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Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

Abstract Efforts to characterize microbial life across diverse environments have progressed tremendously, yet the microbiome of Earth's largest biomass reservoir—the wood of living trees—has been largely unexplored. Current understanding of the tree microbiome is largely confined to roots and leaves, with little attention given to the endophytic microbiome of wood, even though emergent studies have indicated this zone as a niche for unique taxa, of consequence for ecosystem health and global biogeochemical cycles. The lack of investigation derives partly from the physical recalcitrance of wood, which presents challenges during sampling, homogenization, and the extraction of nucleic acids. In response to these issues, we present an optimized method for processing wood for use in microbial analyses, from sampling through to downstream analyses. Using methane‐cycling taxa as model endophytes, we assess losses in recovery during our method, and determine a limit‐of‐detection of approximately 500 cells per 100 mg of (dry) wood. For all six species evaluated—which represented several diverse taxa of hardwoods and softwoods—PCR inhibition proved minimal, and we expect this method to be applicable for a majority of tree species. The methods presented herein can facilitate future investigation into the wood microbiome and global microbial ecology of methane cycling.

Abstract Identifying individuals is key to estimating population sizes by spatial capture–recapture, but identification errors are sometimes made. The most common identification error is the failure to recognise a previously detected individual, thus creating a ‘ghost’ Johansson. This results in positively biased abundance estimates. Ghosts typically manifest as single detection individuals (‘singletons’) in the capture history. To deal with ghosts, we develop a spatial capture–recapture method conditioned on at least K detections. The standard spatial capture–recapture (SCR) model is the special case of K=1. Ghosts can mostly be excluded by fitting a model with K=2 (SCR‐2). We investigated the effect of ‘singleton’ ghosts on the estimation of the model parameters by simulation. The SCR method increasingly overestimated abundance with increasing percentage of ghosts, with positive bias even when only 10% of the detected individuals were ghosts, and bias between 43% and 71% when 30% were ghosts. Estimates from the SCR‐2 method showed lower bias in the presence of ghosts, at the cost of a loss of precision. The mean squared error of the estimated abundance from the SCR‐2 method was lower in all scenarios with ghosts under high encounter rates and for scenarios with 30% or more ghosts with low encounter rates. We also applied our method to capture histories from camera trap surveys of snow leopards (Panthera uncia) at two sites from Mongolia and find that the SCR method produced higher abundance estimates at both sites. Capture histories are susceptible to errors when generated from passive detectors such as camera traps and genetic samples. The SCR‐2 method can remove bias from ghost capture histories, at the cost of some loss in precision. We recommend using the SCR‐2 method in cases when there may be more than 10% ghosts or surveys with a large number of single detection capture histories, except perhaps when the sample size is very low.

Abstract Passive acoustic monitoring (PAM) has become an important tool for surveying birds, and there is a growing demand for approaches to obtain abundance and behavioural information from PAM recordings. Changes in bird populations have been assessed by counting recorded calls and calculating the vocal activity rate (VAR, i.e. the number of calls per recording time). However, bird calls could be counted in various ways and depending on species traits, these call counts could give us different insights on bird abundance, vocal behaviour and/or habitat use. Our study had two goals: (1) to present and evaluate two new indices based on call counts, the detection rate (DR, i.e. the number of 1‐min recordings in which the presence of a target vocalization is detected) and the maximum count per minute (MAX, i.e. the maximum number of calls found in a 1‐min recording); and (2) to present a conceptual framework showing how the interpretations of VAR, DR and MAX could depend on the index and on species traits. For three Neotropical bird species with distinct traits, we calculated VAR, DR and MAX based on PAM data from 25 sites in the Yucatan Peninsula (Mexico) that varied in their degree of anthropogenic habitat disturbance. We found moderate to high correlations between the indices and higher temporal variability in VAR compared to DR and MAX. We also found different effect sizes of habitat disturbance on the three species and indices. We suggest that DR might be a more reliable index of relative abundance than VAR for species whose calling behaviour exhibits a high cue rate and that MAX may be suitable for estimating family or flock size in gregarious birds. Our findings show the potential usefulness of developing new indices based on call counts to generate ecological hypotheses and assess changes in bird abundance and behaviour.

Abstract Image‐based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum‐led initiatives provide researchers with an abundance of data and knowledge to extract. These include extraction of metadata, species identification, and phenomic data. Ecological and evolutionary biologists are increasingly using complex, multi‐step processes on data. These processes often include machine learning techniques, often built by others, that are difficult to reuse by other members in a collaboration. We present a conceptual workflow model for machine learning applications using image data to extract biological knowledge in the emerging field of imageomics. We derive an implementation of this conceptual workflow for a specific imageomics application that adheres to FAIR principles as a formal workflow definition that allows fully automated and reproducible execution, and consists of reusable workflow components. We outline technologies and best practices for creating an automated, reusable and modular workflow, and we show how they promote the reuse of machine learning models and their adaptation for new research questions. This conceptual workflow can be adapted: it can be semi‐automated, contain different components than those presented here, or have parallel components for comparative studies. We encourage researchers—both computer scientists and biologists—to build upon this conceptual workflow that combines machine learning tools on image data to answer novel scientific questions in their respective fields.

Abstract Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g. herbivory) and mutualistic (e.g. pollination) interaction types. Here, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. Contrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso‐scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over‐representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These structural properties can be used to predict the type of interaction with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g. pollination, parasitism). Our results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy.

Abstract Movement models are frequently fit to animal location data to understand how individuals respond to and interact with local environmental features. Several open‐source software packages are available for analysing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness of fit. We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness of fit of integrated step‐selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models and demonstrate the approach using both an integrated step‐selection analysis and a hidden Markov model applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate statistical significance using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g. to explore variability in model behaviour across stochastic simulations or to identify areas where the model could be improved). We provide coded examples and vignettes to demonstrate the flexibility of the approach. We encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness of fit.

Abstract Monitoring mollusk biodiversity is a great challenge due to their large diversity and broad distribution. Environmental DNA (eDNA) technology is increasingly applied for biodiversity monitoring, but relevant studies on marine mollusks are still limited. Although previous studies have developed several pairs of primers for mollusk eDNA analyses, most of them targeted only a small group of mollusks. In this study, seven primers were designed for the mollusk community and validated and compared with eight pairs of published primers to select the best candidates. After in silico test, MollCOI154 and MollCOI255 primers showed non-specific amplification, and same results were also obtained in published primers (COI204, Sepi, and veneroida). Moll12S100, Moll12S195 and Moll16S primers failed to amplify across all genomic DNA from selected mollusk. Except Moll16S, all developed and two published (unionoida and veneroida) primers were successfully amplified on four eDNA samples from Yangtze River estuary. After annotation of the amplified sequences, MollCOI253 showed higher annotation of the amplification results than the other primers. In conclusion, MollCOI253 had better performance in terms of amplification success and specificity, and can provide technical support for eDNA-based research, which will be beneficial for molluscan biodiversity investigation and conservation.

