Symbiont community diversity is more variable in corals that respond poorly to stress

Coral reefs are declining globally as climate change and local water quality press environmental conditions beyond the physiological tolerances of holobionts—the collective of the host and its microbial symbionts. To assess the relationship between symbiont composition and holobiont stress tolerance, community diversity metrics were quantified for dinoflagellate endosymbionts (Family: Symbiodiniaceae) from eight Acropora millepora genets that thrived under or responded poorly to various stressors. These eight selected genets represent the upper and lower tails of the response distribution of 40 coral genets that were exposed to four stress treatments (and control conditions) in a 10‐day experiment. Specifically, four ‘best performer’ coral genets were analyzed at the end of the experiment because they survived high temperature, high pCO₂, bacterial exposure, or combined stressors, whereas four ‘worst performer’ genets were characterized because they experienced substantial mortality under these stressors. At the end of the experiment, seven of eight coral genets mainly hosted Cladocopium symbionts, whereas the eighth genet was dominated by both Cladocopium and Durusdinium symbionts. Symbiodiniaceae alpha and beta diversity were higher in worst performing genets than in best performing genets. Symbiont communities in worst performers also differed more after stress exposure relative to their controls (based on normalized proportional differences in beta diversity), than did best performers. A generalized joint attribute model estimated the influence of host genet and treatment on Symbiodiniaceae community composition and identified strong associations among particular symbionts and host genet performance, as well as weaker associations with treatment. Although dominant symbiont physiology and function contribute to host performance, these findings emphasize the importance of symbiont community diversity and stochasticity as components of host performance. Our findings also suggest that symbiont community diversity metrics may function as indicators of resilience and have potential applications in diverse disciplines from climate change adaptation to agriculture and medicine.     

Spatial synchrony in the response of a long range migratory species (Salmo salar) to climate change in the North Atlantic Ocean

A major challenge in understanding the response of populations to climate change is to separate the effects of local drivers acting independently on specific populations, from the effects of global drivers that impact multiple populations simultaneously and thereby synchronize their dynamics. We investigated the environmental drivers and the demographic mechanisms of the widespread decline in marine survival rates of Atlantic salmon (Salmo salar) over the last four decades. We developed a hierarchical Bayesian life cycle model to quantify the spatial synchrony in the marine survival of 13 large groups of populations (called stock units, SU) from two continental stock groups (CSG) in North America (NA) and Southern Europe (SE) over the period 1971–2014. We found strong coherence in the temporal variation in postsmolt marine survival among the 13 SU of NA and SE. A common North Atlantic trend explains 37% of the temporal variability of the survivals for the 13 SU and declines by a factor of 1.8 over the 1971–2014 time series. Synchrony in survival trends is stronger between SU within each CSG. The common trends at the scale of NA and SE capture 60% and 42% of the total variance of temporal variations, respectively. Temporal variations of the postsmolt survival are best explained by the temporal variations of sea surface temperature (SST, negative correlation) and net primary production indices (PP, positive correlation) encountered by salmon in common domains during their marine migration. Specifically, in the Labrador Sea/Grand Banks for populations from NA, 26% and 24% of variance is captured by SST and PP, respectively and in the Norwegian Sea for populations from SE, 21% and 12% of variance is captured by SST and PP, respectively. The findings support the hypothesis of a response of salmon populations to large climate‐induced changes in the North Atlantic simultaneously impacting populations from distant continental habitats.     

Climate change promotes transitions to tall evergreen vegetation in tropical Asia

Vegetation in tropical Asia is highly diverse due to large environmental gradients and heterogeneity of landscapes. This biodiversity is threatened by intense land use and climate change. However, despite the rich biodiversity and the dense human population, tropical Asia is often underrepresented in global biodiversity assessments. Understanding how climate change influences the remaining areas of natural vegetation is therefore highly important for conservation planning. Here, we used the adaptive Dynamic Global Vegetation Model version 2 (aDGVM2) to simulate impacts of climate change and elevated CO₂ on vegetation formations in tropical Asia for an ensemble of climate change scenarios. We used climate forcing from five different climate models for representative concentration pathways RCP4.5 and RCP8.5. We found that vegetation in tropical Asia will remain a carbon sink until 2099, and that vegetation biomass increases of up to 28% by 2099 are associated with transitions from small to tall woody vegetation and from deciduous to evergreen vegetation. Patterns of phenology were less responsive to climate change and elevated CO₂ than biomes and biomass, indicating that the selection of variables and methods used to detect vegetation changes is crucial. Model simulations revealed substantial variation within the ensemble, both in biomass increases and in distributions of different biome types. Our results have important implications for management policy, because they suggest that large ensembles of climate models and scenarios are required to assess a wide range of potential future trajectories of vegetation change and to develop robust management plans. Furthermore, our results highlight open ecosystems with low tree cover as most threatened by climate change, indicating potential conflicts of interest between biodiversity conservation in open ecosystems and active afforestation to enhance carbon sequestration.     

