processes within the dynamic model. In particular, we demonstrate that neural networks can effectively capture environmental dependencies in demographic rates and recover functional responses governing trophic interactions. Neural network‐based parameterizations have the capability to improve the structural inaccuracies of models while maintaining ecological interpretability through post‐hoc analysis of learned representations. We provide an implementation of the calibration framework and other key utilities as the open‐source Julia package HybridDynamicModels.jl , with the hope that the package will facilitate the development of hybrid modelling approaches in quantitative ecology and related fields.

Abstract Integrating complex geospatial data into research and applications for wildlife ecology remains a challenge. For example, animations of wildlife tracking data can be useful for developing hypotheses, communicating with stakeholders and infrastructure planning. Conveying an effective message often requires visualizing movements in relation to custom background layers, such as dynamic weather conditions or local transportation features. However, animations are commonly made using software that is easy to use but offers few options for input layers, thus limiting their impact. Alternatively, bespoke solutions require advanced programming skills that are not readily available for many ecologists. We developed ECODATA, a suite of open‐source tools to support exploration, analysis and visualization of animal movements and dynamic geospatial data layers. The tools do not require programming skills and guide users through the process of manipulating vector, raster and tabular data files to prepare inputs to custom animations or further analyses. The software was developed by a team of remote sensing experts, quantitative ecologists, wildlife managers and conservation practitioners. We demonstrate the use of ECODATA through two examples. The first describes the use of the software to animate movements of elk (Cervus elaphus) and wolves (Canis lupus) in relation to roads, wildlife crossing structures and seasonal vegetation green‐up near Banff National Park in Canada. The second illustrates the impact of the software on wildlife management, with an animation of caribou (Rangifer tarandus) movements and parturitions during the calving season. Both examples include processed remote sensing data and feature layers that provide relevant local context. ECODATA offers a novel resource to explore and communicate animals' interactions with their environment, informing management decisions and conservation strategies. The flexible tools for geospatial data manipulation can be used for data visualization, as described here, or to create predictor variables for inclusion in habitat selection or other ecological models.

Abstract Generalized additive models (GAMs) are a frequently used, flexible framework applied to many problems in statistical ecology. They are commonly used to incorporate smooth effects into models via splines, including spatial components in species distribution models. GAMs are often considered to be a purely frequentist framework (‘generalized linear models with wiggly bits’), however links between frequentist and Bayesian approaches to these models were highlighted early‐on in the literature. From a practical perspective, Bayesian thinking underlies many parts of the implementation in the popular R package mgcv, so understanding these underpinnings can be informative during model building and assessment. This article aims to highlight useful links (and differences) between Bayesian and frequentist approaches to smoothing, as detailed in the statistical literature, in accessible way, with a focus on the mgcv implementation. By harnessing these links we can expand the set of modelling tools we have at our disposal, as well as our understanding of how existing methods work. Two important topics for quantitative ecologists are covered in detail: model term selection and uncertainty estimation. Taking Bayesian viewpoints for these problems makes them much more tractable in many applied settings. Examples are given using data from the NOAA Alaska Fisheries Science Center's groundfish assessment program.

Abstract The field of ecology has been greatly enhanced by the integration of computational tools and statistical methods, with the programming language R emerging as a pivotal and flexible tool for ecological research. As ecological studies accelerate, understanding the prevalent trends and specific usage patterns of R in recent research is crucial. This study investigated the use of R and its packages in 125 494 scholarly articles published in 40 ecology journals from 2008 to 2023. A total of 52 658 articles (42%) designated R as their principal analytical tool, demonstrating a steady linear growth in its utilization from 10.3% in 2008 to 66.9% in 2023. Twelve R packages, including ‘lme4’, ‘vegan’, ‘nlme’, ‘MuMIn’, ‘ape’, ‘ggplot2’, ‘car’, ‘mgcv’, ‘MASS’, ‘raster’, ‘multcomp’ and ‘lmerTest’, each played a pivotal role in contributing to more than 1000 scholarly articles. The highest usage rate of the 'lme4' package indicates that mixed-effect models have a particularly important role in ecological research, and the application of these models has helped ecologists solve many important scientific problems. Journal-specific package preferences aligned with their scientific domains, while the rise in the average number of R packages per article indicates a trend towards more complex and diverse analytical methods in ecology. Our findings reveal a reciprocal relationship between the development of R and ecological research, underscoring the need for collaboration among quantitative ecologists, R developers and ecologists to further advance both the language and the field. Such collaboration will not only enhance the functionality and versatility of R but also provide robust technical support for the continued progress of ecological research.

