Shift In Distribution Research Articles

Differentiable Integrated Motion Prediction and Planning With Learnable Cost Function for Autonomous Driving.

Predicting the future states of surrounding traffic participants and planning a safe, smooth, and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs). There are two major issues with the current autonomous driving system: the prediction module is often separated from the planning module, and the cost function for planning is hard to specify and tune. To tackle these issues, we propose a differentiable integrated prediction and planning (DIPP) framework that can also learn the cost function from data. Specifically, our framework uses a differentiable nonlinear optimizer as the motion planner, which takes as input the predicted trajectories of surrounding agents given by the neural network and optimizes the trajectory for the AV, enabling all operations to be differentiable, including the cost function weights. The proposed framework is trained on a large-scale real-world driving dataset to imitate human driving trajectories in the entire driving scene and validated in both open-loop and closed-loop manners. The open-loop testing results reveal that the proposed method outperforms the baseline methods across a variety of metrics and delivers planning-centric prediction results, allowing the planning module to output trajectories close to those of human drivers. In closed-loop testing, the proposed method outperforms various baseline methods, showing the ability to handle complex urban driving scenarios and robustness against the distributional shift. Importantly, we find that joint training of planning and prediction modules achieves better performance than planning with a separate trained prediction module in both open-loop and closed-loop tests. Moreover, the ablation study indicates that the learnable components in the framework are essential to ensure planning stability and performance. Code and Supplementary Videos are available at https://mczhi.github.io/DIPP/.

Kidney replacement therapy trends in end-stage kidney disease patients in South Korea during the COVID-19 pandemic.

The corona virus disease 2019 posed a major risk for end-stage kidney disease (ESKD) cases. Our study aimed to assess changes in kidney replacement therapy (KRT) trends and healthcare access for these patients during the pandemic. We retrospectively analyzed nationwide data from July 2017 to June 2022 to assess changes in KRT and ESKD incidence. KRT modalities included peritoneal dialysis (PD), hemodialysis (HD), and kidney transplantation (KT). We utilized the interrupted time series (ITS) method to compare changes in KRT modality before and after the incidence of the COVID-19 pandemic. ESKD incidence remained stable from 2018 to April 2022. The ITS analysis confirmed that the pandemic did not significant impact overall KRT incidence. PD cases decreased (5.7% to 1.3%), while HD cases increased (81.6% to 85%), and KT recipient remained relatively stable (12.7% to 17.3%). The hospitalization and hospital stay decreased in nursing hospital (165.01 days to 147.77 days) and general hospital (61.34 days to 55.58 days) during the pandemic, however, remained unchanged for PD and KT. Our findings indicate no significant changes in ESKD incidence in South Korea during the pandemic. However, there were shifts in modality distribution, with decreased PD and increased HD cases. Notably, HD cases showed a significant reduction in hospital admissions and length of stay. The healthcare system demonstrated stability during the pandemic, with minimal disruptions in ESKD care.

Projecting Untruncated Climate Change Effects on Species' Climate Suitability: Insights From an Alpine Country.

Climate projections for continental Europe indicate drier summers, increased annual precipitation, and less snowy winters, which are expected to cause shifts in species' distributions. Yet, most regions/countries currently lack comprehensive climate-driven biodiversity projections across taxonomic groups, challenging effective conservation efforts. To address this gap, our study evaluated the potential effects of climate change on the biodiversity of an alpine country of Europe, Switzerland. We used a state-of-the art species distribution modeling approach and species occurrence data that covered the climatic conditions encountered across the full species' ranges to help limiting niche truncation. We quantified the relationship between baseline climate and the spatial distribution of 7291 species from 12 main taxonomic groups and projected future climate suitability for three 30-year periods and two greenhouse gas concentration scenarios (RCP4.5 and 8.5). Our results indicated important effects of projected climate changes on species' climate suitability, with responses varying by the taxonomic and conservation status group. The percentage of species facing major changes in climate suitability was higher under RCP8.5 (68%) compared to RCP4.5 (66%). By the end of the century, decreases in climate suitability were projected for 3000 species under RCP8.5 and 1758 species under RCP4.5. The most affected groups under RCP8.5 were molluscs, algae, and amphibians, while it was molluscs, birds, and vascular plants under RCP4.5. Spatially, by 2070-2099, we projected an overall decrease in climate suitability for 39% of the cells in the study area under RCP8.5 and 10% under RCP4.5, while projecting an increase for 50% of the cells under RCP8.5 and 73% under RCP4.5. The most consistent geographical shifts were upward, southward, and eastward. We found that the coverage of high climate suitability cells by protected areas was expected to increase. Our models and maps provide guidance for spatial conservation planning by pointing out future climate-suitable areas for biodiversity.

