Using Delaunay triangulation to sample whole‐specimen color from digital images

1. Color variation is one of the most obvious examples of variation in nature, but biologically meaningful quantification and interpretation of variation in color and complex patterns are challenging. Many current methods for assessing variation in color patterns classify color patterns using categorical measures and provide aggregate measures that ignore spatial pattern, or both, losing potentially important aspects of color pattern.
2. Here, we present Colormesh, a novel method for analyzing complex color patterns that offers unique capabilities. Our approach is based on unsupervised color quantification combined with geometric morphometrics to identify regions of putative spatial homology across samples, from histology sections to whole organisms. Colormesh quantifies color at individual sampling points across the whole sample.
3. We demonstrate the utility of Colormesh using digital images of Trinidadian guppies (Poecilia reticulata), for which the evolution of color has been frequently studied. Guppies have repeatedly evolved in response to ecological differences between up‐ and downstream locations in Trinidadian rivers, resulting in extensive parallel evolution of many phenotypes. Previous studies have, for example, compared the area and quantity of discrete color (e.g., area of orange, number of black spots) between these up‐ and downstream locations neglecting spatial placement of these areas. Using the Colormesh pipeline, we show that patterns of whole‐animal color variation do not match expectations suggested by previous work.
4. Colormesh can be deployed to address a much wider range of questions about color pattern variation than previous approaches. Colormesh is thus especially suited for analyses that seek to identify the biologically important aspects of color pattern when there are multiple competing hypotheses or even no a priori hypotheses at all.

     

Core and occasional species: A new way forward

1. Various methods have been used to divide communities into core species and occasional or satellite species. Some methods are somewhat arbitrary, and there is evidence that many communities are more multimodal than bimodal. They also tend to rely on having multiple years of data.
2. A completely novel method is presented that not only has no requirement for long‐term datasets but can divide communities into multiple groups. It is based on probability a species is present, calculated using Simpson''s index and the sequential removal of species from the data.
3. The sequential Simpson''s index method was applied to species data from a grassland insect community. It was also applied to eleven other datasets that had been divided into core and occasional species in previously published studies.
4. The new method was found not only to be consistent with previous core–occasional assessments but also able to identify multimodality in species abundance distributions.
5. Although ideally used with a measure of persistence (frequency of occurrence) to rank species, community structure is consistently described even with only species abundance data.
6. The method can be applied to short or long‐term datasets and can help identify multimodality and provide valuable insight into how communities change in time or space.

     

Robust and simplified machine learning identification of pitfall trap‐collected ground beetles at the continental scale

1. Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large‐scale insect identification projects are time‐consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.
2. Here, we outline a methodology for training classification models to identify pitfall trap‐collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single‐level) identifying ground beetles from the species to subfamily level. All models were trained using pre‐extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.
3. The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single‐level classification method at higher taxonomic levels.
4. The general methodology outlined here serves as a proof‐of‐concept for classifying pitfall trap‐collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large‐scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa.

     

Automated audio recording as a means of surveying tinamous (Tinamidae) in the Peruvian Amazon

1. The use of machine learning technologies to process large quantities of remotely collected audio data is a powerful emerging research tool in ecology and conservation.
2. We applied these methods to a field study of tinamou (Tinamidae) biology in Madre de Dios, Peru, a region expected to have high levels of interspecies competition and niche partitioning as a result of high tinamou alpha diversity. We used autonomous recording units to gather environmental audio over a period of several months at lowland rainforest sites in the Los Amigos Conservation Concession and developed a Convolutional Neural Network‐based data processing pipeline to detect tinamou vocalizations in the dataset.
3. The classified acoustic event data are comparable to similar metrics derived from an ongoing camera trapping survey at the same site, and it should be possible to combine the two datasets for future explorations of the target species'' niche space parameters.
4. Here, we provide an overview of the methodology used in the data collection and processing pipeline, offer general suggestions for processing large amounts of environmental audio data, and demonstrate how data collected in this manner can be used to answer questions about bird biology.

     

Metadata Made Easy: Develop and Use Domain‐Specific Metadata Schemes by following the dmdScheme approach

1. Metadata plays an essential role in the long‐term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly enough incentivized that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain‐specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co‐development between a metadata expert and the researchers (i.e., the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet.
2. We provide the R package dmdScheme (dmdScheme: An R package for working with domain specific MetaData schemes (Version v0.9.22), 2019) for creating a template domain‐specific scheme. We describe how to create a domain‐specific scheme from this template, including the iterative co‐development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation.
3. The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML.
4. Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by close collaboration between metadata experts and researchers to create domain‐specific schemes. A near‐future priority is to provide methods to interface domain‐specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability.