Given the limited number of interactive educational resources for learning dynamic modelling in ecology, we seek to promote the use of Vensim software in the Quantitative Ecology course taught in the Biology degree program at FES Zaragoza. The objective was to develop an educational blog containing practical exercises solved step by step, covering from simple models to more complex ones, such as predator-prey models, management of aquatic systems and environmental processes. The exercises were addressed in a sequence that helps students understand ecological processes and strengthen their skills in mathematical modelling. The implementation of the edublog seeks, through step-by-step activities and simulations, to reinforce in students their ability to use modelling and simulation software tools, essential for their training in the area of ​​quantitative ecology. The impact of the edublog was reviewed by applying a questionnaire to measure the clarity, relevance and usefulness of the content. Generating an academic support resource accessible at any time and from any place with an Internet connection, so that the student understands and applies the concepts of mathematical modeling from the perspective of technology-mediated self-learning.

Over 20 years ago, ecologists were introduced to the site occupancy model (SOM) for estimating occupancy rates from detection‐nondetection data. In the ensuing decades, the SOM and its hierarchical modeling extensions have become mainstays of quantitative ecology, and estimating occupancy rates has become one of the most common applications of ecological field data. Here, we review 364 peer‐reviewed articles published between 2019–2021 that estimated occupancy. We first document broad patterns in study design and statistical methods to provide educators, developers of methodology and software, and ecologists with a clear picture of the landscape of methodologies used to estimate animal occupancy. Second, we conduct a focused review of a subset of 98 papers that applied the hierarchical SOM, drawing from methodological literature to identify discrepancies between SOM applications and methodological best practices. We discuss limits to statistical power, issues with model checking and model selection procedures, potential problems arising from unmodeled non‐independence, and reproducibility. We highlight areas of rapid advancement in interpreting animal occupancy related to animal movement, imperfect detection, and the occupancy–density relationship. We aim to help readers understand the landscape of methods available, motivate shifts toward robust and reproducible science, and inspire new software and methodological research.

Good modelling practice in ecology, the hierarchical Bayesian perspective

As human activity accelerates the global crisis facing wildlife populations, private land conservation provides an example of wildlife management challenges in social-ecological systems. This study reports on the research phase of ‘WildTracker’ - a co-created citizen science project, involving 160 landholders across three Tasmanian regions. This was a transdisciplinary collaboration between an environmental organisation, university researchers, and local landholders. Focusing on mammal and bird species, the project integrated diverse data types and technologies: social surveys, quantitative ecology, motion sensor cameras, acoustic recorders, and advanced machine-learning analytics. An iterative analytical methodology encompassed Pearson and point-biserial correlation for interrelationships, Non-Metric Multidimensional Scaling (NMDS) for clustering, and Random Forest machine learning for variable importance and prediction. Taken together, these analyses revealed complex relationships between wildlife populations and a suite of ecological, socio-economic, and land management variables. Both site-scale habitat characteristics and landscape-scale vegetation patterns were useful predictors of mammal and bird activity, but these relationships were different for mammals and birds. Four focal mammal species showed variation in their response to ecological and land management drivers. Unexpectedly, threatened species, such as the eastern quoll (Dasyurus viverrinus), favoured locations where habitat was substantially modified by human activities. The research provides actionable insights for landowners, and highlights the importance of ‘messy,’ ecologically heterogeneous, mixed agricultural landscapes for wildlife conservation. The identification of thresholds in habitat fragmentation reinforced the importance of collaboration across private landscapes. Participatory research models such as WildTracker can complement efforts to address the wicked problem of wildlife conservation in the Anthropocene.