Near-Term Forecasting of Terrestrial Mobile Species Distributions for Adaptive Management Under Extreme Weather Events.

Across the globe, mobile species are key components of ecosystems. Migratory birds and nomadic antelope can have considerable conservation, economic or societal value, while irruptive insects can be major pests and threaten food security. Extreme weather events, which are increasing in frequency and intensity under ongoing climate change, are driving rapid and unforeseen shifts in mobile species distributions. This challenges their management, potentially leading to population declines, or exacerbating the adverse impacts of pests. Near-term, within-year forecasting may have the potential to anticipate mobile species distribution changes during extreme weather events, thus informing adaptive management strategies. Here, for the first time, we assess the robustness of near-term forecasting of the distribution of a terrestrial species under extreme weather. For this, we generated near-term (2 weeks to 7 months ahead) distribution forecasts for a crop pest that is a threat to food security in southern Africa, the red-billed quelea Quelea quelea. To assess performance, we generated hindcasts of the species distribution across 13 years (2004-2016) that encompassed two major droughts. We show that, using dynamic species distribution models (D-SDMs), environmental suitability for quelea can be accurately forecast with seasonal lead times (up to 7 months ahead), at high resolution, and across a large spatial scale, including in extreme drought conditions. D-SDM predictive accuracy and near-term hindcast reliability were primarily driven by the availability of training data rather than overarching weather conditions. We discuss how a forecasting system could be used to inform adaptive management of mobile species and mitigate impacts of extreme weather, including by anticipating sites and times for transient management and proactively mobilising resources for prepared responses. Our results suggest that such techniques could be widely applied to inform more resilient, adaptive management of mobile species worldwide.

Microplastics and their interaction with microorganisms in Bosten Lake water

Microplastic pollution in inland freshwater lakes, formed from melting ice, atmospheric precipitation, and groundwater, remains under-researched. This study examines microplastic (MPS) presence and distribution in Bosten Lake and their interaction with microorganisms. We employed a Confocal Raman spectrometer to identify MPS types and 16S rRNA high-throughput sequencing to analyze microbial diversity and composition. In May, the lake's average microplastic concentration was 108.20 particles per liter (PCS/L), decreasing to 21.67 PCS/L by October. Transparent and yellow fibrous microplastics, predominantly 0.2–0.5 mm in size, were most common. Key microplastics in May included PEEK (27%), PMMA (25%), and PET (9%), while October saw a prevalence of PET (35%), PS (13%), and PA (9%). Higher microplastic concentrations correlated with reduced microbial diversity. In May, the high microplastic concentration group (MH) showed increased Planctomycetota, while in October, Patescibacteria and Bacteroidota rose significantly in the high plastic group (OH), known for microplastic degradation. MH also had a higher abundance of Cryptomycota compared to OH. Functional analysis indicated that MH had more genes related to bisphenol, benzene, and chlorine compounds, whereas OH had genes linked to methane, nitrogen, and organic matter degradation. Microplastic concentrations peaked near the lake inlet and tourist areas, affecting microbial quantity, diversity, structure, and function. This research enhances our understanding of MPS distribution and environmental microbial shifts in similar settings, providing essential data for environmental management strategies at Bosten Lake.

Genomics-Informed Range Predictions Under Global Warming Reveal Reduced Adaptive Diversity Whilst Buffering Range Shifts for a Marine Snail.

Understanding the genetic basis of local adaptation in thermal performance is useful for predicting species distribution shifts under anthropogenic climate change. Many species are distributed across multiple biogeographic regions, and the uniquely adapted populations in each region may respond to future ocean warming with distinct distribution changes. In the present study, we investigated phylogeographic patterns, thermal sensitivity, and genetic differentiation in the intertidal snail Littorina brevicula along China's coast. Whole-genome sequencing results based on a newly assembled chromosome-level genome revealed two genetic lineages, with a north-south divergence that is linked to the thermal environment. Within each lineage, individuals could be further subdivided into genetic subgroups that differ at key genomic loci underpinning differences in upper heat tolerance. Heat stress drives adaptive divergence across multiple levels of organization, from the individual to the biogeographic level. Taking into account genetic diversity associated with variation in heat tolerance, a physiological species distribution model (pSDM) was applied to predict the distributions of the different genetic subgroups in response to climate change. Both northern and southern lineages were predicted to experience declines in habitat suitability under a 4°C future warming scenario, and that a genotypic subset of snails from the southern lineage may even be driven to extinction. These findings illustrate that even when a species' range is maintained, it can nonetheless experience a significant decrease in adaptive diversity as a result of climate change. The integrated approach presented here, which considered both physiological and adaptive genetic variation at the level of individuals within a biogeographical context, provided new insights into how marine species can respond to global warming.

