Agent‐based modeling of the effects of forest dynamics,selective logging,and fragment size on epiphyte communities

1. Forest canopies play a crucial role in structuring communities of vascular epiphytes by providing substrate for colonization, by locally varying microclimate, and by causing epiphyte mortality due to branch or tree fall. However, as field studies in the three‐dimensional habitat of epiphytes are generally challenging, our understanding of how forest structure and dynamics influence the structure and dynamics of epiphyte communities is scarce.
2. Mechanistic models can improve our understanding of epiphyte community dynamics. We present such a model that couples dispersal, growth, and mortality of individual epiphytes with substrate dynamics, obtained from a three‐dimensional functional–structural forest model, allowing the study of forest–epiphyte interactions. After validating the epiphyte model with independent field data, we performed several theoretical simulation experiments to assess how (a) differences in natural forest dynamics, (b) selective logging, and (c) forest fragmentation could influence the long‐term dynamics of epiphyte communities.
3. The proportion of arboreal substrate occupied by epiphytes (i.e., saturation level) was tightly linked with forest dynamics and increased with decreasing forest turnover rates. While species richness was, in general, negatively correlated with forest turnover rates, low species numbers in forests with very‐low‐turnover rates were due to competitive exclusion when epiphyte communities became saturated. Logging had a negative impact on epiphyte communities, potentially leading to a near‐complete extirpation of epiphytes when the simulated target diameters fell below a threshold. Fragment size had no effect on epiphyte abundance and saturation level but correlated positively with species numbers.
4. Synthesis: The presented model is a first step toward studying the dynamic forest–epiphyte interactions in an agent‐based modeling framework. Our study suggests forest dynamics as key factor in controlling epiphyte communities. Thus, both natural and human‐induced changes in forest dynamics, for example, increased mortality rates or the loss of large trees, pose challenges for epiphyte conservation.

     

Quantity–quality trade‐offs revealed using a multiscale test of herbivore resource selection on elemental landscapes

1. Herbivores consider the variation of forage qualities (nutritional content and digestibility) as well as quantities (biomass) when foraging. Such selection patterns may change based on the scale of foraging, particularly in the case of ungulates that forage at many scales.
2. To test selection for quality and quantity in free‐ranging herbivores across scales, however, we must first develop landscape‐wide quantitative estimates of both forage quantity and quality. Stoichiometric distribution models (StDMs) bring opportunity to address this because they predict the elemental measures and stoichiometry of resources at landscape extents.
3. Here, we use StDMs to predict elemental measures of understory white birch quality (% nitrogen) and quantity (g carbon/m²) across two boreal landscapes. We analyzed global positioning system (GPS) collared moose (n = 14) selection for forage quantity and quality at the landscape, home range, and patch extents using both individual and pooled resource selection analyses. We predicted that as the scale of resource selection decreased from the landscape to the patch, selection for white birch quantity would decrease and selection for quality would increase.
4. Counter to our prediction, pooled‐models showed selection for our estimates of quantity and quality to be neutral with low explanatory power and no scalar trends. At the individual‐level, however, we found evidence for quality and quantity trade‐offs, most notably at the home‐range scale where resource selection models explain the largest amount of variation in selection. Furthermore, individuals did not follow the same trade‐off tactic, with some preferring forage quantity over quality and vice versa.
5. Such individual trade‐offs show that moose may be flexible in attaining a limiting nutrient. Our findings suggest that herbivores may respond to forage elemental compositions and quantities, giving tools like StDMs merit toward animal ecology applications. The integration of StDMs and animal movement data represents a promising avenue for progress in the field of zoogeochemistry.

