Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics

Using comparative extinction risk analysis to prioritize the IUCN Red List reassessments of amphibians

Assessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land-use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species' range size and spatial configuration of the range, biological traits, climate change, and land-use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models' uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up-to-date IUCN RL.

Machine learning provides insights for spatially explicit pest management strategies by integrating information on population connectivity and habitat use in a key agricultural pest

BACKGROUND

Insect pests have garnered increasing interest because of anthropogenic global change, and their sustainable management requires knowledge of population habitat use and spread patterns. To enhance this knowledge for the prevalent tea pest Empoasca onukii, we utilized a random forest algorithm and a bivariate map to develop and integrate models of its habitat suitability and genetic connectivity across China.

RESULTS

Our modeling revealed heterogeneous spatial patterns in suitability and connectivity despite the common key environmental predictor of isothermality. Analyses indicated that tea cultivation in areas surrounding the Tibetan Plateau and the southern tip of China may be at low risk of population outbreaks because of their predicted low suitability and connectivity. However, regions along the middle and lower reaches of the Yangtze River should consider the high abundance and high recolonization potential of E. onukii, and thus the importance of control measures. Our results also emphasized the need to prevent dispersal from outside regions in the areas north of the Yangtze River and highlighted the effectiveness of internal management efforts in southwestern China and along the southeastern coast. Further projections under future conditions suggested the potential for increased abundance and spread in regions north of the Yangtze River and the southern tip of China, and indicated the importance of long-term monitoring efforts in these areas.

CONCLUSION

These findings highlighted the significance of combining information on habitat use and spread patterns for spatially explicit pest management planning. In addition, the approaches we used have potential applications in the management of other pest systems and the conservation of endangered biological resources. © 2024 Society of Chemical Industry.

Physical habitat modeling for river macroinvertebrate communities

Habitat models rarely consider macroinvertebrate communities as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the whole community. This research aimed at providing an approach to model the habitat of the macroinvertebrate communities. The study was carried out in three rivers, located in Italy and characterized by a braiding morphology, gravel riverbeds, and low flows during the summer period. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the mesohabitat scale. Using different datasets gathered from field data collection and 2D hydrodynamic simulations, the model was calibrated in the Trebbia River (2019 field campaign) and validated in the Trebbia, Taro, and Enza rivers (2020 field campaign). The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R2 coefficient (R2cv) of the training dataset was 0.71, whereas the R2 coefficient (R2test) for the validation dataset was 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and river morphological modifications. Lastly, the proposed approach allowed to extend the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to ecological flows design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Beyond overlap: Considering habitat preference and fitness outcomes in the umbrella species concept

Umbrella species and other surrogate-species approaches to conservation provide an appealing framework to extend the reach of conservation efforts beyond single species. For the umbrella species concept to be effective, populations of multiple species of concern must persist in areas protected on behalf of the umbrella species. Most assessments of the concept, however, focus exclusively on geographic overlap among umbrella and background species, and not measures that affect population persistence (e.g., habitat quality or fitness). We quantified the congruence between the habitat preferences and nesting success of a high-profile umbrella species (greater sage-grouse, Centrocercus urophasianus, hereafter "sage-grouse"), and three sympatric species of declining songbirds (Brewer's sparrow Spizella breweri, sage thasher Oreoscoptes montanus, and vesper sparrow Pooecetes gramineus) in central Wyoming, USA during 2012 - 2013. We used machine-learning methods to create data-driven predictions of sage-grouse nest-site selection and nest survival probabilities by modeling field-collected sage-grouse data relative to habitat attributes. We then used field-collected songbird data to assess whether high-quality sites for songbirds aligned with those of sage-grouse. Nest sites selected by songbirds did not coincide with sage-grouse nesting preferences, with the exception that Brewer's sparrows preferred similar nest sites to sage-grouse in 2012. Moreover, the areas that produced higher rates of songbird nest survival were unrelated to those for sage-grouse. Our findings suggest that management actions at local scales that prioritize sage-grouse nesting habitat will not necessarily enhance the reproductive success of sagebrush-associated songbirds. Measures implemented to conserve sage-grouse and other purported umbrella species at broad spatial scales likely overlap the distribution of many species, however, broad-scale overlap may not translate to fine-scale conservation benefit beyond the umbrella species itself. The maintenance of microhabitat heterogeneity important for a diversity of species of concern will be critical for a more-holistic application of the umbrella species concept.