Abstract Background Anthropogenic impacts on freshwater habitats are causing a recent biodiversity decline far greater than that documented for most terrestrial ecosystems. However, knowledge and description of freshwater biodiversity is still limited, especially targeting all size classes to uncover the distribution of biodiversity between different trophic levels. We assessed the biodiversity of the Lower Rhine and associated water bodies in the river’s flood plain including the river’s main channel, oxbows and gravel-pit lakes, spanning from the level of protists up to the level of larger invertebrate predators and herbivores organized in size classes (nano-, micro, meio- and macrofauna). Morphological diversity was determined by morphotypes, while the molecular diversity (amplicon sequencing variants, ASVs) was assessed through eDNA samples with metabarcoding targeting the V9 region of the 18S rDNA. Results Considering all four investigated size classes, the percentage of shared taxa between both approaches eDNA (ASVs with 80–100% sequence similarity to reference sequences) and morphology (morphotypes), was always below 15% (5.4 ± 3.9%). Even with a more stringent filtering of ASVs (98–100% similarity), the overlap of taxa could only reach up to 43% (18.3 ± 12%). We observed low taxonomic resolution of reference sequences from freshwater organisms in public databases for all size classes, especially for nano-, micro-, and meiofauna, furthermore lacking metainformation if species occur in freshwater, marine or terrestrial ecosystems. Conclusions In our study, we provide a combination of morphotype detection and metabarcoding that particularly reveals the diversity in the smaller size classes and furthermore highlights the lack of genetic resources in reference databases for this diversity. Especially for protists (nano- and microfauna), a combination of molecular and morphological approaches is needed to gain the highest possible community resolution. The assessment of freshwater biodiversity needs to account for its sub-structuring in different ecological size classes and across compartments in order to reveal the ecological dimension of diversity and its distribution.

Abstract Home range is a fundamental characteristic of an animal natural history. The study of home range provides information on the sites where organisms forage for food, find shelter, or locate mates. Home range size and shape can change throughout the lifespan of an organism, during the year, or across seasons, driven by resource availability and the basic needs for each organism. For freshwater and semi-aquatic turtles, home range is greatly affected by water availability, humidity, and temperature throughout the year, nevertheless demographic factors such age and sex are also important determinants of home range size. In this study we estimated home range and dispersal movements for Kinosternon creaseri, Terrapene yucatana, and Rhinoclemmys areolata in a semi-tropical dry forest in central Yucatán. For a two-year period, turtles were surveyed using hoop traps and visual encounters. Twenty-one individuals (5–8 per species) were equipped with radio transmitters to track them across the landscape. Distances between relocations and home range were compared across species seasons, sex, and interactions of these variables. Monthly average movements were positively correlated with rain in the three species studied. Home range of R. areolata was larger than those of K. creaseri and T. yucatana. Home range of the three studied species were larger during the wet season. Home range overlap index within same species individuals was higher during the rainy than dry season, but overall overlap is low between and within species.

Abstract Background Selection of climate-change adapted ecotypes of commercially valuable species to date relies on DNA-assisted screening followed by growth trials. For trees, such trials can take decades, hence any approach that supports focussing on a likely set of candidates may save time and money. We use a non-stationary statistical analysis with spatially varying coefficients to identify ecotypes that indicate first regions of similarly adapted varieties of Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) in North America. For over 70,000 plot-level presence-absences, spatial differences in the survival response to climatic conditions are identified. Results The spatially-variable coefficient model fits the data substantially better than a stationary, i.e. constant-effect analysis (as measured by AIC to account for differences in model complexity). Also, clustering the model terms identifies several potential ecotypes that could not be derived from clustering climatic conditions itself. Comparing these six identified ecotypes to known genetically diverging regions shows some congruence, as well as some mismatches. However, comparing ecotypes among each other, we find clear differences in their climate niches. Conclusion While our approach is data-demanding and computationally expensive, with the increasing availability of data on species distributions this may be a useful first screening step during the search for climate-change adapted varieties. With our unsupervised learning approach being explorative, finely resolved genotypic data would be helpful to improve its quantitative validation.

Abstract Ctenoluciidae is a Neotropical freshwater fish family composed of two genera, Ctenolucius (C. beani and C. hujeta) and Boulengerella (B. cuvieri, B. lateristriga, B. lucius, B. maculata, and B. xyrekes), which present diploid number conservation of 36 chromosomes and a strong association of telomeric sequences with ribosomal DNAs. In the present study, we performed chromosomal mapping of microsatellites and transposable elements (TEs) in Boulengerella species and Ctenolucius hujeta. We aim to understand how those sequences are distributed in these organisms’ genomes and their influence on the chromosomal evolution of the group. Our results indicate that repetitive sequences may had an active role in the karyotypic diversification of this family, especially in the formation of chromosomal hotspots that are traceable in the diversification processes of Ctenoluciidae karyotypes. We demonstrate that (GATA)n sequences also accumulate in the secondary constriction formed by the 18 S rDNA site, which shows consistent size heteromorphism between males and females in all Boulengerella species, suggesting an initial process of sex chromosome differentiation.

Abstract Background Human evolution is a topic that is largely excluded from K-12 classrooms for a variety of reasons, including the inability, unwillingness, or lack of preparedness of educators to teach a topic that has been seen as controversial. This study explored how engagement in professional development infused with 3D printing and ways of knowing discussion influenced science teachers' self-efficacy for teaching human evolution. The professional development opportunity was designed to empower teachers and provide them with the tools necessary to incorporate human evolution into their curriculum. During this workshop, participants learned about paleontology and human origins, spoke with professional paleoanthropologists, discussed implementation strategies with evolution educators, and developed lesson plans centered around human evolution. To explore the role of this professional development on teachers’ self-efficacy and perceptions of the teaching of evolution, we used a previously validated survey that was employed in the pre-test and post-test format and semi-structured focus group interviews. Results The results of this study indicate that the workshop positively impacted teacher perceptions of the teaching of evolution with significant improvements on two of the three tested factors and the third factor almost reaching significance. Conclusions Our data demonstrate that a three-day workshop can successfully impact teachers' perception dof the teaching of evolution and, in turn, increase the implementation of human evolution in K-12 classrooms. By specifically structuring the workshop content in a way that addressed many of the previously indicated obstacles in teaching evolution, we were able to positively impact educators and provide them with the information and tools necessary to add human evolution into their curricula.

Abstract Background A major challenge of evolutionary biology is making underlying concepts accessible to wide audiences. One method for doing so is to utilise multi-media formats that have potential to engage and inform through entertainment. This pilot study outlines and discusses a sonification concert that integrated musical performance with a range of evolutionary concepts and ideas fundamental to an understanding of evolution, such as protein sequences. We aimed to showcase sound-art objects and live-coding performances created using sonification as a mechanism for presenting complex biological processes to both researcher and non-researchers. We sought to evaluate the effectiveness of this art-adjacent practice for public engagement with evolutionary biology research, and also to gather feedback to guide future events. Toward this end, we held a live concert showcasing biologically-based algorithmic music exploring links between evolutionary biology research, sound art, and musical performance. The event had three main acts: a generative audio-visual piece giving an artistic representation of SARS coronavirus based on a parameter-mapping sonification of protein sequence of the replicase polyprotein; a pre-recorded string ensemble demonstrating the effects of codon selection on translation speed using parameter-mapping sonification; and a live-coded music piece interactively sonifying protein structures. Results Our event attracted 90 attendees. We evaluated success using direct observation and written feedback forms with a 58% response rate: 95% of respondents stated they had enjoyed the event and 63% indicated they were inspired by it. Conclusions Presenting the sonic outputs of sonification research in a concert format showed good potential for the pursuit of public engagement with evolutionary biology research, demonstrating the ability to engage curiosity and inspire an audience while also conveying scientific content alongside the nuanced and complex world of modern evolutionary biology.

