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Many variables in biological research-from body size to life-history timing to environmental characteristics-are measured continuously (e.g. body mass in kilograms) but analysed as categories (e.g. large versus small), which can lower statistical power and change interpretation. We conducted a mini-review of 72 recent publications in six popular ecology, evolution and behaviour journals to quantify the prevalence of categorization. We then summarized commonly categorized metrics and simulated a dataset to demonstrate the drawbacks of categorization using common variables and realistic examples. We show that categorizing continuous variables is common (31% of publications reviewed). We also underscore that predictor variables can and should be collected and analysed continuously. Finally, we provide recommendations on how to keep variables continuous throughout the entire scientific process. Together, these pieces comprise an actionable guide to increasing statistical power and facilitating large synthesis studies by simply leaving continuous variables alone. Overcoming the pitfalls of categorizing continuous variables will allow ecologists, ethologists and evolutionary biologists to continue making trustworthy conclusions about natural processes, along with predictions about their responses to climate change and other environmental contexts.

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. [ABSTRACT FROM AUTHOR]

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists. [ABSTRACT FROM AUTHOR]

The Methods section is a key component of any ecology research publication containing detailed information on how the data were collected and analyzed. However, descriptions of which statistical methods were used and how they were applied can substantially vary and may not provide enough information for the analyses to be reproducible. Computational and statistical programming advances have allowed ecological researchers without a strong statistical or mathematical background to access and use increasingly complex statistical methods. Thus, statistical methods are written by and need to be accessible to researchers across a range of quantitative expertise. Poorly written Methods sections can incorrectly inflate the strength of or call into question the results of an analysis. Although there are resources available, we have not found one that is specific to writing statistical methods, includes all the elements we discuss, and is targeted for ecologists. Here we provide guidelines for ecological researchers when writing statistical methods and review frequent errors made in Statistical Methods sections. We highlight some common dos and don'ts when writing Statistical Methods sections and present a simple checklist to help guide authors with their writing to ensure reproducibility. We illustrate the use of this guidance with two examples. [ABSTRACT FROM AUTHOR]

Abstract The Methods section is a key component of any ecology research publication containing detailed information on how the data were collected and analyzed. However, descriptions of which statistical methods were used and how they were applied can substantially vary and may not provide enough information for the analyses to be reproducible. Computational and statistical programming advances have allowed ecological researchers without a strong statistical or mathematical background to access and use increasingly complex statistical methods. Thus, statistical methods are written by and need to be accessible to researchers across a range of quantitative expertise. Poorly written Methods sections can incorrectly inflate the strength of or call into question the results of an analysis. Although there are resources available, we have not found one that is specific to writing statistical methods, includes all the elements we discuss, and is targeted for ecologists. Here we provide guidelines for ecological researchers when writing statistical methods and review frequent errors made in Statistical Methods sections. We highlight some common dos and don'ts when writing Statistical Methods sections and present a simple checklist to help guide authors with their writing to ensure reproducibility. We illustrate the use of this guidance with two examples.

Soil microorganisms perform many important ecosystem functions including nitrogen (N) cycling which dictates plant productivity in agricultural ecosystems. Despite the importance of these communities, connecting microbial composition with ecosystem function has been a long standing challenge. Taxon-specific substrate assimilation traits, measured with quantitative stable isotope probing (qSIP), may provide a means to scale from microbial community composition to community-level process rates. To test the potential for scaling up taxon-specific N assimilation to predict community-level rates of carbon mineralization, N mineralization, and N immobilization we measured soil properties, microbial activity, and N assimilation using 15N qSIP in soils from six distinct farm systems. N assimilation, measured as DNA 15N enrichment, varied among taxa and within taxa across farms. Taxon specific N assimilation was aggregated to calculate a community-weighted mean, which when combined with measures of microbial biomass was used to estimate new microbial biomass N production. This estimate of new microbial biomass production reflects the growth of active microbes over the incubation period and related to microbial activity. The new microbial biomass N produced was predictive of soil C and N mineralization rates, explaining 37–47% of the observed variation across the six farming systems. This approach highlights the ability of trait-based methods to relate microbial community structure data to microbially mediated functional process rates. Such advances may enhance our ability to understand and manage microbially mediated processes, such as N cycling, in both natural and agricultural ecosystems. [Display omitted] • Soil C and N mineralization rates varied across six agricultural systems in Appalachia. • Abiotic soil properties were poor predictors of microbial C and N cycling. • Taxon-specific 15N assimilation was aggregated and scaled with microbial biomass. • Scaled up traits were predictive of soil C and N mineralization across systems. [ABSTRACT FROM AUTHOR]