     

Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes

1. Landscape change is a key driver of biodiversity declines due to habitat loss and fragmentation, but spatially shifting resources can also facilitate range expansion and invasion. Invasive populations are reproductively successful, and landscape change may buoy this success.
2. We show how modeling the spatial structure of reproductive success can elucidate the mechanisms of range shifts and sustained invasions for mammalian species with attendant young. We use an example of white‐tailed deer (deer; Odocoileus virginianus) expansion in the Nearctic boreal forest, a North American phenomenon implicated in severe declines of threatened woodland caribou (Rangifer tarandus).
3. We hypothesized that deer reproductive success is linked to forage subsidies provided by extensive landscape change via resource extraction. We measured deer occurrence using data from 62 camera traps in northern Alberta, Canada, over three years. We weighed support for multiple competing hypotheses about deer reproductive success using multistate occupancy models and generalized linear models in an AIC‐based model selection framework.
4. Spatial patterns of reproductive success were best explained by features associated with petroleum exploration and extraction, which offer early‐seral vegetation resource subsidies. Effect sizes of anthropogenic features eclipsed natural heterogeneity by two orders of magnitude. We conclude that anthropogenic early‐seral forage subsidies support high springtime reproductive success, mitigating or exceeding winter losses, maintaining populations.
5. Synthesis and Applications. Modeling spatial structuring in reproductive success can become a key goal of remote camera‐based global networks, yielding ecological insights into mechanisms of invasion and range shifts to inform effective decision‐making for global biodiversity conservation.

     

Larger workers outperform smaller workers across resource environments: An evaluation of demographic data using functional linear models

1. Behavior and organization of social groups is thought to be vital to the functioning of societies, yet the contributions of various roles within social groups toward population growth and dynamics have been difficult to quantify. A common approach to quantifying these role‐based contributions is evaluating the number of individuals conducting certain roles, which ignores how behavior might scale up to effects at the population‐level. Manipulative experiments are another common approach to determine population‐level effects, but they often ignore potential feedbacks associated with these various roles.
2. Here, we evaluate the effects of worker size distribution in bumblebee colonies on worker production in 24 observational colonies across three environments, using functional linear models. Functional linear models are an underused correlative technique that has been used to assess lag effects of environmental drivers on plant performance. We demonstrate potential applications of this technique for exploring high‐dimensional ecological systems, such as the contributions of individuals with different traits to colony dynamics.
3. We found that more larger workers had mostly positive effects and more smaller workers had negative effects on worker production. Most of these effects were only detected under low or fluctuating resource environments suggesting that the advantage of colonies with larger‐bodied workers becomes more apparent under stressful conditions.
4. We also demonstrate the wider ecological application of functional linear models. We highlight the advantages and limitations when considering these models, and how they are a valuable complement to many of these performance‐based and manipulative experiments.

     

Automatic detection of fish and tracking of movement for ecology

1. Animal movement studies are conducted to monitor ecosystem health, understand ecological dynamics, and address management and conservation questions. In marine environments, traditional sampling and monitoring methods to measure animal movement are invasive, labor intensive, costly, and limited in the number of individuals that can be feasibly tracked. Automated detection and tracking of small‐scale movements of many animals through cameras are possible but are largely untested in field conditions, hampering applications to ecological questions.
2. Here, we aimed to test the ability of an automated object detection and object tracking pipeline to track small‐scale movement of many individuals in videos. We applied the pipeline to track fish movement in the field and characterize movement behavior. We automated the detection of a common fisheries species (yellowfin bream, Acanthopagrus australis) along a known movement passageway from underwater videos. We then tracked fish movement with three types of tracking algorithms (MOSSE, Seq‐NMS, and SiamMask) and evaluated their accuracy at characterizing movement.
3. We successfully detected yellowfin bream in a multispecies assemblage (F1 score =91%). At least 120 of the 169 individual bream present in videos were correctly identified and tracked. The accuracies among the three tracking architectures varied, with MOSSE and SiamMask achieving an accuracy of 78% and Seq‐NMS 84%.
4. By employing this integrated object detection and tracking pipeline, we demonstrated a noninvasive and reliable approach to studying fish behavior by tracking their movement under field conditions. These cost‐effective technologies provide a means for future studies to scale‐up the analysis of movement across many visual monitoring systems.

