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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]

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.

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]

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.

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]