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community‐driven conservation solutions.Here, we present NABat ML, an automated machine‐learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet‐based computing resources ('cloud environment'), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future 'unseen' data. We evaluated model performance using a comprehensive, independent, holdout dataset.NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted‐average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud‐environment computing instance, the entire model training process took <16 h.Synthesis and applications. Our convolutional neural network (CNN)‐based model, NABat ML, classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species‐level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open‐source and reproducible, enabling future implementations as software on end‐user devices and cloud‐based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big‐data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad‐scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species. [ABSTRACT FROM AUTHOR]

Aim: Revealing how diversity varies across the depth gradient is key for understanding the role of mesophotic coral ecosystems in the functioning of coral reefs. We quantitatively examined how alpha and beta generic diversity of scleractinian coral assemblages vary across a wide depth gradient for coral reefs. Location: Sixteen sites in eight islands of three archipelagos in French Polynesia. Methods: We studied generic diversity patterns of scleractinian corals, as derived from the analysis of photo‐quadrats, across the seafloor from shallow to lower mesophotic depths (6–120 m) and on a wide geographic scale. Our sampling considered quantitative coral cover to explore the patterns of alpha and beta components of diversity across depth and horizontal space. Results: We show that in French Polynesia, mesophotic coral ecosystems host higher alpha and beta generic diversity than shallow reefs despite decreasing coral cover with depth. The variation of coral genus richness across the depth gradient is mainly driven by a mid‐domain effect with a peak at 40 m depth. At the same time, we found that the differences in coral genera across islands (spatial beta‐diversity) increased steadily along the depth gradient. Main conclusions: Our findings report the first quantitative results of coral cover and diversity from mesophotic coral ecosystems in French Polynesia and also present one of the few existing studies to examine the broad breadth of the mesophotic depth gradient. We demonstrate that mesophotic depths can host unexpectedly high generic richness of scleractinian coral assemblages. At the same time, we showed that increasing depth increases the differences in generic diversity composition across islands, whereas shallow reefs are similar in between. While a single island could conserve shallow regional biodiversity, mesophotic depths containing the richest diversity require site‐specific measures, suggesting that considering these mesophotic depths in conservation is necessary to maintain regional diversity. [ABSTRACT FROM AUTHOR]

The Spotted Lantern Fly (SLF), Lycorma delicatula (Hemiptera: Fulgoridae), is a sap feeding pest native to southeast Asia that has become a global biosecurity threat following invasions into South Korea, Japan, and the United States in the last two decades. Environmental niche modelling has demonstrated considerable potential for further range expansions, including into Australia and Europe. Further analysis on the potential seasonal life-history and survival of this pest in its invasive range can inform monitoring programs and is a next step in biosecurity preparedness. Here, we incorporated eco-physiological information on the development and survival of SLF life stages across temperature regimes. Using gridded climatic data, we then mapped the developmental sequence of SLF across ranges already occupied in China, South Korea, and the United States, as well as uninvaded ranges in Australia and Europe. The model was able to capture global observations of the seasonal appearance of SLF and highlight regional and seasonal vulnerabilities in regions at risk of invasion. Policy makers can use these results to make science-based biosecurity decisions and target preparedness activities through improved life-history predictions in its invasive range. [ABSTRACT FROM AUTHOR]