Abstract Evolution is one of the controversial topics in biology primarily because of the perceived conflict between religion and evolution. Religiosity is one of the biggest predictors of evolution acceptance, i.e., the more religious an individual, the less accepting they are of evolution. Most students in the United States are religious, so how evolution is taught in the classroom is essential for a more inclusive experience. However, educators do not have a way to measure what factors influence students' evolution acceptance. We developed a survey instrument with validity evidence called the "predictive Factors of Evolution Acceptance and Reconciliation" (pFEAR). Using this measure, with data from eight religiously affiliated institutions in the United States, educators can understand how their religious and scientific worldview factors influence their students' views on evolution acceptance. Our study showed that religious influence was the most statistically significant predictor of evolution acceptance among religious students by a factor of 2, when compared to students scientific worldviews and the perceived conflict between science and religion. It also showed that perceived conflict between science and religion and being influenced by science were significant positive predictors of evolution acceptance. Further, this study identifies ways educators can use the pFEAR in the classroom to better understand their student's views and better modify how they approach teaching evolution in their classroom.

Lophotettiginae Hancock, 1909, endemic to the Neotropics, is one of the most ill-known subfamilies of Tetrigidae. Until now, there have not been any hypothesis on its relationship with other subfamilies. Lophotettiginae comprise only two genera, Lophotettix Hancock, 1909 and Phelene Bolívar, 1906, both with an arduous taxonomic history. Here, we describe a new species, Phelene reinschmidti from Ecuador, and compare it with its congeners: P. turgida from Peru and P. maroon from French Guiana. We found the subfamily Lophotettiginae to most closely resemble the Neotropical subfamily Metrodorinae, meaning that the two are likely related. We also provide a 3D scan of the holotype, a first for Tetrigidae. The 3D model contains all the important taxonomic characters and much more data could be extracted in the future using artificial intelligence-assisted approaches.

Continuous field work on mountains in the upper Pastaza watershed, showed an unexpected cryptic diversity in the recently described Pristimantis anaiae species group. Based on molecular and morphological characters we describe a new frog species from the central region of the eastern Ecuadorian Andes. We present an updated phylogeny for the Pristimantis anaiae species group, based on widely used, ubiquitous, and available genes, three mitochondrial genes (12S rRNA, 16S rRNA, ND1) and RAG1 nuclear gene. Our phylogenetic hypothesis increases genetic diversity of the species group and shows two well-supported subclades. We show the relationships of the new species and include for the first time Pristimantis maryanneae, belonging to a clade related to P. resistencia + P. glendae. Our phylogeny shows that new species, from Cerro Candelaria, is more closely related to P. kunam from Sangay National Park and differs from it by having a less conspicuous dorsolateral row of enlarged tubercles, and sacral oval marks surrounded by thin white pale lines. The new species and two additional undescribed species occur on the south side of the Rio Pastaza Valley, at Cerro Candelaria within the Llanganates – Sangay Ecological Corridor, reflecting the high importance of this area for conservation and understanding of evolution processes in the area, suggesting a probably adaptive radiation zone for the P. anaiae species group.

Abstract Background The ecology and biology of oysters (Ostreidae) across the tropics is poorly understood. Morphological plasticity and shared characteristics among oysters have resulted in the misidentification of species, creating challenges for understanding basic species-specific biological information that is required for restoration and aquaculture. Genetic barcoding has proven essential for accurate species identification and understanding species geographic ranges. To reduce the costs of molecular species identification we developed multiplex assays using the cytochrome c oxidase subunit I (COI or cox1) barcoding gene for the rapid identification of five species of oysters within the genus Saccostrea that are commonly found in Queensland, Australia: Saccostrea glomerata, Saccostrea lineage B, Saccostrea lineage F, Saccostrea lineage G, and Saccostrea spathulata (lineage J). Results Multiplex assays were successful in species-specific amplification of targeted species. The practical application of these primers was tested on wild spat collected from a pilot restoration project in Moreton Bay, Queensland, with identified species (S. glomerata, lineage B and lineage G) validated by Sanger sequencing. DNA sampling by extraction of oyster pallial fluid was also tested on adult oysters collected from the Noosa estuary in Queensland to assess whether oysters were able to be identified non-destructively. DNA concentrations as low as 1 ng/ μL still amplified in most cases, allowing for identification, and mortality at 6 weeks post pallial fluid collection was low (3 out of 104 sampled oysters). Conclusion These multiplex assays will be essential tools for species identification in future studies, and we successfully demonstrate their practical application in both restoration and aquaculture contexts in Queensland. The multiplex assays developed in this study outline easily replicable methods for the development of additional species-specific primer sets for the rapid identification of other species of Saccostrea found across the Indo-Pacific, which will be instrumental in unravelling the taxonomic ambiguities within this genus in tropical regions.

Abstract Ecological resource availability is crucial for the persistence and survival of local desert animal communities. Dryland resources such as shrubs and burrows positively benefit animal species by mitigating harsh abiotic factors and providing habitat. Understanding the role of native shrubs, many of which serve as foundation species within desert regions, as well as the function of underground burrows as resources, provides insights into habitat utilization. In this study, we seek to better understand the co-occurrence of these two resources as a first step in quantifying key patterns locally and regionally in drylands. We tested whether the presence of burrows increased with the density of foundational shrubs near the burrows at two scales—within a 5 m radius of every burrow recorded and at the site level—defined as discrete ecological areas. We performed fieldwork across 31 sites within the arid and semiarid regions of Central California. We used a combination of burrow field surveys and satellite imagery to document both vertebrate animal burrow frequencies and shrub densities. Additionally, the accuracy of the shrub data was verified through ground truthing. Both fine-scale and site-level shrub densities positively predicted the relative likelihood of burrows and the frequency of burrows, respectively. The existence of two highly utilized dryland resources and the relationship between them signal that areas abundant in both resources will likely better support resident animal species. This finding underscores the significance of incorporating both shrub density and burrow frequency in studies of habitat interconnectivity and quality. The co-occurrence patterns of these resources will support novel habitat management and conservation strategies designed around both conservation and restoration efforts.

Abstract Background Classical matrix game models aim to find the endpoint of behavioural evolution for a set of fixed possible interaction outcomes. Here, we introduce an evolutionary model in which not only the players’ strategies but also the payoff matrix evolves according to natural selection. Results We start out from the hawk–dove matrix game and, in a way that is consistent with the monomorphic model setup of Maynard Smith and Price, introduce an evolving phenotypic trait that quantifies fighting ability and determines the probability of winning and the cost of losing escalated hawk–hawk fights. We define evolutionarily stable phenotypes as consisting of an evolutionarily stable strategy and an evolutionarily stable trait, which in turn describes a corresponding evolutionarily stable payoff matrix. Conclusions We find that the maximal possible cost of escalating fights remains constant during evolution assuming a separation in the time scales of fast behavioural and slow trait selection, despite the fact that the final evolutionarily stable phenotype maximizes the payoff of hawk–hawk fights. Our results mirror the dual nature of Darwinian evolution whereby the criteria of evolutionary success, as well as the successful phenotypes themselves, are a product of natural selection.

Abstract Flying foxes of the genus Pteropus, especially those inhabiting islands, face increasing pressure from anthropogenic threats. A first step to implementing effective conservation actions is to establish monitoring projects to understand a species’ population status and trend. Pteropus species are highly affected by seasonality which further requires regular, repeated, and long-term data to understand population trends, and reactions to severe weather events. In the present case study, a regular, bi-annual population census was implemented on Comoros between 2016 and 2023 for the highly threatened Livingstone’s fruit bat, Pteropus livingstonii, and compared the results of standardized monitoring to historical population data. Seasonality had a large impact on the number of bats found at roost sites, with more bats present in the wet season, but the data over the past eight years revealed no significant in- or decrease in the number of bats counted on the island Anjouan. We estimated around 1,200–1,500 bats on Anjouan and 300–400 bats on Mohéli, and found that landcover type has no measurable effect on population distribution at roost sites. Our study highlights the need for long-term surveys to understand past population trends and that single counts are not sufficient to draw final conclusions of a species’ status.