Gut metabolome and microbiota signatures predict response to treatment with exclusive enteral nutrition in a prospective study in children with active Crohn's disease

BACKGROUND

Predicting response to exclusive enteral nutrition (EEN) in active Crohn's disease (CD) could lead to therapy personalization and pretreatment optimization.

OBJECTIVES

This study aimed to explore the ability of pretreatment parameters to predict fecal calprotectin (FCal) levels at EEN completion in a prospective study in children with CD.

METHODS

In children with active CD, clinical parameters, dietary intake, cytokines, inflammation-related blood proteomics, and diet-related metabolites, metabolomics and microbiota in feces, were measured before initiation of 8 wk of EEN. Prediction of FCal levels at EEN completion was performed using machine learning. Data are presented with medians (IQR).

RESULTS

Of 37 patients recruited, 15 responded (FCal < 250 μg/g) to EEN (responders) and 22 did not (nonresponders). Clinical and immunological parameters were not associated with response to EEN. Responders had lesser (μmol/g) butyrate [responders: 13.2 (8.63-18.4) compared with nonresponders: 22.3 (12.0-32.0); P = 0.03], acetate [responders: 49.9 (46.4-68.4) compared with nonresponders: 70.4 (57.0-95.5); P = 0.027], phenylacetate [responders: 0.175 (0.013-0.611) compared with nonresponders: 0.943 (0.438-1.35); P = 0.021], and a higher microbiota richness [315 (269-347) compared with nonresponders: 243 (205-297); P = 0.015] in feces than nonresponders. Responders consumed (portions/1000 kcal/d) more confectionery products [responders: 0.55 (0.38-0.72) compared with nonresponders: 0.19 (0.01-0.38); P = 0.045]. A multicomponent model using fecal parameters, dietary data, and clinical and immunological parameters predicted response to EEN with 78% accuracy (sensitivity: 80%; specificity: 77%; positive predictive value: 71%; negative predictive value: 85%). Higher taxon abundance from Ruminococcaceae, Lachnospiraceae, and Bacteroides and phenylacetate, butyrate, and acetate were the most influential variables in predicting lack of response to EEN.

CONCLUSIONS

We identify microbial signals and diet-related metabolites in feces, which could comprise targets for pretreatment optimization and personalized nutritional therapy in pediatric CD.

Limited effects of population age on the genetic structure of spatially isolated forest herb populations in temperate Europe

Due to multiple land-cover changes, forest herb populations residing in forest patches embedded in agricultural landscapes display different ages and, thus, experience differences in genetic exchange, mutation accumulation and genetic drift. The extent of divergence in present-day population genetic structure among these populations of different ages remains unclear, considering their diverse breeding systems and associated pollinators. Answering this question is essential to understand these species' persistence, maintenance of evolutionary potential and adaptability to changing environments. We applied a multi-landscape setup to compare the genetic structure of forest herb populations across forest patches of different ages (18-338 years). We studied the impact on three common slow-colonizer herb species with distinct breeding systems and associated pollinators: Polygonatum multiflorum (outcrossing, long-distance pollinators), Anemone nemorosa (outcrossing, short-distance pollinators) and Oxalis acetosella (mixed breeding). We aimed to assess if in general older populations displayed higher genetic diversity and lower differentiation than younger ones. We also anticipated that P. multiflorum would show the smallest while O. acetosella the largest difference, between old and young populations. We found that older populations had a higher observed heterozygosity (H o) but a similar level of allelic richness (A r) and expected heterozygosity (H e) as younger populations, except for A. nemorosa, which exhibited higher A r and H e in younger populations. As populations aged, their pairwise genetic differentiation measured by D PS decreased independent of species identity while the other two genetic differentiation measures showed either comparable levels between old and young populations (G" ST) or inconsistency among three species (cGD). The age difference of the two populations did not explain their genetic differentiation. Synthesis: We found restricted evidence that forest herb populations with different ages differ in their genetic structure, indicating that populations of different ages can reach a similar genetic structure within decades and thus persist in the long term after habitat disturbance. Despite their distinct breeding systems and associated pollinators, the three studied species exhibited partly similar genetic patterns, suggesting that their common characteristics, such as being slow colonizers or their ability to propagate vegetatively, are important in determining their long-term response to land-cover change.