Abstract Quantitative skills are becoming central to the undergraduate and graduate curriculum in ecology and evolutionary biology. While previous studies acknowledge that students perceive their quantitative training to be inadequate, there is little guidance on best practices. Moreover, with the recent COVID‐19 sudden transition to online learning, there is even less guidance on how to effectively teach quantitative ecology online. Here, I synthesize a prescription of pedagogical best practices for teaching quantitative ecology online based on a broad review of the literature on multiple quantitative disciplines. These best practices include the following: (1) design and implement the class to meet learning goals using online strategies specifically; (2) create an open, inclusive, and welcoming online environment that promotes a sense of learning community; (3) acknowledge the diversity of talents and learning strategies; (4) use real‐world examples and assessments; (5) account for gaps in knowledge; (6) emphasize the modeling cycle process; (7) focus on developing ideas rather than tools or procedures; (8) if needed, introduce computational tools thoroughly before combining them with mathematical or statistical concepts; (9) evaluate the course constantly; and (10) put your heart and soul into the class. I hope these practices help fellow instructors of quantitative ecology facing similar challenges in providing our students with the knowledge and skills needed to meet the challenges of the future.

Many variables in biological research-from body size to life-history timing to environmental characteristics-are measured continuously (e.g. body mass in kilograms) but analysed as categories (e.g. large versus small), which can lower statistical power and change interpretation. We conducted a mini-review of 72 recent publications in six popular ecology, evolution and behaviour journals to quantify the prevalence of categorization. We then summarized commonly categorized metrics and simulated a dataset to demonstrate the drawbacks of categorization using common variables and realistic examples. We show that categorizing continuous variables is common (31% of publications reviewed). We also underscore that predictor variables can and should be collected and analysed continuously. Finally, we provide recommendations on how to keep variables continuous throughout the entire scientific process. Together, these pieces comprise an actionable guide to increasing statistical power and facilitating large synthesis studies by simply leaving continuous variables alone. Overcoming the pitfalls of categorizing continuous variables will allow ecologists, ethologists and evolutionary biologists to continue making trustworthy conclusions about natural processes, along with predictions about their responses to climate change and other environmental contexts.

The role of theory within ecology has changed dramatically in recent decades. Once primarily a source of qualitative conceptual framing, ecological theories and models are now often used to develop quantitative explanations of empirical patterns and to project future dynamics of specific ecological systems. In this essay, I recount my own experience of this transformation, in which accelerating computing power and the widespread incorporation of stochastic processes into ecological theory combined to create some novel integration of mathematical and statistical models. This stronger integration drives theory towards incorporating more biological realism, and I explore ways in which we can grapple with that realism to generate new general theoretical insights. This enhanced realism, in turn, may lead to frameworks for projecting ecological responses to anthropogenic change, which is, arguably, the central challenge for 21st-century ecology. In an era of big data and synthesis, ecologists are increasingly seeking to infer causality from observational data; but conventional biometry provides few tools for this project. This is a realm where theorists can and should play an important role, and I close by pointing towards some analytical and philosophical approaches developed in our sister discipline of economics that address this very problem. While I make no grand prognostications about the likely discoveries of ecological theory over the coming century, you will find in this essay a scattering of more or less far-fetched ideas that I, at least, think are interesting and (possibly) fruitful directions for our field.

Marine imaging studies have unique constraints on the data collected requiring a tool for defining the biological scope to facilitate data discovery, quality evaluation, sharing and reuse. Defining the 'target population' is way of scoping biological sampling or observations by setting the pool of organisms to be observed or sampled. It is used in survey design and planning, to determine statistical inference, and is critical for data interpretation and reuse (both images and derived data). We designed a set of attributes for defining and recording the target population in biological studies using marine photography, incorporating ecological and environmental delineation and marine imaging method constraints. We describe how this definition may be altered and recorded at different phases of a project. The set of attributes records the definition of the target population in a structured metadata format to enhance data FAIRness. It is designed as an extension to the image FAIR Digital Objects metadata standard, and we map terms to other biological data standards where possible. This set of attributes serves a need to update ecological metadata to align with new remotely-sensed data, and can be applied to other remotely-sensed ecological image data. • Marine imaging studies need a tool to define the target population, the pool of organisms observed. • Definition of the target population includes ecological and imaging method constraints. • It is a structured metadata format extension to the image FAIR Digital Objects metadata standard. • We also map terms to other biological data standards where possible. • This definition may be altered and recorded at different phases of a project. [ABSTRACT FROM AUTHOR]