Abstract Background Dorcus stag beetles in broad sense are one of the most diverse group in Lucanidae and important saproxylic insects playing a crucial role in nutrient recycling and forest biomonitoring. However, the dazzling morphological differentiations have caused numerous systematic confusion within the big genus, especially the puzzlingly generic taxonomy. So far, there is lack of molecular phylogenetic study to address the chaotic situation. In this study, we undertook mitochondrial genome sequencing of 42 representative species including 18 newly-sequenced ones from Eastern Asia and reconstructed the phylogenetic framework of stag beetles in Dorcus sensu lato for the first time. Results The mitogenome datasets of Dorcus species have indicated the variable mitogenomic lengths ranged from 15,785 to 19,813 bp. Each mitogenome contained 13 PCGs, 2 rRNAs, 22 tRNAs, and a control region, and all PCGs were under strong purifying selection (Ka/Ks

Abstract The modification of fertile stamens into sterile staminodes has occurred independently many times in the flowering plant lineage. In the genus Aquilegia (columbine) and its closest relatives, the two stamen whorls closest to the carpels have been converted to staminodes. In Aquilegia, the only genetic analyses of staminode development have been reverse genetic approaches revealing that B-class floral identity genes are involved. A. jonesii, the only species of columbine where staminodes have reverted to fertile stamens, allows us to explore the genetic architecture of staminode development using a forward genetic approach. We performed QTL analysis using an outcrossed F2 population between A. jonesii and a horticultural variety that makes fully developed staminodes, A. coerulea ‘Origami’. Our results reveal a polygenic basis for staminode loss where the two staminode whorls are under some level of independent control. We also discovered that staminode loss in A. jonesii is not complete, in which staminode-like traits sometimes occur in the inner fertile stamens, potentially representing a fading boundary of gene expression. The QTLs identified in this study provide a map to guide future reverse genetic and functional studies examining the genetic basis and evolutionary significance of this trait.

Abstract The epiphytic and endophytic bacteria play an important role in the healthy growth of plants. Both plant species and growth environmental influence the bacterial population diversity, yet it is inconclusive whether it is the former or the latter that has a greater impact. To explore the communities of the epiphytic and endophytic microbes in Camellia oleifera, this study assessed three representative C. oleifera cultivars from three areas in Hunan, China by Illumina high-throughput sequencing. The results showed that the diversity and species richness of endophytic microbial community in leaves were significantly higher than those of microbial community in the epiphytic. The diversity and species richness of epiphytic and endophytic microbes are complex when the same cultivar was grown in different areas. The C. oleifera cultivars grown in Youxian had the highest diversity of epiphytic microbial community, but the lowest abundance, while the cultivars grown in Changsha had the highest diversity and species richness of endophytic microbes in leaves. It was concluded that the dominant phylum mainly included Proteobacteria, Actinobacteriota and Firmicutes through the analysis of the epiphytic and endophytic microbial communities of C. oleifera. The species and relative abundances of epiphytic and endophytic microbial community were extremely different at the genus level. The analysis of NMDS map and PERMANOVA shows that the species richness and diversity of microbial communities in epiphytes are greatly influenced by region. However, the community structure of endophytic microorganisms in leaves is influenced by region and cultivated varieties, but the influence of cultivars is more significant. Molecular ecological network analysis showed that the symbiotic interaction of epiphytic microbial community was more complex.

Abstract The geographical and ecological patterns of morphological disparity are crucial to understand how species are assembled within communities in the context of the evolutionary history, morphological evolution and ecological interactions. However, with limited exceptions, rather few studies have been conducted on the global pattern of disparity, particularly in early land plants. Here we explored the spatial accumulation of disparity in a morphologically variable and species rich liverwort genus Frullania in order to test the hypothesis of latitude disparity gradient. We compiled a morphological data set consisting of eight continuous traits for 244 currently accepted species, and scored the species distribution into 19 floristic regions worldwide. By reconstructing the morphospace of all defined regions and comparisons, we identified a general Gondwana-Laurasia pattern of disparity in Frullania. This likely results from an increase of ecological opportunities and / or relaxed constraints towards low latitudes. The lowest disparity occurred in arid tropical regions, largely due to a high extinction rate as a consequence of paleoaridification. There was weak correlation between species diversity and disparity at different spatial scales. Furthermore, long-distance dispersal may have partially shaped the present-day distribution of Frullania disparity, given its frequency and the great contribution of widely distributed species to local morphospace. This study not only highlighted the crucial roles of paleoenvironmental changes, ecological opportunities, and efficient dispersal on the global pattern of plant disparity, but also implied its dependence on the ecological and physiological function of traits.

Three genera of the mantis subfamily Fulciniinae (Mantodea: Nanomantidae: Fuliciniinae) are revised. In the genus Glabromantis two species are recognized, in Paraoxypilus five species and in Phthersigena one species is recognized. The genus Myrmecomantis is synonymized with Paraoxypilus. Paraoxypilus laticollis is a new synonym of P. kimberleyensis. P. distinctus and P. flavifemur are new synonyms of P. tasmaniensis. Phthersigena centralis, Phthersigena insularis, Phthersigena minor, Phthersigena timorensis and Paraoxypilus insularis are new synonyms of Phthersigena conspersa. Glabromantis melania is a new synonym of Glabromantis nebulosa and Glabromantis pallidifemur is a new synonym of Glabromantis unicornis.

Tree-kangaroos (genus Dendrolagus) are a morphologically distinctive genus of specialized, arboreal macropodids confined to the wet forests of New Guinea and northeast Australia. A distinct Goodfellow’s group, containing up to four species, has long been recognized. Resolving the relationships of taxa within the group has been hampered by limited samples of most taxa. Here we supplement published genetic data from high quality tissue samples with molecular data generated from museum specimens to improve taxon and geographic coverage. This includes specimens of the previously unsampled D. g. goodfellowi, the holotype and paratype of D. deltae, and additional specimens of D. matschiei, D. spadix and D. g. buergersi. DNA sequence data were generated from three mitochondrial loci. Phylogenetic analysis improved the resolution of relationships within the Goodfellow’s group, with the morphologically similar D. g. goodfellowi and D. g. buergersi being recovered as sister taxa, while D. pulcherrimus was the sister to the closely related, but morphologically and ecologically distinct, D. spadix and D. matschiei. Despite being sister to D. g. buergersi, D. g. goodfellowi was highly divergent. However, the two are morphologically very similar and we recommend retaining the taxonomic status quo (recognizing them as two subspecies of a single species) until improved sampling and a more thorough analysis is possible. The problematic D. deltae was confirmed as a junior synonym of D. matschiei.

Abstract The study of thirty-two shed crowns from the Portezuelo Formation (middle Turonian-late Coniacian) at the Sierra del Portezuelo locality, reveals six distinct tooth morphotypes identified through cladistic, discriminant, and cluster analyses. Two morphotypes were identified as belonging to Megaraptoridae, three to Abelisauridae, one to Abelisauroidea, and one to Alvarezsauridae. Additionally, two of the morphotypes exhibit a combination of dental features typically found in megaraptorid and abelisauridtheropods. These results suggest a greater diversity of theropods in the original ecosystem than previously thought, including the presence of a second morphotype of megaraptorid and alvarezsaurid previously undocumented in this formation. Furthermore, the existence of Morphotype 6 indicates the potential coexistence of medium-sized abelisauroids alongside larger abelisaurids in the same ecosystem. These findings underscore the importance of future expeditions to the Sierra del Portezuelo locality to further our understanding of these previously unknown theropod species.