The article describes a methodology to develop, enter, and validate domain specific metadata schemes which is suitable to be used by nonmetadata specialists. The approach uses an R package which forms the backend of the processing of the metadata, uses spreadsheets to enter the metadata, and provides a server based approach to distribute and use the developed metadata schemes.     

Classifying behavior from short‐interval biologging data: An example with GPS tracking of birds

1. Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.
2. We apply a framework for using K‐means clustering to classify bird behavior using points from short time interval GPS tracks. K‐means clustering is a well‐known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K‐means clustering to six focal variables derived from GPS data collected at 1–11 s intervals from free‐flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life‐stage‐ and age‐related variation in behavior.
3. After filtering for data quality, the K‐means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non‐moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.
4. The K‐means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short‐interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high‐dimensional movement data, it provides insight into small‐scale variation in behavior that would not be possible with many other analytical approaches.

     

The importance of fine‐scale predictors of wild boar habitat use in an isolated population

1. Predicting the likelihood of wildlife presence at potential wildlife–livestock interfaces is challenging. These interfaces are usually relatively small geographical areas where landscapes show large variation over small distances. Models of wildlife distribution based on coarse data over wide geographical ranges may not be representative of these interfaces. High‐resolution data can help identify fine‐scale predictors of wildlife habitat use at a local scale and provide more accurate predictions of species habitat use. These data may be used to inform knowledge of interface risks, such as disease transmission between wildlife and livestock, or human–wildlife conflict.
2. This study uses fine‐scale habitat use data from wild boar (Sus scrofa) based on activity signs and direct field observations in and around the Forest of Dean in Gloucestershire, England. Spatial logistic regression models fitted using a variant of penalized quasi‐likelihood were used to identify habitat‐based and anthropogenic predictors of wild boar signs.
3. Our models showed that within the Forest of Dean, wild boar signs were more likely to be seen in spring, in forest‐type habitats, closer to the center of the forest and near litter bins. In the area surrounding the Forest of Dean, wild boar signs were more likely to be seen in forest‐type habitats and near recreational parks and less likely to be seen near livestock.
4. This approach shows that wild boar habitat use can be predicted using fine‐scale data over comparatively small areas and in human‐dominated landscapes, while taking account of the spatial correlation from other nearby fine‐scale data‐points. The methods we use could be applied to map habitat use of other wildlife species in similar landscapes, or of movement‐restricted, isolated, or fragmented wildlife populations.

     

Beyond the encounter: Predicting multi‐predator risk to elk (Cervus canadensis) in summer using predator scats

1. There is growing evidence that prey perceive the risk of predation and alter their behavior in response, resulting in changes in spatial distribution and potential fitness consequences. Previous approaches to mapping predation risk across a landscape quantify predator space use to estimate potential predator‐prey encounters, yet this approach does not account for successful predator attack resulting in prey mortality. An exception is a prey kill site that reflects an encounter resulting in mortality, but obtaining information on kill sites is expensive and requires time to accumulate adequate sample sizes.
2. We illustrate an alternative approach using predator scat locations and their contents to quantify spatial predation risk for elk (Cervus canadensis) from multiple predators in the Rocky Mountains of Alberta, Canada. We surveyed over 1300 km to detect scats of bears (Ursus arctos/U. americanus), cougars (Puma concolor), coyotes (Canis latrans), and wolves (C. lupus). To derive spatial predation risk, we combined predictions of scat‐based resource selection functions (RSFs) weighted by predator abundance with predictions that a predator‐specific scat in a location contained elk. We evaluated the scat‐based predictions of predation risk by correlating them to predictions based on elk kill sites. We also compared scat‐based predation risk on summer ranges of elk following three migratory tactics for consistency with telemetry‐based metrics of predation risk and cause‐specific mortality of elk.
3. We found a strong correlation between the scat‐based approach presented here and predation risk predicted by kill sites and (r = .98, p < .001). Elk migrating east of the Ya Ha Tinda winter range were exposed to the highest predation risk from cougars, resident elk summering on the Ya Ha Tinda winter range were exposed to the highest predation risk from wolves and coyotes, and elk migrating west to summer in Banff National Park were exposed to highest risk of encountering bears, but it was less likely to find elk in bear scats than in other areas. These patterns were consistent with previous estimates of spatial risk based on telemetry of collared predators and recent cause‐specific mortality patterns in elk.
4. A scat‐based approach can provide a cost‐efficient alternative to kill sites of quantifying broad‐scale, spatial patterns in risk of predation for prey particularly in multiple predator species systems.