Understanding, predicting and controlling animal movement is a fundamental problem of conservation and management ecology. The need to mitigate human-wildlife conflicts, such as crop raiding by large herbivores, is becoming increasingly urgent. Because of the substantial costs or the possibility of unsuitable outcomes on wildlife, managers are often encouraged to deploy interventions that can achieve their objective while minimizing the impact on animal populations. We propose an adaptive management framework that can identify cost-effective solutions to reduce human-wildlife conflicts, while also minimizing constraints on animal movement and distribution. We focus on conflicts involving animals for which conflict zones occupy only a portion of their home-range. The adaptive management approach includes four basic steps: define and spatialize conflict areas, predict animal distribution from functional connectivity and patch residency time, predict the impact of management actions on animal distribution, and test predictions and revise predictive models. Key to the process is development of a mathematical model that can predict how habitat-animal interactions shape animal movement dynamics within patch networks. In our model, networks consist of a set of high-quality patches connected by links (i.e., potential inter-patch movements). Inter-patch movement rules and determinants of patch residency time need to be determined empirically. These data then provide information to parameterize a reaction-advection-diffusion model that can predict animal distribution dynamics given habitat features and movement taxis toward (or against) conflict areas depending on management actions. Illustrative simulations demonstrate how quantitative predictions can be used to make spatial adjustments in management interventions (e.g., length of diversionary fences) with respect of conflict areas. Simulations also show that the impact of multiple interventions cannot be considered as simply having additive effect, and their relative impact on animal equilibrium distribution depends on how they are added and deployed across the network. Following the principles of adaptive, integrated landscape management, the predictive model should be revised as monitoring provides new information about the response of animals to the set of interventions. We contend that the proposed quantitative approach provides a robust framework to find cost-effective strategy toward sustainable human-wildlife conflicts.

Quantitative skills are becoming central to the undergraduate and graduate curriculum in ecology and evolutionary biology. While previous studies acknowledge that students perceive their quantitative training to be inadequate, there is little guidance on best practices. Moreover, with the recent COVID‐19 sudden transition to online learning, there is even less guidance on how to effectively teach quantitative ecology online. Here, I synthesize a prescription of pedagogical best practices for teaching quantitative ecology online based on a broad review of the literature on multiple quantitative disciplines. These best practices include the following: (1) design and implement the class to meet learning goals using online strategies specifically; (2) create an open, inclusive, and welcoming online environment that promotes a sense of learning community; (3) acknowledge the diversity of talents and learning strategies; (4) use real‐world examples and assessments; (5) account for gaps in knowledge; (6) emphasize the modeling cycle process; (7) focus on developing ideas rather than tools or procedures; (8) if needed, introduce computational tools thoroughly before combining them with mathematical or statistical concepts; (9) evaluate the course constantly; and (10) put your heart and soul into the class. I hope these practices help fellow instructors of quantitative ecology facing similar challenges in providing our students with the knowledge and skills needed to meet the challenges of the future. [ABSTRACT FROM AUTHOR]

Saltcedar woodlands are plant formations dominated by several species of the genus Tamarix (Tamaricaceae), representing the potential vegetation in saline, subsaline and wet environments under dry and semi-arid conditions. The broad ecological range of Tamarix communities leads to a heterogeneous floristic composition, with significant differences among habitats in terms of soil and vegetation. Some classification systems for Tamarix communities are only based on vegetation features and do not take any quantitative soil characteristics into account. Twelve Tamarix populations were selected under different ecological environments throughout central and southeastern Iberian Peninsula (Spain). Soil samples and vegetation inventories were collected over the course of 1 year to establish the plant–soil relationships based on constrained ordination analyses. The results showed that three different edaphic gradients were relevant to define the floristic composition of the Spanish saltcedar woodlands: a sodium–moisture gradient, a sulphate–magnesium gradient and a texture gradient. On the basis of these findings, we suggest a new classification system for Tamarix woodlands for the Mediterranean area based on plant–soil relationships. Three vegetation types have been proposed: hyperhalophilous, mesohalophilous and freshwater plant communities. Hyperhalophilous plant communities were characterised by soils with high E.C., high Na+ concentration, low soil moisture and high percentage of clay, being usually dominated by T. boveana and halophytes. Mesohalophilous plant communities had soils with high E.C., high Mg2+ and SO42− concentrations and high percentage of sand, being dominated by T. gallica with mesohalophilous and nitrophilous species. Finally, freshwater plant communities typically showed low E.C., low Na+ concentration and high soil moisture, being characterised by T. gallica with riparian and nitrophilous plants. Since the studied saltcedar woodland communities are notably dependent on soil salinity and moisture, the control of the human activities and hydrological alteration should be considered as a priority to contribute to the global preservation of the Tamarix woodlands. [ABSTRACT FROM AUTHOR]