Abstract Background Complex descriptions of new strains of cyanobacteria appear very frequently. The main importance of these descriptions concerns potential new substances that they could synthesise, as well as their different properties as a result of their different ecological niches. The main gene used for these descriptions is 16 S with ITS or whole genome sequencing. Neowestiellopsis persica represents a unique example of the influence of ecology on morphological changes, with almost identical 16 S identity. Although our previously described Neowestiellopsis persica strain A1387 was characterized by 16 S analysis, we used different molecular markers to provide a way to separate strains of this genus that are closely related at the genetic level. Materials and methods In order to conduct an in-depth study, several molecular markers, namely psbA, rpoC1, nifD, nifH and cpcA were sequenced and studied in Neowestiellopsis persica strain A1387. Results The results of the phylogenetic analysis, based on cpcA, showed that the studied strain A 1387 falls into a separate clade than N. persica, indicating that this signature sequence could be a useful molecular marker for phylogenetic separation of similar strains isolated in the future. Conclusions Analysis of strain A1387 based on gene differences confirmed that it is a Neowestiellopsis strain. The morphological changes observed in the previous study could be due to different ecological and cultivation conditions compared to the type species. At the same time, the sequences obtained have increased our understanding of this species and will help in the future to better identify strains belonging to the genus Neowestiellopsis.

Abstract Background Reintroduction represents an effective strategy for the conservation of endangered wildlife, yet it might inadvertently impact the native ecosystems. This investigation assesses the impact of reintroducing endangered Przewalski's horses into the desert grassland ecosystem of the Kalamaili Nature Reserve (KNR), particularly its effect on the spatial distribution of ticks. In a 25 km2 core area of Przewalski's horse distribution, we set up 441 tick sampling sites across diverse habitats, including water sources, donkey trails, and grasslands, recording horse feces and characteristics to analyze the occurrence rate of ticks. Additionally, we gathered the data of 669 fresh feces of horses. To evaluate the spatial dynamics between these feces and ticks, we used methods such as Fixed Kernel Estimation (FKE), Moran’s I spatial autocorrelation index, and Generalized Linear Models (GLM). Results The dominant species of ticks collected in the core area were adult Hyalomma asiaticum (91.36%). Their occurrence rate was higher near donkey trails (65.99%) and water sources (55.81%), particularly in areas with the fresh feces of Przewalski's horses. The ticks’ three risk areas, as defined by FKE, showed significant overlap and positive correlation with the distribution of Przewalski's horses, with respective overlap rates being 90.25% in high risk, 33.79% in medium risk, and 23.09% in low risk areas. Moran's I analysis revealed a clustering trend of the fresh feces of Przewalski's horses in these areas. The GLM confirmed a positive correlation between the distribution of H. asiaticum and the presence of horse fresh feces, alongside a negative correlation with the proximity to water sources and donkey trails. Conclusions This study reveals the strong spatial correlation between Przewalski's horses and H. asiaticum in desert grasslands, underlining the need to consider interspecific interactions in wildlife reintroductions. The findings are crucial for shaping effective strategies of wildlife conservation and maintaining ecological balance.

Abstract Background Karst caves serve as natural laboratories, providing organisms with extreme and constant conditions that promote isolation, resulting in a genetic relationship and living environment that is significantly different from those outside the cave. However, research on cave creatures, especially Opiliones, remains scarce, with most studies focused on water, soil, and cave sediments. Results The structure of symbiotic bacteria in different caves were compared, revealing significant differences. Based on the alpha and beta diversity, symbiotic bacteria abundance and diversity in the cave were similar, but the structure of symbiotic bacteria differed inside and outside the cave. Microorganisms in the cave play an important role in material cycling and energy flow, particularly in the nitrogen cycle. Although microbial diversity varies inside and outside the cave, Opiliones in Beijing caves and Hainan Island exhibited a strong similarity, indicating that the two environments share commonalities. Conclusions The karst cave environment possesses high microbial diversity and there are noticeable differences among different caves. Different habitats lead to significant differences in the symbiotic bacteria in Opiliones inside and outside the cave, and cave microorganisms have made efforts to adapt to extreme environments. The similarity in symbiotic bacteria community structure suggests a potential similarity in host environments, providing an explanation for the appearance of Sinonychia martensi in caves in the north.

Abstract Background Foraging behavior in insects is optimised for locating scattered resources in a complex environment. This behavior can be exploited for use in pest control. Inhibition of feeding can protect crops whereas stimulation can increase the uptake of insecticides. For example, the success of a bait spray, depends on either contact or ingestion, and thus on the insect finding it. Methods To develop an effective bait spray against the invasive pest, Drosophila suzukii, we investigated aspects of foraging behavior that influence the likelihood that the pest interacts with the baits, in summer and winter morphotypes. We video-recorded the flies’ approach behavior towards four stimuli in a two-choice experiment on strawberry leaflets. To determine the most effective bait positioning, we also assessed where on plants the pest naturally forages, using a potted raspberry plant under natural environmental conditions. We also studied starvation resistance at 20 °C and 12 °C for both morphs. Results We found that summer morph flies spent similar time on all baits (agar, combi-protec, yeast) whereas winter morphs spent more time on yeast than the other baits. Both morphs showed a preference to feed at the top of our plant’s canopy. Colder temperatures enhanced survival under starvation conditions in both morphs, and mortality was reduced by food treatment. Conclusions These findings on feeding behavior support informed decisions on the type and placement of a bait to increase pest control.

Abstract Spiders are a diverse order of chelicerates that diverged from other arthropods over 500 million years ago. Research on spider embryogenesis, particularly studies using the common house spider Parasteatoda tepidariorum, has made important contributions to understanding the evolution of animal development, including axis formation, segmentation, and patterning. However, we lack knowledge about the cells that build spider embryos, their gene expression profiles and fate. Single-cell transcriptomic analyses have been revolutionary in describing these complex landscapes of cellular genetics in a range of animals. Therefore, we carried out single-cell RNA sequencing of P. tepidariorum embryos at stages 7, 8 and 9, which encompass the establishment and patterning of the body plan, and initial differentiation of many tissues and organs. We identified 20 cell clusters, from 18.5 k cells, which were marked by many developmental toolkit genes, as well as a plethora of genes not previously investigated. We found differences in the cell cycle transcriptional signatures, suggestive of different proliferation dynamics, which related to distinctions between endodermal and some mesodermal clusters, compared with ectodermal clusters. We identified many Hox genes as markers of cell clusters, and Hox gene ohnologs were often present in different clusters. This provided additional evidence of sub- and/or neo-functionalisation of these important developmental genes after the whole genome duplication in an arachnopulmonate ancestor (spiders, scorpions, and related orders). We also examined the spatial expression of marker genes for each cluster to generate a comprehensive cell atlas of these embryonic stages. This revealed new insights into the cellular basis and genetic regulation of head patterning, hematopoiesis, limb development, gut development, and posterior segmentation. This atlas will serve as a platform for future analysis of spider cell specification and fate, and studying the evolution of these processes among animals at cellular resolution.

Abstract Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existentially threatened by global climate change and local anthropogenic pressures. To better understand the dynamics underlying deterioration of reefs, monitoring at high spatial and temporal resolution is key. However, conventional monitoring methods for quantifying coral cover and species abundance are limited in scale due to the extensive manual labor required. Although computer vision tools have been employed to aid in this process, in particular structure‐from‐motion (SfM) photogrammetry for 3D mapping and deep neural networks for image segmentation, analysis of the data products creates a bottleneck, effectively limiting their scalability. This paper presents a new paradigm for mapping underwater environments from ego‐motion video, unifying 3D mapping systems that use machine learning to adapt to challenging conditions under water, combined with a modern approach for semantic segmentation of images. The method is exemplified on coral reefs in the northern Gulf of Aqaba, Red Sea, demonstrating high‐precision 3D semantic mapping at unprecedented scale with significantly reduced required labor costs: given a trained model, a 100 m video transect acquired within 5 min of diving with a cheap consumer‐grade camera can be fully automatically transformed into a semantic point cloud within 5 min. We demonstrate the spatial accuracy of our method and the semantic segmentation performance (of at least 80% total accuracy), and publish a large dataset of ego‐motion videos from the northern Gulf of Aqaba, along with a dataset of video frames annotated for dense semantic segmentation of benthic classes. Our approach significantly scales up coral reef monitoring by taking a leap towards fully automatic analysis of video transects. The method advances coral reef transects by reducing the labor, equipment, logistics, and computing cost. This can help to inform conservation policies more efficiently. The underlying computational method of learning‐based Structure‐from‐Motion has broad implications for fast low‐cost mapping of underwater environments other than coral reefs.