     

Assessing invertebrate herbivory in human‐modified tropical forest canopies

1. Studies on the effects of human‐driven forest disturbance usually focus on either biodiversity or carbon dynamics but much less is known about ecosystem processes that span different trophic levels. Herbivory is a fundamental ecological process for ecosystem functioning, but it remains poorly quantified in human‐modified tropical rainforests.
2. Here, we present the results of the largest study to date on the impacts of human disturbances on herbivory. We quantified the incidence (percentage of leaves affected) and severity (the percentage of leaf area lost) of canopy insect herbivory caused by chewers, miners, and gall makers in leaves from 1,076 trees distributed across 20 undisturbed and human‐modified forest plots in the Amazon.
3. We found that chewers dominated herbivory incidence, yet were not a good predictor of the other forms of herbivory at either the stem or plot level. Chewing severity was higher in both logged and logged‐and‐burned primary forests when compared to undisturbed forests. We found no difference in herbivory severity between undisturbed primary forests and secondary forests. Despite evidence at the stem level, neither plot‐level incidence nor severity of the three forms of herbivory responded to disturbance.
4. Synthesis. Our large‐scale study of canopy herbivory confirms that chewers dominate the herbivory signal in tropical forests, but that their influence on leaf area lost cannot predict the incidence or severity of other forms. We found only limited evidence suggesting that human disturbance affects the severity of leaf herbivory, with higher values in logged and logged‐and‐burned forests than undisturbed and secondary forests. Additionally, we found no effect of human disturbance on the incidence of leaf herbivory.

     

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.

     

Remotely sensed between‐individual functional trait variation in a temperate forest

1. Trait‐based ecology holds the promise to explain how plant communities work, for example, how functional diversity may support community productivity. However, so far it has been difficult to combine field‐based approaches assessing traits at the level of plant individuals with limited spatial coverage and approaches using remote sensing (RS) with complete spatial coverage but assessing traits at the level of vegetation pixels rather than individuals. By delineating all individual‐tree crowns within a temperate forest site and then assigning RS‐derived trait measures to these trees, we combine the two approaches, allowing us to use general linear models to estimate the influence of taxonomic or environmental variation on between‐ and within‐species variation across contiguous space.
2. We used airborne imaging spectroscopy and laser scanning to collect individual‐tree RS data from a mixed conifer‐angiosperm forest on a mountain slope extending over 5.5 ha and covering large environmental gradients in elevation as well as light and soil conditions. We derived three biochemical (leaf chlorophyll, carotenoids, and water content) and three architectural traits (plant area index, foliage‐height diversity, and canopy height), which had previously been used to characterize plant function, from the RS data. We then quantified the contributions of taxonomic and environmental variation and their interaction to trait variation and partitioned the remaining within‐species trait variation into smaller‐scale spatial and residual variation. We also investigated the correlation between functional trait and phylogenetic distances at the between‐species level. The forest consisted of 13 tree species of which eight occurred in sufficient abundance for quantitative analysis.
3. On average, taxonomic variation between species accounted for more than 15% of trait variation in biochemical traits but only around 5% (still highly significant) in architectural traits. Biochemical trait distances among species also showed a stronger correlation with phylogenetic distances than did architectural trait distances. Light and soil conditions together with elevation explained slightly more variation than taxonomy across all traits, but in particular increased plant area index (light) and reduced canopy height (elevation). Except for foliage‐height diversity, all traits were affected by significant interactions between taxonomic and environmental variation, the different responses of the eight species to the within‐site environmental gradients potentially contributing to the coexistence of the eight abundant species.
4. We conclude that with high‐resolution RS data it is possible to delineate individual‐tree crowns within a forest and thus assess functional traits derived from RS data at individual level. With this precondition fulfilled, it is then possible to apply tools commonly used in field‐based trait ecology to partition trait variation among individuals into taxonomic and potentially even genetic variation, environmental variation, and interactions between the two. The method proposed here presents a promising way of assessing individual‐based trait information with complete spatial coverage and thus allowing analysis of functional diversity at different scales. This information can help to better understand processes shaping community structure, productivity, and stability of forests.