BackgroundPatents are indicators of technological developments. The science & technology categories, to which they are assigned to, form a directed, weighted network where the links are the references between patents belonging to the respective categories. This network can be conceived as a kind of intellectual ecology, lending itself to mathematical analyses analogous to those carried out in numerical ecology. The non-metric Bray–Curtis dissimilarity, commonly used in quantitative ecology, can be used to describe the internal dynamics of this network.ResultsWhile the degree-distribution of the network remained stable during the studied years, that of the sub-networks of with at least k links showed that k = 5 is a critical number of citations: this many are needed that the bias towards already highly cited works come into effect (preferential attachment). Using the dij Bay-Curtis dissimilarity between nodes i and j, a surprising pattern emerged: the log-probability of a change in dij during a quarter of year depended linearly, with a negative coefficient, on the magnitude of the change itself.ConclusionsThe developed methodology could be useful to detect emerging technological developments, to aid decisions, for example, on resource allocation. The pattern found on the internal dynamics of the system depends on the categorisation of the patents, therefore it can serve as an indicator when comparing different categorisation methods.Graphical

Abstract Understanding ecological dynamics has been a central topic in ecology since its origins. Yet, identifying dynamic regimes remains a research frontier for modern ecology. The concept of ecological dynamic regime (EDR) emerged to emphasize the dynamic property of steady states in nature and refers to the fluctuations of ecosystems around some trend or average. Identifying and characterizing EDRs is of utmost importance in the current context of global change since they form the reference against which post‐disturbance dynamics must be compared to assess ecological resilience. However, the implementation of EDRs in empirical science is still challenging given the high dimensionality and stochasticity of ecological data and the large volume of data required to distinguish stochastic dynamics from general and predictable dynamics. The era of big data and the recent advances in quantitative ecology and data science offer an opportunity to study dynamic regimes using empirical approaches from a new perspective. This paper presents a novel methodological framework to describe EDRs from a set of ecological trajectories defined by the temporal changes of state variables in a multidimensional state space. In our framework, we formally define EDRs and include analytical tools to identify, characterize, and compare EDRs based on their geometric characteristics. More specifically, we propose different ways to identify EDRs from empirical data, develop a new algorithm to identify representative trajectories summarizing the main dynamic patterns, propose a set of metrics to describe the internal distribution of ecological trajectories, and define a dissimilarity index to compare two or more dynamic regimes based on their shape and position in the state space. We used artificial data to illustrate the different elements of our framework and applied our analyses to real data, using permanent sampling plots of Canadian boreal forests as an example. Overall, our framework contributes to filling the gap between theoretical and empirical ecology by providing robust analytical tools to assess ecological resilience and study ecosystem dynamics from a multidimensional perspective and considering the variability of natural systems.

The change in the development paradigm to a “green economy” requires the management of oil palm plantations (as one of the national strategic industries) to make more use of biodiversity in increasing its productivity. In addition to reducing production costs (the use of chemical fertilizers, chemical pest and weed control), optimizing the use of biodiversity in oil palm agrosystems will be able to realize the management of oil palm plantations in a sustainable and environmentally sound manner. This study aims to identify the potential for species diversity and the role of birds in oil palm plantation agrosystems. Data collection on the potential for species diversity was carried out on six types of oil palm plantation land cover at PT Selatan Agro Makmur Lestari, South Sumatra with the transect line method along 1 km with a width of 50 meters. As for the role of various types of birds in the ecosystem, a literature review is carried out. The highest number of species and individuals was found in shrubs as many as 19 species and 69 individuals. On the other hand, the lowest number of species and individuals was found in smallholder oil palm plantations-II with 6 species and 16 individuals. In terms of the number of individuals, cave swiftlet (Collocalia linchi) was the largest species with 58 individuals, while cerulean kingfisher (Alcedo coerulescens), pink-necked green pigeon (Treron vernans), and white-headed munia (Lonchura maja) were the species with the fewest individual encounters. (only 1 individual). Based on the literature review, the types of birds found at the research site have four ecological roles. There are insect pest controller, seed dispersers, predator, and weed controller. Further research is needed to examine more deeply the role (quantitative ecology) of each bird species in oil palm agrosystems.