Abstract Biodiversity monitoring is undergoing a revolution, with fauna observation data being increasingly gathered continuously over extended periods, through sensors like camera traps and acoustic recorders, or via opportunistic observations. These data are often analysed with discrete‐time ecological models, requiring the transformation of continuously collected data into arbitrarily chosen, non‐independent discrete‐time intervals. To overcome this issue, ecologists are increasingly turning to the existing continuous‐time models in the literature. Closer to the real detection process, they are lesser known than discrete‐time models, not always easily accessible and can be more complex. Focusing on occupancy models, a type of species distribution models, we asked ourselves: Should we dedicate time and effort to learning and using these continuous‐time models, or can we go on using discrete‐time models? We conducted a comparative simulation study using data generated within a continuous‐time framework. We assessed the performance of five static occupancy models with varying detection processes: discrete detection/non‐detection process, discrete count process, continuous‐time Poisson process and two types of modulated Poisson processes. Our goal was to assess their abilities to estimate occupancy probability with continuously collected data. We applied all models to empirical lynx data as an illustrative example. In scenarios with easily detectable animals, we found that all models accurately estimated occupancy. All models reached their limits with highly elusive animals. Variation in discretisation intervals had minimal impact on the discrete models' capacity to estimate occupancy accurately. Our study underscores that opting for continuous‐time models with an increased number of parameters, aiming to get closer to the sensor detection process, may not offer substantial advantages over simpler models when the sole aim is to accurately estimate occupancy. Model choice can thus be driven by practical considerations such as data availability or implementation time. However, occupancy models can encompass goals beyond estimating occupancy probability. Continuous‐time models, particularly those considering temporal variations in detection, can offer valuable insights into specific species behaviour and broader ecological inquiries. We hope that our findings offer valuable guidance for researchers and practitioners working with continuously collected data in wildlife monitoring and modelling.

Abstract Quantification of phenological patterns (e.g. migration, hibernation or reproduction) should involve statistical assessments of non‐uniform temporal patterns. Circular statistics (e.g. Rayleigh test or Hermans‐Rasson test) provide useful approaches for doing so based on the number of individuals that exhibit particular activities during a number of time intervals. This study used monthly reproductive activity as an example to illustrate problems in applying circular statistics to data when marginal totals characterize experimental designs (e.g. the number of reproductively active individuals per time interval depends on sampling effort or sampling success). We illustrate the nature of this problem by crafting four exemplar data sets and developing a bootstrapping simulation procedure to overcome complications that arise from the existence of marginal totals. In addition, we apply circular statistics and our bootstrapping simulation to empirical data on the reproductive phenology of six species of Neotropical bats from the Amazon. Because sampling effort or success can differ among time intervals, circular statistics can produce misleading results of two types: those suggesting uniform phenologies when empirical patterns are markedly modal, and those suggesting non‐uniform phenologies when empirical patterns are uniform. The bootstrapping simulation overcomes these limitations: the exemplar phenology in which the percentage of reproductively active individuals is modal is appropriately identified as non‐uniform based on the bootstrapping approach, and the exemplar phenology in which the percentage of reproductively active individuals is invariant is appropriately identified as uniform based on the bootstrapping approach. The reproductive phenology of each of the six empirical examples is non‐uniform based on the bootstrapping approach, and this is true for bats species with unimodal peaks or bimodal peaks. In addition to problems with marginal totals, a review of analyses of phenological patterns in ecology identified two other frequent issues in the application of circular statistics: sampling bias and pseudoreplication. Each of these issues and potential solutions are also discussed. By providing source code for the execution of the Rayleigh test and Hermans‐Rasson test, along with the code for the bootstrapping simulation, we offer a useful tool for assessing non‐random phenologies when marginal totals characterize experimental designs.

Abstract Ecologists and evolutionary biologists are regularly tasked with the comparison of binary data across groups. There is, however, some discussion in the biostatistics literature about the best methodology for the analysis of data comprising binary explanatory and response variables forming a 2 × 2 contingency table. We assess several methodologies for the analysis of 2 × 2 contingency tables using a simulation scheme of different sample sizes with outcomes evenly or unevenly distributed between groups. Specifically, we assess the commonly recommended logistic (generalised linear model [GLM]) regression analysis, the classical Pearson chi‐squared test and four conventional alternatives (Yates' correction, Fisher's exact, exact unconditional and mid‐p), as well as the widely discouraged linear model (LM) regression. We found that both LM and GLM analyses provided unbiased estimates of the difference in proportions between groups. LM and GLM analyses also provided accurate standard errors and confidence intervals when the experimental design was balanced. When the experimental design was unbalanced, sample size was small, and one of the two groups had a probability close to 1 or 0, LM analysis could substantially over‐ or under‐represent statistical uncertainty. For null hypothesis significance testing, the performance of the chi‐squared test and LM analysis were almost identical. Across all scenarios, both had high power to detect non‐null effects and reject false positives. By contrast, the GLM analysis was underpowered when using z‐based p‐values, in particular when one of the two groups had a probability near 1 or 0. The GLM using the LRT had better power to detect non‐null results. Our simulation results suggest that, wherever a chi‐squared test would be recommended, a linear regression is a suitable alternative for the analysis of 2 × 2 contingency table data. When researchers opt for more sophisticated procedures, we provide R functions to calculate the standard error of a difference between two probabilities from a Bernoulli GLM output using the delta method. We also explore approaches to compliment GLM analysis of 2 × 2 contingency tables with credible intervals on the probability scale. These additional operations should support researchers to make valid assessments of both statistical and practical significances.

Abstract Forest above‐ground biomass (AGB) is often estimated by converting the observed tree size using allometric scaling between the dry weight and size of an organism. However, the variations in biomass allocation and scaling between tree crowns and stems due to survival competition during a tree's lifecycle remain unclear. This knowledge gap can improve the understanding of modelling tree biomass allometry because traditional allometries ignore the dynamics of allocation. Herein, we characterised allometric scaling using the dynamic ratio (r) of the stem biomass (SB) to AGB and a dynamic exponent. The allometric models were biologically parameterised by the r values for initial, intermediate and final ages rather than only a regression result. The scaling was tested using field measurements of 421 species and 2213 different‐sized trees in pantropical regions worldwide. We found that the scaling fluctuated with tree size, and this fluctuation was driven by the trade‐off relationship of biomass allocation between the tree crown and stem depending on the dynamic crown trait. The allometric scaling between SB and AGB varied from 0.8 to 1.0 for a tree during its entire lifecycle. The fluctuations presented a general law for the allometric scaling of the pantropical tree biomass and size. Our model quantified the trade‐off and explained 94.1% of the allometric relationship between the SB and AGB (93.8% of which between D2H and AGB) for pantropical forests, which resulted in a better fit than that of the traditional model. Considering the effects of the trade‐off on modelling, the actual biomass of large trees could be substantially greater than conventional estimates. These results highlight the importance of coupling growth mechanisms in modelling allometry and provide a theoretical foundation for better describing and predicting forest carbon accumulation.