     

Trait hierarchies are stronger than trait dissimilarities in structuring spatial co‐occurrence patterns of common tree species in a subtropical forest

1. The dissimilarity and hierarchy of trait values that characterize niche and fitness differences, respectively, have been increasingly applied to infer mechanisms driving community assembly and to explain species co‐occurrence patterns. Here, we predict that limiting similarity should result in the spatial segregation of functionally similar species, while functionally similar species will be more likely to co‐occur either due to environmental filtering or due to competitive exclusion of inferior competitors (hereafter hierarchical competition).
2. We used a fully mapped 50‐ha subtropical forest plot in southern China to explore how pairwise spatial associations between saplings and between adult trees were influenced by trait dissimilarity and hierarchy in order to gain insight into assembly mechanisms. We assessed pairwise spatial associations using two summary statistics of spatial point patterns at different spatial scales and compared the effects of trait dissimilarity and trait hierarchy of different functional traits on the interspecific spatial associations. These comparisons allow us to disentangle the effects of limiting similarity, environmental filtering, and hierarchical competition on species co‐occurrence.
3. We found that trait dissimilarity was generally negatively related to interspecific spatial associations for both saplings and adult trees across spatial scales, meaning that species with similar trait values were more likely to co‐occur and thus supporting environmental filtering or hierarchical competition. We further found that trait hierarchy outweighed trait dissimilarity in structuring pairwise spatial associations, suggesting that hierarchical competition played a more important role in structuring our forest community than environmental filtering across life stages.
4. This study employed a novel method, by offering the integration of pairwise spatial association and trait dissimilarity as well as trait hierarchy, to disentangle the relative importance of multiple assembly mechanisms in structuring co‐occurrence patterns, especially the mechanisms of environmental filtering and hierarchical competition, which lead to indistinguishable co‐occurrence patterns. This study also reinforced the importance of trait hierarchy rather than trait dissimilarity in driving neighborhood competition.

     

The influence of competing root symbionts on below‐ground plant resource allocation

1. Plants typically interact with multiple above‐ and below‐ground organisms simultaneously, with their symbiotic relationships spanning a continuum ranging from mutualism, such as with arbuscular mycorrhizal fungi (AMF), to parasitism, including symbioses with plant‐parasitic nematodes (PPN).
2. Although research is revealing the patterns of plant resource allocation to mutualistic AMF partners under different host and environmental constraints, the root ecosystem, with multiple competing symbionts, is often ignored. Such competition is likely to heavily influence resource allocation to symbionts.
3. Here, we outline and discuss the competition between AMF and PPN for the finite supply of host plant resources, highlighting the need for a more holistic understanding of the influence of below‐ground interactions on plant resource allocation. Based on recent developments in our understanding of other symbiotic systems such as legume–rhizobia and AMF‐aphid‐plant, we propose hypotheses for the distribution of plant resources between contrasting below‐ground symbionts and how such competition may affect the host.
4. We identify relevant knowledge gaps at the physiological and molecular scales which, if resolved, will improve our understanding of the true ecological significance and potential future exploitation of AMF‐PPN‐plant interactions in order to optimize plant growth. To resolve these outstanding knowledge gaps, we propose the application of well‐established methods in isotope tracing and nutrient budgeting to monitor the movement of nutrients between symbionts. By combining these approaches with novel time of arrival experiments and experimental systems involving multiple plant hosts interlinked by common mycelial networks, it may be possible to reveal the impact of multiple, simultaneous colonizations by competing symbionts on carbon and nutrient flows across ecologically important scales.