     

Marking through molts: An evaluation of visible implant elastomer to permanently mark individuals in a lower termite species

1. Advances in individual marking methods have facilitated detailed studies of animal populations and behavior as they allow tracking of individuals through time and space. Hemimetabolous insects, representing a wide range of commonly used model organisms, present a unique challenge to individual marking as they are not only generally small‐bodied, but also molt throughout development, meaning that traditional surface marks are not persistent.
2. Visible implant elastomer (VIE) offers a potential solution as small amounts of the inert polymer can be implanted under the skin or cuticle of an animal. VIE has proved useful for individually marking fish, crustaceans, and amphibians in both field and laboratory studies and has recently been successfully trialed in laboratory populations of worms and fly larvae. We trialed VIE in the single‐piece nesting termite Zootermopsis angusticollis, a small hemimetabolous insect.
3. We found that there was no effect of VIE on survival and that marks persisted following molting. However, we found some evidence that marked termites performed less allogrooming and trophallaxis than controls, although effect sizes were very small.
4. Our study suggests that VIE is an effective technique for marking small hemimetabolous insects like termites but we advocate that caution is applied, particularly when behavioral observation is important.

     

Beyond BACI: Offsetting carcass numbers with flight intensity to improve risk assessments of bird collisions with power lines

1. The continuing global expansion of electricity networks increases the risk of bird collisions with power lines. Several field studies have demonstrated that this risk can be reduced by marking lines with flight diverters. A before‐after control‐impact (BACI) design is currently the suggested approach for evaluating the effectiveness of these diverters and is generally assumed to give unbiased results.
2. Using systematic flight survey data, we demonstrate that the assumptions underlying the BACI approach are frequently violated, leading to biased effectiveness estimates. We present an alternative field and statistical design in which the number of bird strike victims is directly related to bird flight intensity (“fusion design”), instead of estimating it indirectly using a control site. The presented design is validated based on simulations.
3. We demonstrate that the presented method is unbiased and shows an approximately 3‐fold higher statistical power compared with BACI, even under ideal/unbiased data conditions, with similar field‐experimental effort. Moreover, this approach can provide a direct analysis of bird reactions/collisions, estimation of collision rates, and the possibility of conducting the required fieldwork within a single season.
4. Our presented method can be used to standardize and improve future studies on diverter effectiveness, for example, by supporting the acquisition of a more detailed picture of species‐, diverter type‐, and habitat‐specific estimates.

     

Conventional wisdom on roosting behavior of Australian flying‐foxes—A critical review,and evaluation using new data

1. Fruit bats (Family: Pteropodidae) are animals of great ecological and economic importance, yet their populations are threatened by ongoing habitat loss and human persecution. A lack of ecological knowledge for the vast majority of Pteropodid species presents additional challenges for their conservation and management.
2. In Australia, populations of flying‐fox species (Genus: Pteropus) are declining and management approaches are highly contentious. Australian flying‐fox roosts are exposed to management regimes involving habitat modification, through human–wildlife conflict management policies, or vegetation restoration programs. Details on the fine‐scale roosting ecology of flying‐foxes are not sufficiently known to provide evidence‐based guidance for these regimes, and the impact on flying‐foxes of these habitat modifications is poorly understood.
3. We seek to identify and test commonly held understandings about the roosting ecology of Australian flying‐foxes to inform practical recommendations and guide and refine management practices at flying‐fox roosts.
4. We identify 31 statements relevant to understanding of flying‐fox roosting structure and synthesize these in the context of existing literature. We then contribute a contemporary, fine‐scale dataset on within‐roost structure to further evaluate 11 of these statements. The new dataset encompasses 13‐monthly repeat measures from 2,522 spatially referenced roost trees across eight sites in southeastern Queensland and northeastern New South Wales.
5. We show evidence of sympatry and indirect competition between species, including spatial segregation of black and grey‐headed flying‐foxes within roosts and seasonal displacement of both species by little red flying‐foxes. We demonstrate roost‐specific annual trends in occupancy and abundance and provide updated demographic information including the spatial and temporal distributions of males and females within roosts.
6. Insights from our systematic and quantitative study will be important to guide evidence‐based recommendations on restoration and management and will be crucial for the implementation of priority recovery actions for the preservation of these species in the future.