Species-specific coral microbiome assemblages support host bleaching resistance during an extreme marine heatwave

Scleractinian assemblages are threatened by marine heat waves with coral survivorship depending on host genetics and microbiome composition. We documented an extreme marine heat wave in Fiji and the response of corals in two thermally stressed reef flats. Through high-throughput amplicon sequencing of 16S and ITS rDNA phylogenetic markers, we assessed coral microbiomes (Symbiodiniaceae, prokaryotes, fungi, and Apicomplexa) of paired bleached and unbleached colonies of four common coral species representative of dominant genera in the South Pacific. While all coral species exhibited one or more pathways to bleaching resistance, harboring assemblages composed primarily of thermally tolerant photosymbionts did not always result in host bleaching resistance. Montipora and Pocillopora species, which associate with diverse Symbiodiniaceae and vertically transmit their photosymbionts, fared better than Acropora, which acquire their photosymbionts from the environment, and Porites, which associate with a narrow photosymbiont assemblage. Prokaryotic and fungal beta diversity did not differ between bleached and unbleached conspecifics, however, the relative abundance of the fungus Malassezia globosa was significantly greater in unbleached colonies of Montipora digitata. Each coral species harbored distinct assemblages of Symbiodiniaceae, prokaryotes, and Apicomplexa, but not fungi, reiterating the importance of host genetics in structuring components of its microbiome. Terrestrial fungal and prokaryotic taxa were detected at low abundance across coral microbiomes, indicating that allochthonous microbial inputs occur, but that coral microbiomes remain dominated by marine microbial taxa. Our study offers valuable insights into the microbiome assemblages associated with coral tolerance to extreme water temperatures.

Change in body size in a rapidly warming marine ecosystem: Consequences of tropicalization

Climate change is profoundly affecting the physical environment and biota of the Northeast U.S. Continental Shelf ecosystem. To understand adaptations to climate change, in particular warming temperatures, we used bottom trawl survey data to describe the size of individual fish and macroinvertebrates. Using species distribution models to estimate abundance and biomass, we determined body size in weight for all modeled species. We demonstrate a tendency for increased abundance and biomass and a concomitant decline in body size over time. An analysis of length frequency data supports this assertion. There was no trend in the combined anthropogenic removals from the ecosystem, i.e. catches, suggesting a limited role of fisheries in influencing these changes. The changes in the fish and macroinvertebrate communities are consistent with the hypothesis of a tropicalization of this ecosystem, where the ecosystem experiences a change in diversity, abundance, biomass, and the size of individuals consistent with lower latitudes. The changes in how productivity is expressed in the ecosystem factors into how human populations relate to it; in a practical sense, change in body size will likely influence the strategies and efficiencies of harvest procedures and the industries built to support them.

Conservation genomics of an endangered montane amphibian reveals low population structure, low genomic diversity and selection pressure from disease

Wildlife diseases are a major global threat to biodiversity. Boreal toads (Anaxyrus [Bufo] boreas) are a state-endangered species in the southern Rocky Mountains of Colorado and New Mexico, and a species of concern in Wyoming, largely due to lethal skin infections caused by the amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd). We performed conservation and landscape genomic analyses using single nucleotide polymorphisms from double-digest, restriction site-associated DNA sequencing in combination with the development of the first boreal toad (and first North American toad) reference genome to investigate population structure, genomic diversity, landscape connectivity and adaptive divergence. Genomic diversity (π = 0.00034-0.00040) and effective population sizes (Ne = 8.9-38.4) were low, likely due to post-Pleistocene founder effects and Bd-related population crashes over the last three decades. Population structure was also low, likely due to formerly high connectivity among a higher density of geographically proximate populations. Boreal toad gene flow was facilitated by low precipitation, cold minimum temperatures, less tree canopy, low heat load and less urbanization. We found >8X more putatively adaptive loci related to Bd intensity than to all other environmental factors combined, and evidence for genes under selection related to immune response, heart development and regulation and skin function. These data suggest boreal toads in habitats with Bd have experienced stronger selection pressure from disease than from other, broad-scale environmental variations. These findings can be used by managers to conserve and recover the species through actions including reintroduction and supplementation of populations that have declined due to Bd.

Free-ranging dogs are seriously threatening Himalayan environment: delineating the high-risk areas for curbing free-ranging dog infestation in the Trans-Himalayan region