Abstract Ecology increasingly relies on a massive volume of biodiversity occurrence records to draw insights into large‐scale biogeographical, ecological and evolutionary phenomena. This often involves defining a set of criteria that guides the collection, filtering and standardising of available records. These curation processes are often neither described in detail nor well documented. This hampers the comparability and reproducibility of studies and thus undermines the robustness of any ecological result. Yet, to date, there is no guide providing a friendly way to reason and document a complete data curation process. We reviewed the available literature on data curation, including tools such as R packages and workflows. From this assessment, we created a complete guide organised into five modules that allows users to consciously select and validate occurrence records based on these curation criteria. This is presented in the user‐friendly Shiny R OCCUR application, available at https://ecoinformatic.shinyapps.io/OCCUR/. OCCUR application provides a guide for researchers to deal with the trade‐off between data certainty and coverage generated in each data curation step. An interactive graph of these changes is provided within each module. OCCUR also produces a custom‐made final report including all steps used for data filtering. This report helps to streamline the writing of the methods section of manuscripts and technical reports, thus promoting the reproducibility of data curation processes. This Shiny application provides an interactive overview of the data curation methods and their use for handling occurrence records from public repositories. It brings together the taxonomic, temporal and spatial dimensions of data and also the identification of duplicates of the records. OCCUR can be applied for multiple purposes, such as teaching R to ecologists or enhancing reproducibility of macroecology and biogeography.

Abstract A fundamental pattern in ecology is that smaller organisms are more abundant than larger organisms. This pattern is known as the individual size distribution (ISD), which is the frequency distribution of all individual body sizes in an ecosystem. The ISD is described by a power law and a major goal of size spectra analyses is to estimate the exponent of the power law, λ. However, while numerous methods have been developed to do this, they have focused almost exclusively on estimating λ from single samples. Here, we develop an extension of the truncated Pareto distribution within the probabilistic modelling language Stan. We use it to estimate multiple λs simultaneously in a hierarchical modelling approach. The most important result is the ability to examine hypotheses related to size spectra, including the assessment of fixed and random effects, within a single Bayesian generalized mixed model. While the example here uses size spectra, the technique can also be generalized to any data that follow a power law distribution.

Abstract Ecological and ecosystem monitoring is rapidly shifting towards using environmental DNA (eDNA) data, particularly in aquatic systems. This approach enables a combined coverage of biodiversity across all major organismal groups and the assessment of ecological indices. Yet, most current approaches are not exploiting the full potential of eDNA data, largely interpreting results in a localized perspective. In riverine networks, by explicitly modelling hydrological transport and associated DNA decay, hydrology‐based models enable upscaling eDNA‐based diversity information, providing spatially integrated inference. To capitalize on these unprecedented biodiversity data and translate it into space‐filling biodiversity projections, a streamlined implementation is needed. Here, we introduce the eDITH R‐package, implementing the eDITH model to project biodiversity across riverine networks with minimal prior information. eDITH couples a species distribution model relating a local taxon's eDNA shedding rate in streamwater to environmental covariates, a mass balance expressing the eDNA concentration at a river's cross‐section as a weighted sum of upstream contributions, and an observational model accounting for uncertainties in eDNA measurements. By leveraging on spatially replicated eDNA measurements and minimal hydro‐morphological data, eDITH enables disentangling the various upstream eDNA sources, and produces space‐filling maps of a taxon's spatial distribution at any chosen resolution. eDITH is applicable to both eDNA concentration and metabarcoding data, and to any taxon whose DNA can be retrieved in streamwater. The eDITH package provides user‐friendly functions for single‐run execution and fitting of eDITH to eDNA data with both Bayesian methods (via the BayesianTools package) and non‐linear optimization. An interface to the DHARMa package allows model validation via posterior predictive checks. Necessary preliminary steps such as watershed delineation and hydrological characterization are implemented via the rivnet package. We illustrate eDITH's workflow and functionalities with two case studies from published fish eDNA data. The eDITH package provides a user‐friendly implementation of eDITH, specifically intended for ecologists and conservation biologists. It can be used without previous modelling knowledge but also allows customization for experienced users. Ultimately, eDITH allows upscaling eDNA biodiversity data for any river globally, transforming how state and change in biodiversity in riverine systems can be tracked at high resolution in a highly versatile manner.

Abstract Accurate biomass estimates are key to understanding a wide variety of ecological functions. In marine systems, epibenthic biomass estimates have traditionally relied on either destructive/extractive methods that are limited to horizontal soft‐sediment environments, or simplistic geometry‐based biomass conversions that are unsuitable for more complex morphologies. Consequently, there is a requirement for non‐destructive, higher‐accuracy methods that can be used in an array of environments, targeting more morphologically diverse taxa, and at ecological relevant scales. We used a combination of 3D photogrammetry, convolutional neural network (CNN) automated taxonomic identification, and taxa‐specific biovolume:biomass calibrations to test the viability of estimating biomass of three species of morphologically complex epibenthic taxa from in situ stereo 2D source imagery. Our trained CNN produced accurate and reliable annotations of our target taxa across a wide range of conditions. When incorporated into photogrammetric 3D models of underwater surveys, we were able to automatically isolate our three target taxa from their environment, producing biovolume measurements that had respective mean similarities of 99%, 102% and 120% of those obtained from human annotators. When combined with taxa‐specific biovolume:biomass calibration values, we produced biomass estimates of 88%, 125% and 133% mean similarity to that of the ‘true’ biomass of the respective taxa. Our methodology provides a highly reliable and efficient method for estimating epibenthic biomass of morphologically complex taxa using non‐destructive 2D imagery. This approach can be applied to a variety of environments and photo/video survey approaches (e.g. SCUBA, ROV, AUV) and is especially valuable in spatially extensive surveys where manual approaches are prohibitively time‐consuming.

Abstract Generalization is difficult to quantify, and many classifications exist. A beta diversity framework can be used to establish a numeric measure of generalist tendencies that jointly describes many important features of species interactions, namely spatiotemporal heterogeneity. This framework is promising for studying generalized symbiotic relationships of any form. We formulated a novel index, turnover importance (T). T describes spatiotemporal heterogeneity in interactor assemblages, an inherent feature of generalist relationships that is not captured by available metrics. We simulated the behaviour of T relative to other available metrics, calculated T for native North American orchid‐insect relationships, and tested correlations between T and eco‐geographic variables. We performed case studies to demonstrate applications of T for conservation and eco‐evolutionary studies. T behaves predictably across simulations, and dynamically interacts with site number, gamma diversity, and species range sizes. T is moderately sensitive to sampling depth. Orchids with higher T scores occupy larger ranges and broader climatic niches. Alternative interactor‐specific measures of generalism are best employed for local‐level community networks over short timespans. While these interactor metrics can assess use versus availability in local communities, T can be used to measure spatiotemporal patterns of variation in interactor assemblages across a focal species' range. This study provides a roadmap for future work focused on better understanding the patterns and consequences of generalized relationships.

Abstract Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the sampling and statistical methods, sampling and ecological variation, and sample size. We used images of moose (Alces americanus) from camera traps to parameterize N‐mixture models and tested the effect of ecological conditions, the spatial scale of measurement, and the criteria used to define independent detections on estimates of population abundance. We compared the model estimates to those generated empirically with aerial survey data, the standard method for many species of ungulate. We explored the sensitivity of estimates to model choice based on the common statistical criterion of parsimony. The two most parsimonious N‐mixture models (i.e. AICc) were considerably biased, producing implausibly large and considerably imprecise estimates of abundance. Most of the other models produced estimates of moose abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N‐mixture models was not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggested that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N‐mixture model. We recommend using a suite of models to generate predictions of abundance instead of the single top‐ranked model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps provided a broader set of insights into the behaviour of moose and the co‐occurrence of competitors and predators.