     

Automated location invariant animal detection in camera trap images using publicly available data sources

1. A time‐consuming challenge faced by camera trap practitioners is the extraction of meaningful data from images to inform ecological management. An increasingly popular solution is automated image classification software. However, most solutions are not sufficiently robust to be deployed on a large scale due to lack of location invariance when transferring models between sites. This prevents optimal use of ecological data resulting in significant expenditure of time and resources to annotate and retrain deep learning models.
2. We present a method ecologists can use to develop optimized location invariant camera trap object detectors by (a) evaluating publicly available image datasets characterized by high intradataset variability in training deep learning models for camera trap object detection and (b) using small subsets of camera trap images to optimize models for high accuracy domain‐specific applications.
3. We collected and annotated three datasets of images of striped hyena, rhinoceros, and pigs, from the image‐sharing websites FlickR and iNaturalist (FiN), to train three object detection models. We compared the performance of these models to that of three models trained on the Wildlife Conservation Society and Camera CATalogue datasets, when tested on out‐of‐sample Snapshot Serengeti datasets. We then increased FiN model robustness by infusing small subsets of camera trap images into training.
4. In all experiments, the mean Average Precision (mAP) of the FiN trained models was significantly higher (82.33%–88.59%) than that achieved by the models trained only on camera trap datasets (38.5%–66.74%). Infusion further improved mAP by 1.78%–32.08%.
5. Ecologists can use FiN images for training deep learning object detection solutions for camera trap image processing to develop location invariant, robust, out‐of‐the‐box software. Models can be further optimized by infusion of 5%–10% camera trap images into training data. This would allow AI technologies to be deployed on a large scale in ecological applications. Datasets and code related to this study are open source and available on this repository: https://doi.org/10.5061/dryad.1c59zw3tx.

     

Favorable spring conditions can buffer the impact of winter carryover effects on a key breeding decision in an Arctic‐breeding seabird

1. The availability and investment of energy among successive life‐history stages is a key feature of carryover effects. In migratory organisms, examining how both winter and spring experiences carryover to affect breeding activity is difficult due to the challenges in tracking individuals through these periods without impacting their behavior, thereby biasing results.
2. Using common eiders Somateria mollissima, we examined whether spring conditions at an Arctic breeding colony (East Bay Island, Nunavut, Canada) can buffer the impacts of winter temperatures on body mass and breeding decisions in birds that winter at different locations (Nuuk and Disko Bay, Greenland, and Newfoundland, Canada; assessed by analyzing stable isotopes of 13‐carbon in winter‐grown claw samples). Specifically, we used path analysis to examine how wintering and spring environmental conditions interact to affect breeding propensity (a key reproductive decision influencing lifetime fitness in female eiders) within the contexts of the timing of colony arrival, pre‐breeding body mass (body condition), and a physiological proxy for foraging effort (baseline corticosterone).
3. We demonstrate that warmer winter temperatures predicted lower body mass at arrival to the nesting colony, whereas warmer spring temperatures predicted earlier arrival dates and higher arrival body mass. Both higher body mass and earlier arrival dates of eider hens increased the probability that birds would initiate laying (i.e., higher breeding propensity). However, variation in baseline corticosterone was not linked to either winter or spring temperatures, and it had no additional downstream effects on breeding propensity.
4. Overall, we demonstrate that favorable pre‐breeding conditions in Arctic‐breeding common eiders can compensate for the impact that unfavorable wintering conditions can have on breeding investment, perhaps due to greater access to foraging areas prior to laying.