It is becoming more widely recognised that free-ranging dogs, which have a nearly global distribution, threatening native wildlife. Their increasing population and spread to new areas is of growing concern for the long-term viability of wildlife species. Hence, it is imperative to understand the factors responsible for their infestation and map areas where native species are most vulnerable. Using the random forests algorithm, we modelled the free-ranging dog infestation in the Trans-Himalayan region to pinpoint the high-risk areas where free-ranging dogs are threatening the native wildlife species. We found that the likelihood of free-ranging dog occurrence is most in valley regions and up to 4000 m, often in proximity to roads. Our results also indicated that free-ranging dog prefers areas with wildlife near to protected areas. The predictor variables, such as potential evapotranspiration of the coldest quarter, distance to protected areas, elevation, distance to roads, and potential evapotranspiration of the driest quarter, significantly influence the distribution of the free-ranging dogs. We found that within the Ladakh region of the Trans-Himalayan area, the high-risk zones for free-ranging dogs are located in and around Hemis National Park, Karakoram Wildlife Sanctuary, and Changthang Wildlife Sanctuary. While, in the Lahaul and Spiti region the high-risk areas encompass Pin Valley National Park, Inderkilla National Park, Khirganga National Park, Kugti Wildlife Sanctuary, and several other protected areas. We identified the potentially high-risk areas for implementing strategies to mitigate the possible impact of free-ranging dogs on native wildlife of the Himalayas. Hence, the identified high priority areas can be used for implementing actions for controlling the population growth and further preventing the infestation of the free-ranging dogs into the new areas.

Impact of HIV on the Oral Microbiome of Children Living in Sub-Saharan Africa, Determined by Using an rpoC Gene Fragment Metataxonomic Approach

Children living with HIV have a higher prevalence of oral diseases, including caries, but the mechanisms underlying this higher prevalence are not well understood. Here, we test the hypothesis that HIV infection is associated with a more cariogenic oral microbiome, characterized by an increase in bacteria involved in the pathogenesis of caries. We present data generated from supragingival plaques collected from 484 children representing three exposure groups: (i) children living with HIV (HI), (ii) children who were perinatally exposed but uninfected (HEU), and (iii) unexposed and therefore uninfected children (HUU). We found that the microbiome of HI children is distinct from those of HEU and HUU children and that this distinction is more pronounced in diseased teeth than healthy teeth, suggesting that the impact of HIV is more severe as caries progresses. Moreover, we report both an increase in bacterial diversity and a decrease in community similarity in our older HI cohort compared to our younger HI cohort, which may in part be a prolonged effect of HIV and/or its treatment. Finally, while Streptococcus mutans is often a dominant species in late-stage caries, it tended to be found at lower frequency in our HI cohort than in other groups. Our results highlight the taxonomic diversity of the supragingival plaque microbiome and suggest that broad and increasingly individualistic ecological shifts are responsible for the pathogenesis of caries in children living with HIV, coupled with a diverse and possibly severe impact on known cariogenic taxa that potentially exacerbates caries. IMPORTANCE Since its recognition as a global epidemic in the early 1980s, approximately 84.2 million people have been diagnosed with HIV and 40.1 million people have died from AIDS-related illnesses. The development and increased global availability of antiretroviral treatment (ART) regimens have dramatically reduced the mortality rate of HIV and AIDS, yet approximately 1.5 million new infections were reported in 2021, 51% of which are in sub-Saharan Africa. People living with HIV have a higher prevalence of caries and other chronic oral diseases, the mechanisms of which are not well understood. Here, we used a novel genetic approach to characterize the supragingival plaque microbiome of children living with HIV and compared it to the microbiomes of uninfected and perinatally exposed children to better understand the role of oral bacteria in the etiology of tooth decay in the context of HIV exposure and infection.

Coursing the mottled mosaic: Generalist predators track pulses in availability of neonatal ungulates

The density and distribution of resources shape animal movement and behavior and have direct implications for population dynamics. Resource availability often is "pulsed" in space and time, and individuals should cue in on resource pulses when the energetic gain of doing so exceeds that of stable resources. Birth pulses of prey represent a profitable but ephemeral resource and should thereby result in shifting functional responses by predators. We evaluated movements and resource selection of coyotes (Canis latrans) across a gradient of reproductive stages ranging from late gestation to peak lactation of female mule deer (Odocoileus hemionus) in southwest Wyoming, USA, to test whether coyotes exhibited shifts in selection and movement behavior relative to the availability and vulnerability of neonatal mule deer. We expected coyotes to track pulses in availability of neonatal mule deer, and such behavior would be represented by shifts in resource selection and search behavior of coyotes that would be strongest during peak parturition of mule deer. Coyotes selected areas of high relative probability of use by female mule deer and did so most strongly during peak parturition. Furthermore, searching behavior of coyotes intensified during pulses of availability of deer neonates. Our findings support the notion that coyotes exploit pulses of neonatal deer, presumably as an attempt to capitalize on a vulnerable, energy-rich resource. Our work quantifies the behavioral mechanisms by which coyotes consume ungulate neonates and provides one of the first examples of a mammalian predator-prey system centered on a pulsed resource.