     

Maternal effects and the outcome of interspecific competition

1. Maternal environmental effects create lagged population responses to past environments. Although they are ubiquitous and vary in expression across taxa, it remains unclear if and how their presence alters competitive interactions in ecological communities.
2. Here, we use a discrete‐time competition model to simulate how maternal effects alter competitive dynamics in fluctuating and constant environments. Further, we explore how omitting maternal effects alter estimates of known model parameters from observational time series data.
3. Our simulations demonstrate that (i) maternal effects change competitive outcomes, regardless of whether competitors otherwise interact neutrally or exhibit non‐neutral competitive differences, (ii) the consequences of maternal effects for competitive outcomes are mediated by the temporal structure of environmental variation, (iii) even in constant conditions, competitive outcomes are influenced by species'' maternal effects strategies, and (iv) in observational time series data, omitting maternal effects reduces variation explained by models and biases parameter estimates, including competition coefficients.
4. Our findings demonstrate that the ecological consequences of maternal effects hinge on the competitive environment. Evolutionary biologists have long recognized that maternal effects can be an important but often overlooked strategy buffering populations from environmental change. We suggest that maternal effects are similarly critical to ecology and call for research into maternal effects as drivers of dynamics in populations and communities.

     

Scaling the extinction vortex: Body size as a predictor of population dynamics close to extinction events

1. Mutual reinforcement between abiotic and biotic factors can drive small populations into a catastrophic downward spiral to extinction—a process known as the “extinction vortex.” However, empirical studies investigating extinction dynamics in relation to species'' traits have been lacking.
2. We assembled a database of 35 vertebrate populations monitored to extirpation over a period of at least ten years, represented by 32 different species, including 25 birds, five mammals, and two reptiles. We supplemented these population time series with species‐specific mean adult body size to investigate whether this key intrinsic trait affects the dynamics of populations declining toward extinction.
3. We performed three analyses to quantify the effects of adult body size on three characteristics of population dynamics: time to extinction, population growth rate, and residual variability in population growth rate.
4. Our results provide support for the existence of extinction vortex dynamics in extirpated populations. We show that populations typically decline nonlinearly to extinction, while both the rate of population decline and variability in population growth rate increase as extinction is approached. Our results also suggest that smaller‐bodied species are particularly prone to the extinction vortex, with larger increases in rates of population decline and population growth rate variability when compared to larger‐bodied species.
5. Our results reaffirm and extend our understanding of extinction dynamics in real‐life extirpated populations. In particular, we suggest that smaller‐bodied species may be at greater risk of rapid collapse to extinction than larger‐bodied species, and thus, management of smaller‐bodied species should focus on maintaining higher population abundances as a priority.

     

Collecting eco‐evolutionary data in the dark: Impediments to subterranean research and how to overcome them

1. Caves and other subterranean habitats fulfill the requirements of experimental model systems to address general questions in ecology and evolution. Yet, the harsh working conditions of these environments and the uniqueness of the subterranean organisms have challenged most attempts to pursuit standardized research.
2. Two main obstacles have synergistically hampered previous attempts. First, there is a habitat impediment related to the objective difficulties of exploring subterranean habitats and our inability to access the network of fissures that represents the elective habitat for the so‐called “cave species.” Second, there is a biological impediment illustrated by the rarity of most subterranean species and their low physiological tolerance, often limiting sample size and complicating laboratory experiments.
3. We explore the advantages and disadvantages of four general experimental setups (in situ, quasi in situ, ex situ, and in silico) in the light of habitat and biological impediments. We also discuss the potential of indirect approaches to research. Furthermore, using bibliometric data, we provide a quantitative overview of the model organisms that scientists have exploited in the study of subterranean life.
4. Our over‐arching goal is to promote caves as model systems where one can perform standardized scientific research. This is important not only to achieve an in‐depth understanding of the functioning of subterranean ecosystems but also to fully exploit their long‐discussed potential in addressing general scientific questions with implications beyond the boundaries of this discipline.