     

Video recording and vegetation classification elucidate sheep foraging ecology in species‐rich grassland

1. Factors influencing grazing behavior in species‐rich grasslands have been little studied. Methodologies have mostly had a primary focus on grasslands with lower floristic diversity.
2. We test the hypothesis that grazing behavior is influenced by both animal and plant factors and investigate the relative importance of these factors, using a novel combination of video technology and vegetation classification to analyze bite and step rates.
3. In a semi‐natural, partially wooded grassland in northern Estonia, images of the vegetation being grazed and records of steps and bites were obtained from four video cameras, each mounted on the sternum of a sheep, during 41 animal‐hours of observation over five days. Plant species lists for the immediate field of view were compiled. Images were partnered by direct observation of the nearest‐neighbor relationships of the sheep. TWINSPAN, a standard vegetation classification technique allocating species lists to objectively defined classes by a principal components procedure, was applied to the species lists and 25 vegetation classes (15 open pasture and 10 woodland) were identified from the images.
4. Taking bite and step rates as dependent variables, relative importance of animal factors (sheep identity), relative importance of day, and relative importance of plant factors (vegetation class) were investigated. The strongest effect on bite rates was of vegetation class. Sheep identity was less influential. When the data from woodland were excluded, sheep identity was more important than vegetation class as a source of variability in bite rate on open pasture.
5. The original hypothesis is therefore supported, and we further propose that, at least with sheep in species‐rich open pastures, animal factors will be more important in determining grazing behavior than plant factors. We predict quantifiable within‐breed and between‐breed differences, which could be exploited to optimize conservation grazing practices and contribute to the sustainability of extensive grazing systems.

     

The use of trail cameras to monitor species inhabiting artificial nest boxes

1. Artificial boxes are commonly used in studies of cavity‐dwelling animals of various taxa. One advantage of nest boxes is that cameras can be used to monitor animals inside the cavity, however, most cameras used today have to be built de novo or modified or are expensive.
2. Here, we describe a method for monitoring nest boxes using off‐the‐shelf models of trail cameras that can record photographs and videos in daylight and darkness (TCM; Trail Camera Method). The cameras can record sequentially within a given time lapse or an infra‐red motion sensor can be triggered by activity in the nest box.
3. Using TCM in a Great Tit (Parus major) nest box population, we studied the hourly pattern of the first egg laying and the first egg hatching. We found that Great Tits laid eggs within 2 h of the sunrise while the timing of hatching spanned the 24‐h day. Moreover, we found that the hour of hatching affects the nestlings’ mass on the 2nd day of life, but not on the 12th day of life.
4. Comparing to traditional nest box checks, TMC requires about 75% less time to obtain data on the timing of egg laying and hatching. Moreover, the hour estimation error was several orders of magnitude greater with the traditional method.
5. Our data demonstrate that commercially available trail cameras are an affordable and convenient method of monitoring artificial cavities. Trail cameras are small, standalone, weather‐proof devices with integrated powering, memory storage, lighting, and recording systems. They could be easily swapped between boxes or removed. After small modifications of the box, they could be used to monitor a wide variety of behaviors of many animal taxa.

     

Plotting receiver operating characteristic and precision–recall curves from presence and background data

1. The receiver operating characteristic (ROC) and precision–recall (PR) plots have been widely used to evaluate the performance of species distribution models. Plotting the ROC/PR curves requires a traditional test set with both presence and absence data (namely PA approach), but species absence data are usually not available in reality. Plotting the ROC/PR curves from presence‐only data while treating background data as pseudo absence data (namely PO approach) may provide misleading results.
2. In this study, we propose a new approach to calibrate the ROC/PR curves from presence and background data with user‐provided information on a constant c, namely PB approach. Here, c defines the probability that species occurrence is detected (labeled), and an estimate of c can also be derived from the PB‐based ROC/PR plots given that a model with good ability of discrimination is available. We used five virtual species and a real aerial photography to test the effectiveness of the proposed PB‐based ROC/PR plots. Different models (or classifiers) were trained from presence and background data with various sample sizes. The ROC/PR curves plotted by PA approach were used to benchmark the curves plotted by PO and PB approaches.
3. Experimental results show that the curves and areas under curves by PB approach are more similar to that by PA approach as compared with PO approach. The PB‐based ROC/PR plots also provide highly accurate estimations of c in our experiment.
4. We conclude that the proposed PB‐based ROC/PR plots can provide valuable complements to the existing model assessment methods, and they also provide an additional way to estimate the constant c (or species prevalence) from presence and background data.