Two machine learning approaches for predicting cyanobacteria abundance in aquaculture ponds

Cyanobacteria blooms in aquaculture ponds harm the harvesting of aquatic animals and threaten human health. Therefore, it is crucial to identify key drivers and develop methods to predict cyanobacteria blooms in aquaculture water management. In this study, we analyzed monitoring data from 331 aquaculture ponds in central China and developed two machine learning models - the least absolute shrinkage and selection operator (LASSO) regression model and the random forest (RF) model - to predict cyanobacterial abundance by identifying the key drivers. Simulation results demonstrated that both machine learning models are feasible for predicting cyanobacterial abundance in aquaculture ponds. The LASSO model (R² = 0.918, MSE = 0.354) outperformed the RF model (R² = 0.798, MSE = 0.875) in predicting cyanobacteria abundance. Farmers with well-equipped aquaculture ponds that have abundant water monitoring data can use the nine environmental variables identified by the LASSO model as an operational solution to accurately predict cyanobacteria abundance. For crude ponds with limited monitoring data, the three environmental variables identified by the RF model provide a convenient solution for useful cyanobacteria prediction. Our findings revealed that chemical oxygen demand (COD) and total organic carbon (TOC) were the two most important predictors in both models, indicating that organic carbon concentration had a close relationship with cyanobacteria growth and should be considered a key metric in water monitoring and pond management of these aquaculture ponds. We suggest that monitoring of organic carbon coupled with phosphorus reduction in feed usage can be an effective management approach for cyanobacteria prevention and to maintain a healthy ecological state in aquaculture ponds.

Single-cell analysis of human nasal mucosal IgE antibody secreting cells reveals a newly minted phenotype

Immunoglobulin (Ig) E is central to the pathogenesis of allergic conditions, including allergic fungal rhinosinusitis. However, little is known about IgE antibody secreting cells (ASCs). We performed single-cell RNA sequencing from cluster of differentiation (CD)19+ and CD19- ASCs of nasal polyps from patients with allergic fungal rhinosinusitis (n = 3). Nasal polyps were highly enriched in CD19+ ASCs. Class-switched IgG and IgA ASCs were dominant (95.8%), whereas IgE ASCs were rare (2%) and found only in the CD19+ compartment. Through Ig gene repertoire analysis, IgE ASCs shared clones with IgD-CD27- "double-negative" B cells, IgD+CD27+ unswitched memory B cells, and IgD-CD27+ switched memory B cells, suggesting ontogeny from both IgD+ and memory B cells. Transcriptionally, mucosal IgE ASCs upregulate pathways related to antigen presentation, chemotaxis, B cell receptor stimulation, and survival compared with non-IgE ASCs. Additionally, IgE ASCs have a higher expression of genes encoding lysosomal-associated protein transmembrane 5 (LAPTM5) and CD23, as well as upregulation of CD74 (receptor for macrophage inhibitory factor), store-operated Calcium entry-associated regulatory factor (SARAF), and B cell activating factor receptor (BAFFR), which resemble an early minted ASC phenotype. Overall, these findings reinforce the paradigm that human ex vivo mucosal IgE ASCs have a more immature plasma cell phenotype than other class-switched mucosal ASCs and suggest unique functional roles for mucosal IgE ASCs in concert with Ig secretion.

A big data-model integration approach for predicting epizootics and population recovery in a keystone species

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.

Predicting Environmental and Ecological Drivers of Human Population Structure

Landscape, climate, and culture can all structure human populations, but few existing methods are designed to simultaneously disentangle among a large number of variables in explaining genetic patterns. We developed a machine learning method for identifying the variables which best explain migration rates, as measured by the coalescent-based program MAPS that uses shared identical by descent tracts to infer spatial migration across a region of interest. We applied our method to 30 human populations in eastern Africa with high-density single nucleotide polymorphism array data. The remarkable diversity of ethnicities, languages, and environments in this region offers a unique opportunity to explore the variables that shape migration and genetic structure. We explored more than 20 spatial variables relating to landscape, climate, and presence of tsetse flies. The full model explained ∼40% of the variance in migration rate over the past 56 generations. Precipitation, minimum temperature of the coldest month, and elevation were the variables with the highest impact. Among the three groups of tsetse flies, the most impactful was fusca which transmits livestock trypanosomiasis. We also tested for adaptation to high elevation among Ethiopian populations. We did not identify well-known genes related to high elevation, but we did find signatures of positive selection related to metabolism and disease. We conclude that the environment has influenced the migration and adaptation of human populations in eastern Africa; the remaining variance in structure is likely due in part to cultural or other factors not captured in our model.