We begin the section with Alexander Mamonov's review on the book An Introduction to the Mathematical Theory of Inverse Problems, written by Andreas Kirsch. Our reviewer describes it as a classic in the field of inverse problems and recommends it to all readers inclined to the theory and solution of inverse problems. The next review discusses the book Nonlinear Solid Mechanics for Finite Element Analysis: Dynamics, by Javier Bonet, Antonio J. Gil, and Richard D. Wood. The reviewer Josip Tambača praises in particular the free source code that comes with the book and which is an extra feature the reviewer found most beneficial. The next three books are in one way or another related to biological aspects of mathematics. Yuan Gao and Jian-Guo Liu review the book Stochastic Chemical Reaction Systems in Biology, by Hong Qian and Hao Ge. The reviewers describe the textbook as accessible for any graduate student with a background in differential equations. Russell Milne reviews the book Introduction to Quantitative Ecology, by Timothy E. Essington. He recommends the book as an excellent resource to an undergraduate-level and also graduate-level quantitative ecology course. Phylogenetic Comparative Methods in R, by Liam J. Revell and Luke J. Harmon, is reviewed by Emmanuel Paradis. He recalls the description of the field as “a specialized branch of evolutionary biology that aims to study the evolution of species traits and its mechanisms” and discusses the features of the book in great detail. He finally recommends the book to students and evolutionary biologists interested in the field. The section ends on a meta level. The book An Applied Mathematician's Apology is written by L. N. Trefethen, who is a very well known applied mathematician. Rather than mathematical concepts, this book contemplates attitudes towards mathematics---specifically applied mathematics. The reviewer Robert Corless mentions having enjoyed reading the short, though powerful, book and highly recommends it.

Abstract Ecological data are being collected at a large scale from a multitude of different sources, each with their own sampling protocols and assumptions. As a result, the integration of disparate datasets is a rapidly growing area in quantitative ecology, and is subsequently becoming a major asset in understanding the shifts and trends in species' distributions. However, the tools and software available to construct statistical models to integrate these disparate datasets into a unified framework is lacking. This has made these methods inaccessible to general practitioners and has stagnated the growth of data integration in more applied settings. We therefore present PointedSDMs : an easy to use R package used to construct integrated species distribution models. It provides functions to easily format the data, fit the models in a computationally efficient way and presents the output in a format that is convenient for additional work. This paper illustrates the different uses and functions available in the package, which are designed to simplify the modelling of integrated models. A case study using the package is also presented: combining three datasets coming from different sampling protocols, all containing records of Setophaga caerulescens across Pennsylvania state.