     

Can artificial ecological islands alter the biodiversity of macroinvertebrate? A case study in Fujin National Wetland Park,the Sanjiang Plain,China

1. Many policies and studies globally have highlighted the pivotal role of wetland ecosystems regarding wetland biota and their ecological status. With the strengthening of wetland ecosystem management legislation and policy, wetland restoration should also consider increasing habitat diversity to improve biota. We explore whether the construction of artificial ecological islands can increase the diversity of and macroinvertebrates before assessing the effects of actively constructing islands via human intervention on wetland protection.
2. We discuss changes in macroinvertebrate diversity (i) with and without islands, (ii) at different water‐level gradients surrounding the islands, (ⅲ) on different island substrates, and (ⅳ) at different time scales. We used ANOVA, ANOSIM, and cluster analysis to test the differences.
3. The macroinvertebrate communities had spatially heterogeneous distributions which changes over time due to both natural and anthropogenic stresses. The establishment of islands significantly increased the community composition and biodiversity of the macroinvertebrate. Water depth and substrate affect community composition of macrozoobenthos. The abundance and diversity of macroinvertebrates can influence the biodiversity of their predators (fish and waterbirds). Potentially, the construction of islands could provide some cobenefits for the conservation of wetland fauna.

Synthesis and applications. Establishing artificial ecological islands in broad open‐water areas and increasing water‐level gradient and substrate diversity can increase microhabitat availability and habitat heterogeneity. These changes can adapt to different ecological niches of aquatic organisms, increase biodiversity, and have a positive effect on the ecological restoration of inland freshwater marshes and wetlands.     

A community and functional comparison of coral and reef fish assemblages between four decades of coastal urbanisation and thermal stress

1. Urbanized coral reefs experience anthropogenic disturbances caused by coastal development, pollution, and nutrient runoff, resulting in turbid, marginal conditions in which only certain species can persist. Mortality effects are exacerbated by increasingly regular thermal stress events, leading to shifts towards novel communities dominated by habitat generalists and species with low structural complexity.
2. There is limited data on the turnover processes that occur due to this convergence of anthropogenic stressors, and how novel urban ecosystems are structured both at the community and functional levels. As such, it is unclear how they will respond to future disturbance events.
3. Here, we examine the patterns of coral reef community change and determine whether ecosystem functions provided by specialist species are lost post‐disturbance. We present a comparison of community and functional trait‐based changes for scleractinian coral genera and reef fish species assemblages subject to coastal development, coastal modification, and mass bleaching between two time periods, 1975–1976 and 2018, in Nakagusuku Bay, Okinawa, Japan.
4. We observed an increase in fish habitat generalists, a dominance shift from branching to massive/sub‐massive corals and increasing site‐based coral genera richness between years. Fish and coral communities significantly reassembled, but functional trait‐based multivariate space remained constant, indicating a turnover of species with similar traits. A compression of coral habitat occurred, with shallow (<5 m) and deep (>8 m) coral genera shifting towards the mid‐depths (5–8 m).
5. We show that although reef species assemblages altered post disturbance, new communities retained similar ecosystem functions. This result could be linked to the stressors experienced by urban reefs, which reflect those that will occur at an increasing frequency globally in the near future. Yet, even after shifts to disturbed communities, these fully functioning reef systems may maintain high conservation value.

     

A modified niche model for generating food webs with stage‐structured consumers: The stabilizing effects of life‐history stages on complex food webs

1. Almost all organisms grow in size during their lifetime and switch diets, trophic positions, and interacting partners as they grow. Such ontogenetic development introduces life‐history stages and flows of biomass between the stages through growth and reproduction. However, current research on complex food webs rarely considers life‐history stages. The few previously proposed methods do not take full advantage of the existing food web structural models that can produce realistic food web topologies.
2. We extended the niche model developed by Williams and Martinez (Nature, 2000, 404, 180–183) to generate food webs that included trophic species with a life‐history stage structure. Our method aggregated trophic species based on niche overlap to form a life‐history structured population; therefore, it largely preserved the topological structure of food webs generated by the niche model. We applied the theory of allometric predator–prey body mass ratio and parameterized an allometric bioenergetic model augmented with biomass flow between stages via growth and reproduction to study the effects of a stage structure on the stability of food webs.
3. When life‐history stages were linked via growth and reproduction, more food webs persisted, and persisting food webs tended to retain more trophic species. Topological differences between persisting linked and unlinked food webs were small to modest. The slopes of biomass spectra were lower, and weak interaction links were more prevalent in the linked food webs than the unlinked ones, suggesting that a life‐history stage structure promotes characteristics that can enhance stability of complex food webs.
4. Our results suggest a positive relationship between the complexity and stability of complex food webs. A life‐history stage structure in food webs may play important roles in dynamics of and diversity in food webs.