Sex-specific outbreeding advantages and sexual dimorphism in the seedlings of dioecious trees

PREMISE

Dioecious trees are important components of many forest ecosystems. Outbreeding advantage and sexual dimorphism are two major mechanisms that explain the persistence of dioecious plants; however, they have rarely been studied in dioecious trees.

METHODS

We investigated the influence of sex and genetic distance between parental trees (GDPT) on the growth and functional traits of multiple seedlings of a dioecious tree, Diospyros morrisiana.

RESULTS

We found significant positive relationships between GDPT and seedling sizes and tissue density. However, the positive outbreeding effects on seedling growth mainly manifested in female seedlings, but were not prominent in males. Among seedlings, the male ones generally had higher biomass and leaf area than female seedlings, but such differences diminished as GDPT increased.

CONCLUSIONS

Our research highlights that outbreeding advantage in plants can be sex-specific and that sexual dimorphism begins from the seedling stage of dioecious trees.

Estimating resistance surfaces using gradient forest and allelic frequencies

Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Connectivity methods that use link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the gradient forest approach to produce a resistance surface. Used in community ecology, gradient forest is an extension of random forest, and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resistance Gradient Forest (resGF) performance was compared to other published methods (maximum likelihood population effects model, random forest-based least-cost transect analysis and species distribution model). In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the gradient forest approach performed similarly to the other random forest-based approach using least-cost transect analysis but outperformed MLPE-based methods. Additionally, two worked examples are provided using two previously published data sets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and inform long-term biodiversity conservation strategies.

Landscape genetics of a sub-alpine toad: climate change predicted to induce upward range shifts via asymmetrical migration corridors

Climate change is expected to have a major hydrological impact on the core breeding habitat and migration corridors of many amphibians in the twenty-first century. The Yosemite toad (Anaxyrus canorus) is a species of meadow-specializing amphibian endemic to the high-elevation Sierra Nevada Mountains of California. Despite living entirely on federal lands, it has recently faced severe extirpations, yet our understanding of climatic influences on population connectivity is limited. In this study, we used a previously published double-digest RADseq dataset along with numerous remotely sensed habitat features in a landscape genetics framework to answer two primary questions in Yosemite National Park: (1) Which fine-scale climate, topographic, soil, and vegetation features most facilitate meadow connectivity? (2) How is climate change predicted to influence both the magnitude and net asymmetry of genetic migration? We developed an approach for simultaneously modeling multiple toad migration paths, akin to circuit theory, except raw environmental features can be separately considered. Our workflow identified the most likely migration corridors between meadows and used the unique cubist machine learning approach to fit and forecast environmental models of connectivity. We identified the permuted modeling importance of numerous snowpack-related features, such as runoff and groundwater recharge. Our results highlight the importance of considering phylogeographic structure, and asymmetrical migration in landscape genetics. We predict an upward elevational shift for this already high-elevation species, as measured by the net vector of anticipated genetic movement, and a north-eastward shift in species distribution via the network of genetic migration corridors across the park.

An ensemble learning approach to map the genetic connectivity of the parasitoid Stethynium empoasca (Hymenoptera: Mymaridae) and identify the key influencing environmental and landscape factors

The effect of landscape patterns and environmental factors on the population structure and genetic diversity of organisms is well-documented. However, this effect is still unclear in the case of Mymaridae parasitoids. Despite recent advances in machine learning methods for landscape genetics, ensemble learning still needs further investigation. Here, we evaluated the performance of different boosting algorithms and analyzed the effects of landscape and environmental factors on the genetic variations in the tea green leafhopper parasitoid Stethynium empoasca (Hymenoptera: Mymaridae). The S. empoasca populations showed a distinct pattern of isolation by distance. The minimum temperature of the coldest month, annual precipitation, the coverage of evergreen/deciduous needleleaf trees per 1 km2, and the minimum precipitation of the warmest quarter were identified as the dominant factors affecting the genetic divergence of S. empoasca populations. Notably, compared to previous machine learning studies, our model showed an unprecedented accuracy (r = 0.87) for the prediction of genetic differentiation. These findings not only demonstrated how the landscape shaped S. empoasca genetics but also provided an essential basis for developing conservation strategies for this biocontrol agent. In a broader sense, this study demonstrated the importance and efficiency of ensemble learning in landscape genetics.