The use of three-dimensional model construction of virtual technology in orthopedic treatment

ObjectiveThe objective of this study is to explore the construction of a digital three-dimensional model of virtual technology that plays an auxiliary role in orthopedic treatment.MethodsThree fracture patients were selected, with no abnormality was observed in bone examination, no musculoskeletal disease in the past; and spiral CT scan of the spine and pelvis, upper limbs, and lower limbs was performed. The virtual technology was used to build a digital 3D model, mainly using the editing software Mimics10.0 software. In addition, the virtual three-dimensional model was verified by virtual surgery, data storage security, work efficiency of the model, model validity, three-dimensional characteristics of the model, the interaction mode of the model, and the data accuracy of the model were studied.ResultsThe digital 3D model was successfully established by Mimics10.0 software. The data fitting efficiency was very high. The data storage security of the 3D model was greatly improved compared with the 2D model, and the work efficiency was improved by at least 50%. There was also a significant change in the accuracy and interaction of data acquisition. Therefore, the detection of digital 3D model work through virtual surgery simulation fully demonstrated the positive auxiliary role of 3D model in orthopedic treatment.ConclusionThe digital 3D model based on Mimics10.0 software is efficient and accurate in obtaining data. It is very effective for subsequent adjuvant therapy in the field of orthopedics, reducing the probability of misdiagnosis by doctors, saving time and improving efficiency, reducing patient's physical pain and unnecessary economic expenses.     

The efficacy of 18F-FDG PET/CT-based diagnostic model in the diagnosis of colorectal cancer regional lymph node metastasis

In order to assess the efficacy of ¹⁸F-FDG PET/CT-based diagnostic model in diagnosing colorectal cancer (CRC) lymph node metastasis (LNM), the ¹⁸F-FDG PET/CT medical records of CRC patients were acquired, and the CRC regional LNM diagnostic model was constructed through the combination of image and grain factors of ¹⁸F-FDG PET/CT. The specific analysis methods include univariate analysis, multivariate analysis, ROC curve analysis, and statistical analysis. The research results showed statistical differences in TNM staging, intestinal obstructions, tumor infiltration, regional lymph node (LN) SUVmax, regional LN minimum dimension, and remote metastasis between the CRC patients in the LNM positive group and the LNM negative group. Through the comparisons between the diagnostic model proposed in the research and other diagnostic methods, it was found that the AUC (95%CI) and sensitivity of the proposed diagnostic model were the highest, the comprehensive diagnostic efficacy of the diagnostic model was optimal. Therefore, it was concluded that the diagnostic model was of significant application values, which provided the basis for subsequent clinical diagnosis of CRC.     

Nonlinearity in interspecific interactions in response to climate change: Cod and haddock as an example

Climate change has profound ecological effects, yet our understanding of how trophic interactions among species are affected by climate change is still patchy. The sympatric Atlantic haddock and cod are co‐occurring across the North Atlantic. They compete for food at younger stages and thereafter the former is preyed by the latter. Climate change might affect the interaction and coexistence of these two species. Particularly, the increase in sea temperature (ST) has been shown to affect distribution, population growth and trophic interactions in marine systems. We used 33‐year long time series of haddock and cod abundances estimates from two data sources (acoustic and trawl survey) to analyse the dynamic effect of climate on the coexistence of these two sympatric species in the Arcto‐Boreal Barents Sea. Using a Bayesian state‐space threshold model, we demonstrated that long‐term climate variation, as expressed by changes of ST, affected species demography through different influences on density‐independent processes. The interaction between cod and haddock has shifted in the last two decades due to an increase in ST, altering the equilibrium abundances and the dynamics of the system. During warm years (ST over ca. 4°C), the increase in the cod abundance negatively affected haddock abundance while it did not during cold years. This change in interactions therefore changed the equilibrium population size with a higher population size during warm years. Our analyses show that long‐term climate change in the Arcto‐Boreal system can generate differences in the equilibrium conditions of species assemblages.     

Temperature and precipitation at migratory grounds influence demographic trends of an Arctic‐breeding bird

Anthropogenic climate disruption, including temperature and precipitation regime shifts, has been linked to animal population declines since the mid‐20th century. However, some species, such as Arctic‐breeding geese, have thrived during this period. An increased understanding of how climate disruption might link to demographic rates in thriving species is an important perspective in quantifying the impact of anthropogenic climate disruption on the global state of nature. The Greenland barnacle goose (Branta leucopsis) population has increased tenfold in abundance since the mid‐20th century. A concurrent weather regime shift towards warmer, wetter conditions occurred throughout its range in Greenland (breeding), Ireland and Scotland (wintering) and Iceland (spring and autumn staging). The aim of this study was to determine the relationship between weather and demographic rates of Greenland barnacle geese to discern the role of climate shifts in the population trend. We quantified the relationship between temperature and precipitation and Greenland barnacle goose survival and productivity over a 50 year period from 1968 to 2018. We detected significant positive relationships between warmer, wetter conditions on the Icelandic spring staging grounds and survival. We also detected contrasting relationships between warmer, wetter conditions during autumn staging and survival and productivity, with warm, dry conditions being the most favourable for productivity. Survival increased in the latter part of the study period, supporting the possibility that spring weather regime shifts contributed to the increasing population trend. This may be related to improved forage resources, as warming air temperatures have been shown to improve survival rates in several other Arctic and northern terrestrial herbivorous species through indirect bottom‐up effects on forage availability.     

Heterogeneity–diversity relationships in sessile organisms: a unified framework

The hypothesis that environmental heterogeneity promotes species richness by increasing opportunities for niche partitioning is a fundamental paradigm in ecology. However, recent studies suggest that heterogeneity–diversity relationships (HDR) are more complex than expected from this niche‐based perspective, and often show a decrease in richness at high levels of heterogeneity. These findings have motivated ecologists to propose new mechanisms that may explain such deviations. Here we provide an overview of currently recognised mechanisms affecting the shape of HDRs and present a conceptual model that integrates all previously proposed mechanisms within a unified framework. We also translate the proposed framework into an explicit community dynamic model and use the model as a tool for generating testable predictions concerning how landscape properties interact with species traits in determining the shape of HDRs. Our main finding is that, despite the enormous complexity of such interactions, the predicted HDRs are rather simple, ranging from positive to unimodal patterns in a highly consistent and predictable manner.     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.