Abstract Multibeam imaging SONARs have been used for a range of measurement applications, such as measurements of fish lengths. This study aimed to quantify the accuracy of imaging SONAR systems, that varied in frequency and beam geometry, to measure the length of synthetic targets positioned perpendicularly. Blueprint Oculus imaging SONAR systems, with four different (centre) frequencies (750 kHz, 1.2 MHz, 2.1 MHz and 3 MHz), were used to measure the length of three targets of nominal lengths: 10 cm, 20 cm and at ranges between 1 and 15.5 m. The effect of beam geometry on measurement error was then examined using regression analysis. This study found that there was an overestimation of the actual length of the target for all measurements that was inversely proportional to the horizontal beamwidth. The measurement error can be reduced by normalising for beamwidth. However, the variation of measurements (i.e. the precision) was found to also increase with range, which was attributed to the increasing beam separation. It is important that the effect of beamwidth on the accuracy of target length measurements, by imaging SONARs is acknowledged in future studies. One approach to mitigating this problem, is to limit the range at which length measurements are made to a beamwidth that produces an estimated level of error that is acceptable for that study.

Abstract Automated cameras (including camera traps) are an established observation tool, allowing, for example the identification of behaviours and monitoring without harming organisms. However, limitations including imperfect detection, insufficient data storage and power supply restrict the use of camera traps, making inexpensive and customizable solutions desirable. We describe a camera system and evaluation toolset based on Raspberry Pi computers and YOLOv5 that can overcome those shortcomings with its modular properties. We facilitate the set‐up and modification for researchers via detailed step‐by‐step guides. A customized camera system prototype was constructed to monitor fast‐moving organisms on a continuous schedule. For testing and benchmarking, we recorded mason bees (Osmia cornuta) approaching nesting aids on 20 sites. To efficiently process the extensive video material, we developed an evaluation toolset utilizing the convolutional neural network YOLOv5 to detect bees in the videos. In the field test, the camera system performed reliably for more than a week (2 h per day) under varying weather conditions. YOLOv5 detected and classified bees with only 775 original training images. Overall detection reliability varied depending on camera perspective, site and weather conditions, but a high average detection precision (78%) was achieved, which was confirmed by a human observer (80% of algorithm‐based detections confirmed). The customized camera system mitigates several disadvantages of commercial camera traps by using interchangeable components and incorporates all major requirements a researcher has for working in the field including moderate costs, easy assembly and an external energy source. We provide detailed user guides to bridge the gap between ecology, computer science and engineering.

Abstract In recent years considerable interest has been generated in a new approach known as resist–accept–direct, or RAD, for managing ecosystems in the face of climate change. Under RAD, strategic responses to climate change are described in terms of three broad categories: resisting climate transformation, accepting the transformation and continuing to manage as best one can, and directing the transformed system toward novel ecological conditions. In particular, the potential for integrating RAD and adaptive management has been broadly considered, though absent a decision‐making framework needed for implementation. We propose a hierarchical decision scheme for RAD that accounts for strategy selection among the three RAD options, as well as adaptive decision making within each option. We use stochastic models and uncertainties about ecosystem processes to account for the dynamics of climate‐transformed ecosystems, and show how these features can be used to inform RAD strategies. Operationally, the approach involves decisions at two levels: one level involves choosing a policy for each strategy, and the second level involves deciding which strategy has the greatest policy value. The structure described here extends recent work in climate change adaptation, by including Markovian decisions under climate change, strategy‐specific policies, and value functions for assessing and selecting RAD strategies. We provide a hierarchical accounting of decisions and responses, and develop rules for the timing of those decisions. Combining RAD and adaptive management can help to organize thinking about ecological conservation under climate change, and focus attention on mechanisms for making decisions. We believe the structure presented here can facilitate conservation efforts under the non‐stationary climate conditions we are sure to face for the foreseeable future.

Abstract Ecologists, foresters and conservation practitioners need ‘biodiversity scanners’ to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using remote sensing methods remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these ‘extended phenotypes’ could serve as environmental landmarks of insect presence in remotely sensed data. To test the feasibility of this approach, we studied symbiotic ants that alter the canopy shape of whistling thorn acacias (Acacia [syn. Vachellia] drepanolobium), a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol for using light detection and ranging (LiDAR) data to collect, prepare (including a customizable tree‐segmentation algorithm) and apply a convolutional neural network‐based classification for the detection of ant‐inhabited acacia tree phenotypic variations. Applying this protocol enabled us to effectively detect intra‐specific tree phenotypic variation induced by insects. Surveying ant occupancy across 16 ha and 9680 acacia trees took 1000 work hours, whereas surveyed patterns of ant distribution were replicated by our trained classifier using only an hour‐long airborne LiDAR collection time. We suggest that large‐scale surveys of insect occupancy (including insect‐vectored disease) can be automated through a combination of airborne LiDAR and machine learning.

Abstract Background Despite its implications for population dynamics and evolution, the relationship between genetic and phenotypic variation in wild populations remains unclear. Here, we estimated variation and plasticity in life-history traits and fitness of the annual plant Arabidopsis thaliana in two common garden experiments that differed in environmental conditions. We used up to 306 maternal inbred lines from six Iberian populations characterized by low and high genotypic (based on whole-genome sequences) and ecological (vegetation type) diversity. Results Low and high genotypic and ecological diversity was found in edge and core Iberian environments, respectively. Given that selection is expected to be stronger in edge environments and that ecological diversity may enhance both phenotypic variation and plasticity, we expected genotypic diversity to be positively associated with phenotypic variation and plasticity. However, maternal lines, irrespective of the genotypic and ecological diversity of their population of origin, exhibited a substantial amount of phenotypic variation and plasticity for all traits. Furthermore, all populations harbored maternal lines with canalization (robustness) or sensitivity in response to harsher environmental conditions in one of the two experiments. Conclusions Overall, we conclude that the environmental attributes of each population probably determine their genotypic diversity, but all populations maintain substantial phenotypic variation and plasticity for all traits, which represents an asset to endure in changing environments.

Abstract Background Land uses such as crop production, livestock grazing, mining, and urban development have contributed to degradation of drylands worldwide. Loss of big sagebrush (Artemisia tridentata) on disturbed drylands across the western U.S. has prompted massive efforts to re-establish this foundational species. There has been growing interest in avoiding the severe limitations experienced by plants at the seed and seedling stages by instead establishing plants from containerized greenhouse seedlings (“tubelings”). In some settings, a potential alternative approach is to transplant larger locally-collected plants (“wildlings”). We compared the establishment of mountain big sagebrush (A. tridentata ssp. vaseyana) from tubelings vs. wildlings in southeastern Idaho. A mix of native and non-native grass and forb species was drill-seeded in a pasture previously dominated by the introduced forage grass, smooth brome (Bromus inermis). We then established 80 m x 80 m treatment plots and planted sagebrush tubelings (n = 12 plots, 1200 plants) and wildlings (n = 12 plots, 1200 plants). We also established seeded plots (n = 12) and untreated control plots (n = 6) for long-term comparison. We tracked project expenses in order to calculate costs of using tubelings vs. wildlings as modified by probability of success. Results There was high (79%) tubeling and low (10%) wildling mortality within the first year. Three years post-planting, chance of survival for wildlings was significantly higher than that of tubelings (85% and 14% respectively). Despite high up-front costs of planting wildlings, high survival rates resulted in their being