Multitemporal lidar captures heterogeneity in fuel loads and consumption on the Kaibab Plateau

BACKGROUND

Characterization of physical fuel distributions across heterogeneous landscapes is needed to understand fire behavior, account for smoke emissions, and manage for ecosystem resilience. Remote sensing measurements at various scales inform fuel maps for improved fire and smoke models. Airborne lidar that directly senses variation in vegetation height and density has proven to be especially useful for landscape-scale fuel load and consumption mapping. Here we predicted field-observed fuel loads from airborne lidar and Landsat-derived fire history metrics with random forest (RF) modeling. RF models were then applied across multiple lidar acquisitions (years 2012, 2019, 2020) to create fuel maps across our study area on the Kaibab Plateau in northern Arizona, USA. We estimated consumption across the 2019 Castle and Ikes Fires by subtracting 2020 fuel load maps from 2019 fuel load maps and examined the relationship between mapped surface fuels and years since fire, as recorded in the Monitoring Trends in Burn Severity (MTBS) database.

RESULTS

R-squared correlations between predicted and ground-observed fuels were 50, 39, 59, and 48% for available canopy fuel, 1- to 1000-h fuels, litter and duff, and total surface fuel (sum of 1- to 1000-h, litter and duff fuels), respectively. Lidar metrics describing overstory distribution and density, understory density, Landsat fire history metrics, and elevation were important predictors. Mapped surface fuel loads were positively and nonlinearly related to time since fire, with asymptotes to stable fuel loads at 10-15 years post fire. Surface fuel consumption averaged 16.1 and 14.0 Mg ha^- 1 for the Castle and Ikes Fires, respectively, and was positively correlated with the differenced Normalized Burn Ratio (dNBR). We estimated surface fuel consumption to be 125.3 ± 54.6 Gg for the Castle Fire and 27.6 ± 12.0 Gg for the portion of the Ikes Fire (42%) where pre- and post-fire airborne lidar were available.

CONCLUSIONS

We demonstrated and reinforced that canopy and surface fuels can be predicted and mapped with moderate accuracy using airborne lidar data. Landsat-derived fire history helped account for spatial and temporal variation in surface fuel loads and allowed us to describe temporal trends in surface fuel loads. Our fuel load and consumption maps and methods have utility for land managers and researchers who need landscape-wide estimates of fuel loads and emissions. Fuel load maps based on active remote sensing can be used to inform fuel management decisions and assess fuel structure goals, thereby promoting ecosystem resilience. Multitemporal lidar-based consumption estimates can inform emissions estimates and provide independent validation of conventional fire emission inventories. Our methods also provide a remote sensing framework that could be applied in other areas where airborne lidar is available for quantifying relationships between fuels and time since fire across landscapes.

Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction

The management of low-density savannah and woodland forests for carbon storage presents a mechanism to offset the expense of ecologically informed forest management strategies. However, existing carbon monitoring systems draw on vast amounts of either field observations or aerial light detection and ranging (LiDAR) collections, making them financially prohibitive in low productivity systems where forest management focuses on promoting resilience to disturbance and multiple uses. This study evaluates how UAS altitude and flight speed influence area-based aboveground forest biomass model predictions. The imagery was acquired across a range of UAS altitudes and flight speeds that influence the efficiency of data collection. Data were processed using common structures from motion photogrammetry algorithms and then modeled using Random Forest. These results are compared to LiDAR observations collected from fixed-wing manned aircraft and modeled using the same routine. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling accuracy. UAS predictions increasingly outperformed (2–24% increased variance explained) commercial airborne LiDAR strategies as acquisition altitude increased from 80–120 m. The reduced cost of UAS data collection and processing and improved biomass modeling accuracy over airborne LiDAR approaches could make carbon monitoring viable in low productivity forest systems.

Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs

Genetic structure, i.e. intra-population genetic diversity and inter-population genetic differentiation, is influenced by the amount and spatial configuration of habitat. Measuring the amount of reachable habitat (ARH) makes it possible to describe habitat patterns by considering intra-patch and inter-patch connectivity, dispersal capacities and matrix resistance. Complementary ARH metrics computed under various resistance scenarios are expected to reflect both drift and gene flow influence on genetic structure. Using an empirical genetic dataset concerning the large marsh grasshopper (Stethophyma grossum), we tested whether ARH metrics are good predictors of genetic structure. We further investigated (i) how the components of the ARH influence genetic structure and (ii) which resistance scenario best explains these relationships. We computed local genetic diversity and genetic differentiation indices in genetic graphs, and ARH metrics in the unified and flexible framework offered by landscape graphs, and we tested the relationships between these variables. ARH metrics were relevant predictors of the two components of genetic structure, providing an advantage over commonly used habitat metrics. Although allelic richness was significantly explained by three complementary ARH metrics in the best PLS regression model, private allelic richness and MIW indices were essentially related with the ARH measured outside the focal patch. Considering several matrix resistance scenarios was also key for explaining the different genetic responses. We thus call for further use of ARH metrics in landscape genetics to explain the influence of habitat patterns on the different components of genetic structure.