Characteristics of powder charging behavior via forced triboelectric charging on an inclined chute

This study presents a novel method for selective charging of powders by dry triboelectric forced charging utilized as the first stage of material sorting in an electric field. In forced triboelectric charging, a high voltage is applied to the copper chute in which particles are charged during their transportation. In this research, the effect of the operating parameters of the chute, including mass flow rate, angle of inclination, and length, on the specific particle charge of limestone powders was investigated. In addition, the behavior of the charged particles in an electrostatic sorter was analyzed by evaluating their charge distribution after sorting. The results reveal that as the length of the chute increases, the specific charge asymptotically approaches a saturation value, which is determined by the high voltage applied. Furthermore, for a given chute length, the high voltage influences the amount and the sign of the specific particle charge, including a high voltage value at which the particles are neutral on average, i.e. the so-called point of zero net charge (PZNC). This PZNC is of particular interest for sorting out a target component from a powder mixture. However, if the mass flow rate is increased above a certain value, the specific charge decreases as the number of particle wall contacts is reduced due to stratification. Bipolar charge distributions were observed for all investigated high voltages, with the distribution width increasing with particle size to the power of 2.1. The modal value of the charge distribution shifts according to the difference in voltage applied to the PZNC.

Out-of-distribution detection in digital pathology: Do foundation models bring the end to reconstruction-based approaches?

Artificial intelligence (AI) has shown promising results for computational pathology tasks. However, one of the limitations in clinical practice is that these algorithms are optimised for the distribution represented by the training data. For out-of-distribution (OOD) data, they often deliver predictions with equal confidence, even though these often are incorrect. In the pursuit of OOD detection in digital pathology, this study evaluates the state-of-the-art (SOTA) in computational pathology OOD detection, based on diffusion probabilistic models, specifically by adapting the latent diffusion model (LDM) for this purpose (AnoLDM). We compare this against post-hoc methods based on the latent space of foundation models, which are SOTA in general computer vision research. The approaches are not only evaluated on data from the same medical centres as the training set, but also on several datasets with data distribution shifts. The results show that AnoLDM performs similarly well or better than diffusion model based approaches published in previous studies in computational pathology but with reduced computational costs. However, our optimal configuration of an approach based on foundation models (kang_residual) outperforms AnoLDM on OOD detection on data not experiencing any covariate shifts, with an AUROC of 96.17 versus 91.86. Interestingly, AnoLDM is more successful at handling the data distribution shifts investigated in this study. However, both AnoLDM and kang_residual suffer substantial loss in the performance under the data distribution shifts, hence future work should focus on improving the generalisation of OOD detection for computational pathology applications.

Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer.

Since labeled samples are typically scarce in real-world scenarios, self-supervised representation learning in time series is critical. Existing approaches mainly employ the contrastive learning framework, which automatically learns to understand similar and dissimilar data pairs. However, they are constrained by the request for cumbersome sampling policies and prior knowledge of constructing pairs. Also, few works have focused on effectively modeling temporal-spectral correlations to improve the capacity of representations. In this article, we propose the cross reconstruction transformer (CRT) to solve the aforementioned issues. CRT achieves time series representation learning through a cross-domain dropping-reconstruction task. Specifically, we obtain the frequency domain of the time series via the fast Fourier transform (FFT) and randomly drop certain patches in both time and frequency domains. Dropping is employed to maximally preserve the global context while masking leads to the distribution shift. Then a Transformer architecture is utilized to adequately discover the cross-domain correlations between temporal and spectral information through reconstructing data in both domains, which is called Dropped Temporal-Spectral Modeling. To discriminate the representations in global latent space, we propose instance discrimination constraint (IDC) to reduce the mutual information between different time series samples and sharpen the decision boundaries. Additionally, a specified curriculum learning (CL) strategy is employed to improve the robustness during the pretraining phase, which progressively increases the dropping ratio in the training process. We conduct extensive experiments to evaluate the effectiveness of the proposed method on multiple real-world datasets. Results show that CRT consistently achieves the best performance over existing methods by 2%-9%. The code is publicly available at https://github.com/BobZwr/Cross-Reconstruction-Transformer.