     

Age‐specific habitat preference,carrying capacity,and landscape structure determine the response of population spatial variability to fishing‐driven age truncation

1. Understanding the mechanisms underlying spatial variability of exploited fish is critical for the sustainable management of fish stocks. Empirical studies suggest that size‐selective fishing can elevate fish population spatial variability (i.e., more heterogeneous distribution) through age truncation, making the population less resilient to changing environment. However, species differ in how their spatial variability responds to age truncation and the underlying mechanisms remain unclear.
2. We hypothesize that age‐specific habitat preference, together with environmental carrying capacity and landscape structure, determines the response of population spatial variability to fishing‐induced age truncation. To test these hypotheses, we design an individual‐based model of an age‐structured fish population on a two‐dimensional landscape under size‐selective fishing. Individual fish reproduces and survives, and moves between habitats according to age‐specific habitat preference and density‐dependent habitat selection.
3. Population spatial variability elevates with increasing age truncation, and the response is stronger for populations with stronger age‐specific habitat preference. On a gradient landscape, reducing carrying capacity elevates the relative importance of density dependence in habitat selection, which weakens the response of spatial variability to age truncation for populations with strong age‐specific habitat preference. On a fragmented landscape, both populations with strong and weak age‐specific habitat preferences are restricted at local optimal habitats, and reducing carrying capacity weakens the responses of spatial variability to age truncation for both populations.
4. Synthesis and applications. We demonstrate that to track and predict the changes in population spatial variability under exploitation, it is essential to consider the interactive effects of age‐specific habitat preference, carrying capacity, and landscape structure. To improve spatial management in fisheries, it is crucial to enhance empirical and theoretical developments in the methodology to quantify age‐specific habitat preference of marine fish, and to understand how climatic change influences carrying capacity and landscape continuity.

     

The normalized segment classification model: A new tool to compare spectral reflectance curves

1. Color patterns are complex traits under selective pressures from conspecifics, mutualists, and antagonists. To evaluate the salience of a pattern or the similarity between colors, several visual models are available. Color discrimination models estimate the perceptual difference between any two colors. Their application to a diversity of taxonomic groups has become common in the literature to answer behavioral, ecological, and evolutionary questions. To use these models, we need information about the visual system of our beholder species. However, many color patterns are simultaneously subject to selective pressures from different species, often from different taxonomic groups, with different visual systems. Furthermore, we lack information about the visual system of many species, leading ecologists to use surrogate values or theoretical estimates for model parameters.
2. Here, we present a modification of the segment classification method proposed by Endler (Biological Journal of the Linnean Society, 1990 41, 315–352): the normalized segment classification model (NSC). We explain its logic and use, exploring how NSC differs from other visual models. We also compare its predictions with available experimental data.
3. Even though the NSC model includes no information about the visual system of the receiver species, it performed better than traditional color discrimination models when predicting the output of some behavioral tasks. Although vision scientists define color as independent of stimulus brightness, a likely explanation for the goodness of fit of the NSC model is that its distance measure depends on brightness differences, and achromatic information can influence the decision‐making process of animals when chromatic information is missing.
4. Species‐specific models may be insufficient for the study of color patterns in a community context. The NSC model offers a species‐independent solution for color analyses, allowing us to calculate color differences when we ignore the intended viewer of a signal or when different species impose selective pressures on the signal.

     

Pliant pathogens: Estimating viral spread when confronted with new vector,host, and environmental conditions

1. Pathogen spread rates are determined, in part, by the performance of pathogens under altered environmental conditions and their ability to persist while switching among hosts and vectors.
2. To determine the effects of new conditions (host, vector, and nutrient) on pathogen spread rate, we introduced a vector‐borne viral plant pathogen, Barley Yellow Dwarf Virus PAV (BYDV‐PAV) into hosts, vectors, and host nutrient supplies that it had not encountered for thousands of viral generations. We quantified pathogen prevalence over the course of two serial inoculations under the new conditions. Using individual‐level transmission rates from this experiment, we parameterized a dynamical model of disease spread and projected spread across host populations through a growing season.
3. A change in nutrient conditions (increased supply of phosphorus) reduced viral transmission whereas shifting to a new vector or host species had no effect on infection prevalence. However, the reduction in the new nutrient environment was only temporary; infection prevalence recovered after the second inoculation.
4. Synthesis. These results highlight how robust the pathogen, BYDV‐PAV, is to changes in its biotic and abiotic environment. Our study also highlights the need to quantify longitudinal infection information beyond snapshot assessments to project disease risk for pathogens in new environments.