The study of mammals has promoted the development and testing of many ideas in contemporary ecology. Here we address recent developments in foraging and habitat selection, source–sink dynamics, competition (both within and between species), population cycles, predation (including apparent competition), mutualism, and biological invasions. Because mammals are appealing to the public, ecological insight gleaned from the study of mammals has disproportionate potential in educating the public about ecological principles and their application to wise management. Mammals have been central to many computational and statistical developments in recent years, including refinements to traditional approaches and metrics (e.g. capture-recapture) as well as advancements of novel and developing fields (e.g. spatial capture-recapture, occupancy modeling, integrated population models). The study of mammals also poses challenges in terms of fully characterizing dynamics in natural conditions. Ongoing climate change threatens to affect global ecosystems, and mammals provide visible and charismatic subjects for research on local and regional effects of such change as well as predictive modeling of the long-term effects on ecosystem function and stability. Although much remains to be done, the population ecology of mammals continues to be a vibrant and rapidly developing field. We anticipate that the next quarter century will prove as exciting and productive for the study of mammals as has the recent one. [ABSTRACT FROM AUTHOR]

Panarchy is a heuristic of complex system change rooted in resilience science. The concept has been rapidly assimilated across scientific disciplines due to its potential to envision and address sustainability challenges, such as climate change and regime shifts, that pose significant challenges for humans in the Anthropocene. However, panarchy has been studied almost exclusively via qualitative research. Quantitative approaches are scarce and preliminary but have revealed novel insights that allow for a more nuanced understanding of the heuristic and resilience science more generally. In this roadmap we discuss the potential for future quantitative approaches to panarchy. Transdisciplinary development of quantitative approaches, combined with advances in data accrual, curation and machine learning, may build on current tools. Combined with qualitative research and traditional approaches used in ecology, quantification of panarchy may allow for broad inference of change in complex systems of people and nature and provide critical information for management of social-ecological systems.

Highlights • The LGLVE can accurately predict the species yields for most of the experiments. • The LGLVE outperform predictions from randomly sampling methods. • Predictions are compared against experiments covering a wide variety of taxa. • Predictions using only a fraction of the set of model parameters are quite accurate. • This is the case of the novel focal species approximation is introduced. Abstract The linear generalized Lotka–Volterra equations (LGLVE) constitute the simplest theoretical framework for ecological communities involving different kinds of interspecific interactions ― e.g. competition, facilitation. These equations have been often criticized as being too simple to model real systems. This study has two main goals: First, to test the LGLVE as a quantitative tool for describing/explaining/predicting the equilibrium species abundances. That is, how accurately the LGLVE predict the yields of S interacting species? Second, to show analyze which quantitative predictions are possible with an incomplete knowledge of the LGLVE parameters. With this aim I compiled from the literature 33 experiments, most of them for plants, which measured species yields in monoculture, biculture and in mixtures of S >2 species. I found that, by obtaining the LGLVE parameters from the yields in monoculture and biculture experiments, the LGLVE can accurately predict the majority of the equilibrium species yields in the mixtures of S >2 species for most of the experiments. However, in many natural communities, e.g. tropical forests or plankton, S can be of the order of hundreds and estimating all the model parameters from empirical data is unfeasible. But, by estimating the mean interaction coefficient from the yields of an incomplete set of monoculture and biculture experiments, the LGLVE still can make accurate predictions. Firstly, it is possible to derive simple formulas which are able to predict the relative yield total and mean relative yield. Secondly, when we are interested in the fate or performance of a particular species, we can use a more refined approximation to predict the yield of this focal species with accuracy comparable to the one obtained when using the full set of LGLVE. [ABSTRACT FROM AUTHOR]