Comprehensive stereotactic radiosurgery platform characterization: A novel end-to-end approach with anthropomorphic 3D dosimetry.

Stereotactic radiosurgery (SRS) is a widely employed strategy for intracranial metastases, utilizing linear accelerators and volumetric modulated arc therapy (VMAT). Ensuring precise linear accelerator performance is crucial, given the small planning target volume (PTV) margins. Rapid dose falloff is vital to minimize brain radiation necrosis. Despite advances in SRS planning, tools for end-to-end testing of SRS treatments are lacking, hindering confidence in the procedure. This study introduces a novel end-to-end three-dimensional (3D) anthropomorphic dosimetry system for characterization of a radiosurgery platform, aiming to measure planning metrics, dose gradient index (DGI), brain volumes receiving at least 10 and 12Gy (V10, V12), as well as assess delivery uncertainties in multitarget treatments. The study also compares metrics from benchmark plans to enhance understanding and confidence in SRS treatments. The developed anthropomorphic 3D dosimetry system includes a modified Stereotactic End-to-End Verification (STEEV) phantom with a customized insert integrating 3D dosimeters and a fiber optic CT scanner. Labview and MATLAB programs handle optical scanning, image preprocessing, and dosimetric analysis. SlicerRT is used for 3D dose visualization and analysis. A film stack insert was used to validate the 3D dosimeter measurements at specific slices. Benchmark plans were developed and measured to investigate off-axis errors, dose spillage, small field dosimetry, and multi-target delivery. The accuracy of the developed 3D dosimetry system was rigorously assessed using radiochromic films. Two two-dimensional (2D) dose planes, extracted from the 3D dose distribution, were compared with film measurements, resulting in high passing rates of 99.9% and 99.6% in gamma tests. The mean relative dose difference between film and 3D dosimeter measurements was -1%, with a standard deviation of 2.2%, well within dosimeter uncertainties. In the first module, evaluating single-isocenter multitarget treatments, a 1.5mm dose distribution shift was observed when targets were 7cm off-axis. This shift was attributed to machine mechanical errors and image-guided system uncertainties, indicating potential limitations in conventional gamma tests. The second module investigated discrepancies in intermediate-to-low dose spillage, revealing higher measured doses in the connecting region between closely positioned targets. This discrepancy was linked to uncertainties in treatment planning system (TPS) modeling of out-of-field dose and multileaf collimator (MLC) characteristics, resulting in lower DGI values and higher V10 and V12 values compared to TPS calculations. In the third module, irradiating multiple targets showed consistent V10 and V12 values within 1 cm3 agreement with dose calculations. However, lower DGI values from measurements compared to calculations suggested intricacies in the treatment process. Conducting vital end-to-end testing demonstrated the anthropomorphic 3D dosimetry system's capacity to assess overall treatment uncertainty, offering a valuable tool for enhancing treatment accuracy in radiosurgery platforms. The study introduces a novel anthropomorphic 3D dosimetry system for end-to-end testing of a radiosurgery platform. The system effectively measures plan quality metrics, captures mechanical errors, and visualizes dose discrepancies in 3D space. The comprehensive evaluation capability enhances confidence in the commissioning and verification process, ensuring patient safety. The system is recommended for commissioning new radiosurgery platforms and remote auditing of existing programs.

Shift in distribution of division of labour in chronically stressed honeybee colonies after perturbation.

Division of labour (DOL) in eusocial insects plays an important role in colony fitness. Honeybees face a variety of stressors that compromise the homeostasis of the colony and reduce survival and reproduction. Considering the significance of DOL in colony homeostasis, it is important to understand whether and how DOL may be altered as a result of chronic stress. Therefore, we tested whether honeybee colonies shift DOL in response to high infestation with the parasitic mite Varroa destructor. For this, we monitored chronically stressed and presumably low-stress colonies from April till December 2022. During the experiment, we applied a cold shock to test whether a perturbation resulted in a larger alteration in DOL in chronically stressed colonies. We found that after cold shock, there was a lower proportion of nurses in the chronically stressed colonies. For foragers, we found higher activity post-cold shock in chronically stressed colonies, but no difference between treatments in nectar inflow, suggesting less efficient foragers. Furthermore, we found that there was an accelerated task switch in chronically stressed colonies after the cold shock. The large changes after the perturbation may indicate inefficient task allocation due to chronic stress. Our study contributes to the understanding of social resilience and chronic stress responses in eusocial animals.