     

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.

     

Integrating omics to characterize eco‐physiological adaptations: How moose diet and metabolism differ across biogeographic zones

1. With accelerated land conversion and global heating at northern latitudes, it becomes crucial to understand, how life histories of animals in extreme environments adapt to these changes. Animals may either adapt by adjusting foraging behavior or through physiological responses, including adjusting their energy metabolism or both. Until now, it has been difficult to study such adaptations in free‐ranging animals due to methodological constraints that prevent extensive spatiotemporal coverage of ecological and physiological data.
2. Through a novel approach of combining DNA‐metabarcoding and nuclear magnetic resonance (NMR)‐based metabolomics, we aim to elucidate the links between diets and metabolism in Scandinavian moose Alces alces over three biogeographic zones using a unique dataset of 265 marked individuals.
3. Based on 17 diet items, we identified four different classes of diet types that match browse species availability in respective ecoregions in northern Sweden. Individuals in the boreal zone consumed predominantly pine and had the least diverse diets, while individuals with highest diet diversity occurred in the coastal areas. Males exhibited lower average diet diversity than females.
4. We identified several molecular markers indicating metabolic constraints linked to diet constraints in terms of food availability during winter. While animals consuming pine had higher lipid, phospocholine, and glycerophosphocholine concentrations in their serum than other diet types, birch‐ and willow/aspen‐rich diets exhibit elevated concentrations of several amino acids. The individuals with highest diet diversity had increased levels of ketone bodies, indicating extensive periods of starvation for these individuals.
5. Our results show how the adaptive capacity of moose at the eco‐physiological level varies over a large eco‐geographic scale and how it responds to land use pressures. In light of extensive ongoing climate and land use changes, these findings pave the way for future scenario building for animal adaptive capacity.

     

Joint control of plant ecological strategy by climate,regeneration mode, and ontogeny in Northeastern Chinese forests

1. Research on how plant ecological strategies (competitive, stress‐tolerant, or ruderal) vary within species may improve our understanding of plant and community responses to climate warming and also successional changes. With increasing temperature, the importance of ruderal (R) and stress tolerance (S) components is hypothesized to decrease, while the strength of the competitive (C) component should increase. Offshoots and younger plants are predicted to have greater R and smaller S components.
2. Leaf area, leaf dry matter content, and specific leaf area were measured for 1,344 forest plants belonging to 134 species in Liangshui and Fenglin Nature Reserves in Northeastern China, and C, R, and S scores calculated for each. Linear mixed effect models were used to assess how these indicators differed among study sites (n = 2), regeneration types, ontogenetic stages, and plant life forms. The two study sites have an average annual temperature difference of 0.675°C, simulating a temperature increase of 0.630°C due to climate warming.
3. Higher temperatures reduce low‐temperature stress and frost damage, which may explain the observed decrease in R and S scores; at the same time, plant competitive ability increased, as manifested by higher C scores. This effect was most pronounced for herbaceous plants, but nearly negligible as compared to the effect of regeneration type for trees and of ontogeny for woody species. Resprouting trees and younger woody plants had higher R scores and lower S scores, a sign of adaptation to high disturbance.
4. In this study, a small increase in mean annual temperature led to shifts in CSR strategy components for herbaceous species, without altering the vegetation type or community composition. Offshoots and younger plants had higher R and lower S scores, shedding light on similar changes in the ecological strategies of tree communities during secondary succession, such as the transition of Quercus mongolica coppices to forest and age‐related changes in Populus davidiana–Betula platyphylla forests.