Aims: Establishing a vegetation-soil model in Mediterranean saltmarshes based on the relationships between the plant communities and the abiotic factors, considering temporary variation.Methods: Relationships between perennial plant species abundances and plant communities were analysed by DCAs. A CCA was performed to study the relationships between floristic composition and edaphic variables. Sixteen soil variables and Pearson correlations between them were considered. Marginal and conditional effects were supported by mixed ANOVA. Statistical analyses were performed to check temporary variation.Results: DCAs results showed eight vegetation types. CCA showed E.C. as the main gradient, with the succulent halophyte communities growing in high E.C. soils. SAR and percentage of sand were considered as secondary gradients. Finally, the highest values of the edaphic variables were observed, in general, during the cold period.Conclusions: The main gradient of salinity, together with sodicity and texture gradients, would markedly influence the plant distribution in Mediterranean saltmarshes. Two principal plant zones were observed: succulent zone vs. non-succulent zone, with a specific edaphic distribution for each plant community and for the proposed Limonium morphotypes treatment. A plant-soil model based on these three gradients is here proposed. Our results would complement the previous knowledge about plant-soil relationships in Mediterranean saltmarshes. [ABSTRACT FROM AUTHOR]

This article is a review of the selection of mathematical models for population dynamics and the computer systems used for their simulation. Some examples of models that simulate pest dynamics and the advantages and disadvantages of statistical tools used for modeling are shown. It was evidenced that, despite the advances in Information and Communication Technology in Cuba and the rest of the world, there are no computer systems of easy access and use for the simulation of the processes in the mathematical modeling for pest control. Therefore, it is highlighted the importance of implementing an effective and friendly computer tool that allows the simulation of mathematical models to interpret the relationships established between the pests and their natural enemies in the ecosystems and the making of decisions for their management. The review includes the steps to follow in the simulation of population dynamics and shows some of the advances in this matter achieved in Cuba. [ABSTRACT FROM AUTHOR]

With growing anthropogenic pressure on deep-sea ecosystems, large quantities of data are needed to understand their ecology, monitor changes over time and inform conservation managers. Current methods of image analysis are too slow to meet these requirements. Recently, computer vision has become more accessible to biologists, and could help address this challenge. In this study we demonstrate a method by which non-specialists can train a YOLOV4 Convolutional Neural Network (CNN) able to count and measure a single class of objects. We apply CV to the extraction of quantitative data on the density and population size structure of the xenophyophore Syringammina fragilissima , from more than 58,000 images taken by an AUV 1200 m deep in the North-East Atlantic. The workflow developed used open-source tools, cloud-base hardware, and only required a level of experience with CV commonly found among ecologists. The CNN performed well, achieving a recall of 0.84 and precision of 0.91. Individual counts per image and size measurements resulting from model predictions were highly correlated (0.96 and 0.92, respectively) with manually collected data. The analysis could be completed in less than 10 days thus bringing novel insights into the population size structure and fine scale distribution of this Vulnerable Marine Ecosystem. It showed S. fragilissima distribution is patchy. The average density is 2.5 ind.m−2 but can vary from up to 45 ind.m−2 only a few tens of meter away from areas where it is almost absent. The average size is 5.5 cm and the largest individuals (>15 cm) tend to be in areas of low density. This study demonstrates how researchers could take advantage of CV to quickly and efficiently generate large quantitative datasets data on benthic ecosystems extent and distribution. This, coupled with the large sampling capacity of AUVs could bypass the bottleneck of image analysis and greatly facilitate future deep-ocean exploration and monitoring. It also illustrates the future potential of these new technologies to meet the goals set by the UN Ocean Decade. • YOLOV4 CNN used to measure S.fragilissima density and size in AUV images. • 58,000 images analysed in <10 days including training data generation. • Correlation between automated and manual density and size measures >0.92. • Distribution is patchy, max density 45ind.m−2 (avg. 2.5), max size 15 cm (avg. 5.5). • Our workflow can dramatically speed-up deep-sea benthic ecology studies. [ABSTRACT FROM AUTHOR]