Fish distribution shifts due to climate change in the Northeast Atlantic: Using a hierarchical filtering approach on marine-estuarine opportunist species

Marine-estuarine opportunist (MEO) species are fish that occur in the continental shelf and use estuaries and/or shallow coastal areas as nurseries. These commercially important resources are facing significant environmental modifications caused by direct and/or indirect anthropogenic climate change effects. In this study, we investigated the directionality and the magnitude of the distribution shifts (i.e., range size, gravity centroids, and margins) in marine environment suitability for six main MEO fish species within the Northeast Atlantic expected for the end of the 21st century. In the framework of this study, we have distinguished ‘sub-boreal’ from ‘sub-tropical’ species. The ‘hierarchical filters’ concept was adopted for modelling the potential species distributions and combined the predictions of i) a bioclimatic model with ii) a habitat model. The bioclimatic model is based on large-scale and time-variant variables while variables of the habitat model are fine-grained and time-invariant. Two Intergovernmental Panel on Climate Change (IPCC) scenarios are tested: an intermediate (SSP2-4.5) and a pessimistic one (SSP5-8.5). We applied this framework using international databases of biodiversity occurrences, ensemble forecasting producing consensual predictions, and innovative indices of distribution shifts. A visible north-westward shift was predicted for all six species in our study area. However, the northward expansion was greater for ‘sub-tropical’ than for ‘sub-boreal’ species due to faster gravity centroid displacement shifts and faster margins shifts. These range shifts may lead to major ecological impacts (e.g., changes in recruitment to estuarine and coastal nurseries, as well as changes in spawning grounds) that may alter populations' connectivity.

Insights into the seasonal variation, distribution, composition and dynamics of microplastics in the Ganga River ecosystem of Varanasi City, Uttar Pradesh, India.

The current study explores the seasonal dynamics of microplastic (MP) pollution in the Ganga River of Varanasi City, Uttar Pradesh, India, focusing on water and sediment samples collected during pre-monsoon and post-monsoon periods. The analysis shows significant variations in MP occurrence, shape dynamics, color distribution, and size composition across diverse sampling sites. During the pre-monsoon season, MP concentrations ranged from 17 to 36 particles/L in water samples and 160 to 312 particles/kg in sediment, indicating a moderate to high level of contamination. Post-monsoon sampling showed higher MP concentrations at most sites, indicating the influence of seasonal hydrological changes on MP distribution. Shifts in MP shape dynamics were observed between seasons, with films, foams, fragments, and filaments showing variable distributions. Similarly, color variations in MPs exhibited site-specific patterns, with white, brown, blue, and other colors being predominant. These findings highlight the diverse sources and compositions of MPs in the river ecosystem, highlighting the complexity of MP pollution dynamics. Polymer-type distributions further elucidated the composition of MPs, with notable contributions from polyethylene terephthalate, rayon, polyester, and polyvinyl chloride. PCA analysis revealed significant shifts in particle size and shape distribution between pre-monsoon and post-monsoon periods in both water and sediment samples, with post-monsoon samples showing an increase in larger particles and filaments. These changes highlighted key factors driving the variance in microplastic contamination across different sites. The prevalence of these polymers features diverse sources of MP pollution, including textiles, packaging materials, and industrial waste. Ongoing monitoring and research are crucial to understanding its sources, distribution, and impact on river ecosystems, essential for protecting aquatic biodiversity and human health.