Introduction: Ukraine is one of the European leaders in amber deposits. The main deposits of the mineral are concentrated in the forests of the Rivne, Zhytomyr and Volyn regions. As a result of the extraction process, the integrity of forest’s ecosystems is violated, the fertile soil layer is destroyed, and 3.5 thousand hectares of forests have to be restored. Aim: Evaluation of different forest management strategies in degraded soil regeneration. Objective: The study aims to explore the response of soil mycobiota to extreme conditions associated with amber mining and to propose biotechnology to restore the fertile soil layer by methods of regenerative land use, namely the use of biomass of various ways of birch cuts, which is the primary succession to the indigenous pine stands in the region. Materials and Methods: The study was carried out on the territory of the Klesiv amber deposit in the Ukrainian Polissya. The bioindication method with the help of soil micromycetes was used to assess the quality of the plant development environment in the conditions of ecosystem restoration after amber extraction. To determine the species composition of hyphomycetes, the method of serial dilutions and direct seeding on agar media of soil suspensions was used. The analysis of soil mycobiota was performed using quantitative ecology methods. The method of correlation groups was used to determine the taxonomic diversity of hyphomycetes. To determine the effectiveness of the restoration of the studied forest soils, the express analysis of the content of essential nutrients using NPK-sensor was used. Standard methods for the determination of mobile phosphorus, potassium and nitrogen compounds were used as controls. Results: The species composition and taxonomic characteristics of soil micromycetes of forest ecosystems disturbed by amber mining have been studied. It was found that in the areas of amber mining, soil micromycetes form linear connections and three-membered structures, which is characteristic of disturbed biocenoses. In the 60-year-old plantation, soil micromycetes form strong six-membered structures that are characteristic of menopausal ecosystems or intact biocenoses. The biotechnology of restoration of the indigenous plantation characteristic of these conditions - pine with an admixture of hanging birch is offered. The result of the application of this technology will allow to reproduce natural forest ecosystems in large areas. Conclusion: For the first time, the structure of fungal complexes in the areas of amber mining has been determined, which indicates that the formation of a stable structure requires time and a systematic approach to the restoration of damaged soils. In the soils disturbed by amber mining, initial linear, three-membered and four-membered structures were formed, the structural genera of which are the “pioneer genera” Penicillium, Mucor, Rhizopus, the species of which were the first to inhabit plant remains. In the process of reforestation in areas affected by amber mining, biotechnology was used for the first time, which involved the formation of natural pine stands by cutting birch, forming the primary succession in the studied areas. The comparison of the results of chemical analysis of soils of the studied areas of the Klesiv forestry before and after the application of birch pruning technology for the formation of pine stands proves the effectiveness of the technology, as in all areas nitrogen, phosphorus and potassium were increased in the soil.

Background. Volunteers’ participation in scientific research has increased in recent decades. Citizen science (CS) data have been used in quantitative ecology to analyse species ranges by means of species distribution models. We investigated the Italian distribution of five large saproxylic beetles (big five), to describe their niche space, paramount areas for their conservation, and conservation gaps. Methods. CS data from two projects, climate and environmental variables were used to produce Habitat suitability (HS) maps for each species and averaged HS maps. The big five’s conservation status was assessed interpolating HS maps with the distribution of protected areas, concomitantly identifying conservation gaps. Results. The pre-alpine and Apennines arcs, north-eastern Sicily and eastern Sardinia, were identified as conservation’s hotspots. Ranking HS levels from minimum to optimal, the extent of conservation gaps decreases as environmental suitability for the big five increases. Conclusions. For the first time in Italy, CS data have been used to investigate niche space of the largest protected saproxylic beetles and analyse the distribution of their suitable habitat. The resulting HS raster maps and vector layers, reporting HS value in all Italian protected areas (n° 3771), were provided and discussed, reporting an application example for conservation purposes.

Joint species distribution modeling is attracting increasing attention in the literature these days, recognizing the fact that single species modeling fails to take into account expected dependence/interaction between species. This short paper offers discussion that attempts to illuminate five noteworthy technical issues associated with such modeling in the context of plant data. In this setting, the joint species distribution work in the literature considers several types of species data collection. For convenience of discussion, we focus on joint modeling of presence/absence data. For such data, the primary modeling strategy has been through introduction of latent multivariate normal random variables. These issues address the following: (i) how the observed presence/absence data is linked to the latent normal variables as well as the resulting implications with regard to modeling the data sites as independent or spatially dependent, (ii) the incompatibility of point referenced and areal referenced presence/absence data in spatial modeling of species distribution, (iii) the effect of modeling species independently/marginally rather than jointly within site, with regard to assessing species distribution, (iv) the interpretation of species dependence under the use of latent multivariate normal specification, and (v) the interpretation of clustering of species associated with specific joint species distribution modeling specifications. It is hoped that, by attempting to clarify these issues, ecological modelers and quantitative ecologists will be able to better appreciate some subtleties that are implicit in this growing collection of modeling ideas. In this regard, this paper can serve as a useful companion piece to the recent survey/comparison article by [33] in Methods in Ecology and Evolution.

Marginality indices for biodiversity conservation in forest trees

On comparing design-based estimation versus model-based prediction to assess the abundance of biological populations