Abstract: Sound taxonomy is a major requirement for quantitative environmental reconstruction using biological data. Transfer function performance should theoretically be expected to decrease with reduced taxonomic resolution. However for many groups of organisms taxonomy is imperfect and species level identification not always possible. We conducted numerical experiments on five testate amoeba water table (DWT) transfer function data sets. We sequentially reduced the number of taxonomic groups by successively merging morphologically similar species and removing inconspicuous species. We then assessed how these changes affected model performance and palaeoenvironmental reconstruction using two fossil data sets. Model performance decreased with decreasing taxonomic resolution, but this had only limited effects on patterns of inferred DWT, at least to detect major dry/wet shifts. Higher-resolution taxonomy may however still be useful to detect more subtle changes, or for reconstructed shifts to be significant. [Copyright &y& Elsevier]

Abstract: With the prospect of altered levels of fire in mediterranean-type environments due to changing climatic conditions, managers are faced with the need to regulate fire with an incomplete knowledge of the ecological outcomes of their actions. Burning intervals for natural vegetation are often determined by using methods which provide an indication of ‘appropriate’ intervals based on the tolerances of key fire response species. The research presented here is an investigation into variation in species abundance in response to fire frequency, interval and time since fire. An ordination of species composition constrained by the corresponding fire history matrix revealed limited response at a community level. However, when species were considered individually using quantitative models, over half of the species responded significantly to at least one of the assessed components of fire regime. Species richness exhibited little variation; however there was change in species diversity (Shannon H) in response to time since fire. Quantitative models showed a range of species responses to fire, indicating that any given regime will favour some species at the cost of others. This study demonstrated that while current techniques provide guidance on tolerable fire intervals, such an approach is not necessarily ideal for all species. The determination of ecologically meaningful fire regimes may be better served when the changing abundances of species of interest are also taken into account. [Copyright &y& Elsevier]

Aquatic biota in Central Asia witnesses and faces a changing environment. Because ostracodes contribute to both extant and fossil lacustrine diversity, they can be used to track evolution in water quality. Living ostracode communities in a variety of aquatic habitats of western Mongolia were analyzed in relation to environmental and hydrochemical variables of those habitats, based on presence/absence data from net samples. The sampled water bodies represent broad gradients in ionic concentration and composition. Ostracode community composition of springs differed from all other sampled habitats, which was also reflected in CCA analyses. Our data indicated that the ostracode fauna of western Mongolia shows a high response to calcium content, alkalinity, salinity, temperature, nutrients, and altitude. Species composition and diversity seem to be determined by solute evolution dominated by Ca-depletion. In general, this seems to be an important regulator of lacustrine ostracode species composition in several semi-arid regions. Binary logistic regression was used to predict the occurrence of the most common species with a minimal set of environmental variables. The resulting models showed on average low performance, and mainly demonstrated the potential of such modeling to predict the distribution of typical bio-indicator species. [ABSTRACT FROM AUTHOR]

Acid mine drainage (AMD) from mining activities can cause major environmental problems. The acidity is due in part to the microbial oxidation of sulfide minerals in the exposed ores. As little is known about seasonal variations of microbial population sizes in AMD, the objective of this investigation was to quantify the principal bacterial populations (iron-oxidizing acidophiles, sulfur-oxidizing acidophiles, sulfur-oxidizing neutrophiles, and acidophilic heterotrophs), with respect to season. The AMD sites sampled were four streams emerging from a copper/nickel tailings area. All bacterial populations with the exception of acidophilic sulfur oxidizers were recovered throughout the year. The most numerous bacteria were acidophilic heterotrophs and iron-oxidizing acidophiles. Surprisingly, there were no obvious trends in the relative abundance of the various bacterial groups as a function of season. Recovery of the various bacterial groups at an incubation temperature of 4 °C indicated that psychrotrophic members do exist. During the course of the investigation, it was observed that the number of Acidithiobacillus ferrooxidans (Thiobacillus ferrooxidans) colonies recovered on iron salts purified agarose, a preferred medium, was highly variable. The variability was attributed to the presence of non-iron-oxidizing satellite bacteria, the growth of which could be suppressed by increasing the concentration of the electron donor ferrous sulfate. [ABSTRACT FROM AUTHOR]