Disentangled Dynamic Graph Attention Network for Out-of-distribution Sequential Recommendation

Sequential recommendation, leveraging user-item interaction histories to provide personalized and timely suggestions, has drawn significant research interest recently. With the power of exploiting spatio-temporal dynamics, dynamic graph neural networks (DyGNNs) show great potential in sequential recommendation by modeling the dynamic relationship between users and items. However, spatio-temporal distribution shifts naturally exist in out-of-distribution sequential recommendation, where both user-item relationships and temporal sequences demonstrate pattern shifts. The out-of-distribution scenarios may lead to the failure of existing DyGNNs in handling spatio-temporal distribution shifts in sequential recommendation, given that the patterns they exploit tend to be variant w.r.t labels under distribution shifts. In this paper, we propose D isentangled I ntervention-based D ynamic graph A ttention networks with I nvariance Promotion ( I-DIDA ) to handle spatio-temporal distribution shifts in sequential recommendation by discovering and utilizing invariant patterns, ie , structures and features whose predictive abilities are stable across distribution shifts. Specifically, we first propose a disentangled spatio-temporal attention network to capture the variant and invariant patterns. By utilizing the disentangled patterns, we design a spatio-temporal intervention mechanism to create multiple interventional distributions and an environment inference module to infer the latent spatio-temporal environments, and minimize the invariance loss to leverage the invariant patterns with stable predictive abilities under distribution shifts. Extensive experiments demonstrate the superiority of our method over state-of-the-art sequential recommendation baselines under distribution shifts.

Wastewater Surveillance of SARS-CoV-2 in Slovenia: Key Public Health Tool in Endemic Time of COVID-19

With the reclassification of COVID-19 as an endemic disease and the relaxation of measures, Slovenia needed a complementary system for monitoring SARS-CoV-2 infections. This article provides an overview of the epidemiological situation of SARS-CoV-2 in Slovenia using a wastewater surveillance system, demonstrating its usefulness as a complementary tool in epidemiological surveillance. This study found that estimated SARS-CoV-2 infections in Slovenia peaked in September 2022 and showed a declining trend with subsequent lower peaks in March–April and December 2023, mirroring the trends observed from clinical data. Based on both surveillance systems, the most prevalent variant in 2022 was BA.5. By 2023, BQ.1 and other Omicron variants increased in prevalence. By the end of 2023, XBB sublineages and the BA.2.86 variant had become predominant, demonstrating consistent dynamic shifts in variant distribution across both monitoring methods. This study found that wastewater surveillance at wastewater treatment plants in Slovenia effectively tracked SARS-CoV-2 infection trends, showing a moderate to strong correlation with clinical data and providing early indications of changes in infection trends and variant emergence. Despite limitations during periods of low virus concentration, the system proved significant in providing early warnings of infection trends and variant emergence, thus enhancing public health response capabilities.

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

Temperature-enhanced effects of iron on Southern Ocean phytoplankton

Abstract. Iron (Fe) is a key limiting nutrient for Southern Ocean phytoplankton. Input of Fe into the Southern Ocean is projected to change due to global warming, yet the combined effects of a concurrent increase in temperature with dissolved Fe (dFe) addition on phytoplankton growth and community composition have not been extensively studied. To improve our understanding of how Antarctic phytoplankton communities respond to Fe and enhanced temperature, we performed four full factorial onboard bioassays under trace-metal-clean conditions with phytoplankton communities from different regions of the Weddell Sea and the Amundsen Sea in the Southern Ocean. Treatments consisted of 2 nM Fe addition with 2 °C warming (TF), Fe addition at in situ temperature (F) +2 °C warming with no Fe addition (T) and a control at in situ temperature with no Fe addition (control, C). Temperature had a limited effect by itself but boosted the positive response of the phytoplankton to Fe addition. Photosynthetic efficiency, phytoplankton abundances and chlorophyll a concentrations typically increased (significantly) with Fe addition (F and/or TF treatment), and the phytoplankton community generally shifted from haptophytes to diatoms upon Fe addition. The < 20 µm phytoplankton fraction displayed population-specific growth responses, resulting in a pronounced shift in community composition and size distribution (mainly towards larger-sized phytoplankton) for the F and TF treatments. Such a distinct enhanced impact of dFe supply with warming on Antarctic phytoplankton size, growth and composition will likely affect trophic transfer efficiency and ecosystem structure, with potential significance for the biological carbon pump.

Physiological and morphological traits affect contemporary range expansion and implications for species distribution modelling in an amphibian species.

Species range shifts due to climate alterations have been increasingly well-documented. Although amphibians are one of the most sensitive groups of animals to environmental perturbations due to climate change, almost no studies have offered evidence of poleward distribution shifts in this taxon in response to climate warming. Range shifts would be facilitated by variation in traits associated with the ability of species to persist and/or shift their range in the face of climate change, but the extent and consequences of intraspecific variation in these traits is unclear. We studied the role of intraspecific variation in the ongoing range shift of green treefrogs (Hyla cinerea) in response to climate change. We explored factors that are often associated with range shifts to test the hypothesis that there are differences in these traits between recently range-expanded and nearby historical populations. We then tested the consequences of intraspecific variation for modelling climate-induced range shifts by comparing species distribution models (SDMs) that used as input either data from the entire species range or separate inputs from 'subpopulations' corresponding to the historical range or the recently expanded range. We expected that building a separate SDM for each population would more accurately characterize the species range if historical and expanded populations differed in traits related to their response to climate. We found that critical thermal minimum decreased and thermal breadth increased with latitude, but the effect of latitude was significantly stronger for expanded populations compared to historical populations. Additionally, we found that individuals from expanded populations had longer leg lengths when compared to their historical counterparts. Finally, we found higher model accuracy for one of the population-level SDMs than the species-level SDM. Our results suggest that thermal tolerance and dispersal morphologies are associated with amphibian distributional shifts as these characteristics appear to facilitate rapid range expansion of a native anuran. Additionally, our modelling results emphasize that SDM accuracy could be improved by dividing a species range to consider potential differences in traits associated with climate responses. Future research should identify the mechanisms underlying intraspecific variation along climate gradients to continue improving SDM prediction of range shifts under climate change.

A review of climate change impacts on migration patterns of marine vertebrates in Arctic and Subarctic ecosystems

Climate change is impacting marine ecosystems throughout the circumpolar Arctic, altering seasonal habitats and the food bases for fishes, seabirds, and marine mammals. Arctic and Subarctic regions provide resources for resident species and for species that migrate to the north from more southerly regions. Changes in northerly latitudes thus impact endemic as well as non-endemic animals. Herein, we review what is known about climate-driven changes in the migration patterns of Arctic and Subarctic marine vertebrates, including: 1) Arctic residents with seasonal movements – those fishes, seabirds, and marine mammals that complete their entire life cycle within the Arctic but exhibit seasonal movements; 2) Breeding migrants – many seabirds enter the Arctic to breed and subsequently migrate south in the fall; and 3) Summer visitors for feeding – certain species of boreal fishes, seabirds and marine mammals arrive during the northern summer to feed on abundant prey though they breed elsewhere. Migratory movements are often driven by the timing and extent of sea ice, which defines suitable habitat for some animals and limits access to open water and prey for others. Longer open-water seasons, warmer ocean temperatures, and stronger winds have resulted in earlier production blooms in spring and often, extended open-ocean plankton blooms into late summer, resulting in altered prey types and distributions. A common thread among taxa is that shifts in distribution and timing of migrating animals indicate they are traveling farther north, or shifting longitudinally, and migrations are occurring over longer seasonal time frames. Species performing multiple lifetime migrations or long-distance migrants may need to adjust migration timing or routing iteratively to match changes in marine productivity. Altered animal distributions or phenology, and reduced sea ice, affects access to animals that are critical nutritional, economical, and cultural components of Indigenous people’s lives in the Arctic. Ongoing changes challenge the resilience and adaptability of Arctic people and ecosystems, and will require adaptive research and management approaches.

Gastrointestinal morphology is an effective functional dietary proxy that predicts small mammal community structure.

The availability and quality of food resources can alter the intensity of competition and predation pressure within communities. Understanding species capacity to respond to global change-driven shifts in resource distribution is therefore crucial for biodiversity conservation. Small mammal communities are often structured by competition for food resources, but understanding and monitoring these processes are currently hindered by lack of functional dietary trait information in these hard-to-sample systems. In this study, we collected a comprehensive suite of gastrointestinal (GI) measurements from 26 small mammal species (including some never reported), compared them with more traditional craniodental traits in predicting dietary guild, and used them in a novel way to understand how diet structures 22 small mammal communities across the Appalachian Mountains of eastern North America. As predicted, we found GI traits to be effective dietary trait proxies; they were equally or more accurate than craniodental proportions in predicting the dietary guild of individual species. Furthermore, at the community level, we found that both the mean and functional dispersion of GI length were positively correlated with latitude and measures of temperature seasonality. Our results indicate that small mammal communities in more seasonal environments are filtered to include species with longer GI tracts (on average) as well as those that can partition food resources more finely, as expected based on the lower productivity of these regions. Conversely, communities in less seasonal environments display functional redundancy from the addition of species with short to intermediate GI lengths. Proportions of the GI tract represent novel dietary traits that can illuminate community assembly processes across regional environmental gradients and in the face of changing timing and availability of resources.