Identifying core habitats and corridors for giant pandas by combining multiscale random forest and connectivity analysis

Habitat loss and fragmentation are widely acknowledged as the main driver of the decline of giant panda populations. The Chinese government has made great efforts to protect this charming species and has made remarkable achievements, such as population growth and habitat expansion. However, habitat fragmentation has not been reversed. Protecting giant pandas in a large spatial extent needs to identify core habitat patches and corridors connecting them. This study used an equal-sampling multiscale random forest habitat model to predict a habitat suitability map for the giant panda. Then, we applied the resistant kernel method and factorial least-cost path analysis to identify core habitats connected by panda dispersal and corridors among panda occurrences, respectively. Finally, we evaluated the effectiveness of current protected areas in representing core habitats and corridors. Our results showed high scale dependence of giant panda habitat selection. Giant pandas strongly respond to bamboo percentage and elevation at a relatively fine scale (1 km), whereas they respond to anthropogenic factors at a coarse scale (≥2 km). Dispersal ability has significant effects on core habitats extent and population fragmentation evaluation. Under medium and high dispersal ability scenarios (12,000 and 20,000 cost units), most giant panda habitats in the Qionglai mountain are predicted to be well connected by dispersal. The proportion of core habitats covered by protected areas varied between 38% and 43% under different dispersal ability scenarios, highlighting significant gaps in the protected area network. Similarly, only 43% of corridors that connect giant panda occurrences were protected. Our results can provide crucial information for conservation managers to develop wise strategies to safeguard the long-term viability of the giant panda population.

Matrix condition mediates the effects of habitat fragmentation on species extinction risk

Habitat loss is the leading cause of the global decline in biodiversity, but the influence of human pressure within the matrix surrounding habitat fragments remains poorly understood. Here, we measure the relationship between fragmentation (the degree of fragmentation and the degree of patch isolation), matrix condition (measured as the extent of high human footprint levels), and the change in extinction risk of 4,426 terrestrial mammals. We find that the degree of fragmentation is strongly associated with changes in extinction risk, with higher predictive importance than life-history traits and human pressure variables. Importantly, we discover that fragmentation and the matrix condition are stronger predictors of risk than habitat loss and habitat amount. Moreover, the importance of fragmentation increases with an increasing deterioration of the matrix condition. These findings suggest that restoration of the habitat matrix may be an important conservation action for mitigating the negative effects of fragmentation on biodiversity.

The influence of human pressure within the matrix surrounding habitat fragments remains poorly understood. This study measures the relationship between habitat fragmentation, matrix condition and the change in extinction risk of 4,426 terrestrial mammals, finding that fragmentation and matrix condition are stronger predictors of risk than habitat loss and habitat amount.

Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety

Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections approximately 260 ha in area in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time (range: 0% to 90.5%) and the mobile goTenna was disconnected from all other devices 18.2% of the time (range: 0% to 44.5%). RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the connectivity of real-time mesh networks over large spatial extents using remote sensing data in order to forecast how well similar networks are expected to perform for wildland firefighting, forestry, and public safety applications. However, safety professionals should be aware of the impacts of vegetation on connectivity.

High stability and metabolic capacity of bacterial community promote the rapid reduction of easily decomposing carbon in soil

Irreversible climate change alters the decomposition and sequestration of soil carbon (C). However, the stability of C components in soils with different initial organic matter contents and its relationship with the response of major decomposers to climate warming are still unclear. In this study, we translocated Mollisols with a gradient of organic matter (OM) contents (2%-9%) from in situ cold region to five warmer climatic regions to simulate climate change. Soil C in C-rich soils (OM >5%) was more vulnerable to translocation warming than that in C-poor soils (OM ≤ 5%), with a major loss of functional groups like O-alkyl, O-aryl C and carboxyl C. Variations of microbial β diversity with latitude, temperature and precipitation indicated that C-rich soils contained more resistant bacterial communities and more sensitive fungal communities than C-poor soils, which led to strong C metabolism and high utilization ability of the community in C-rich soils in response to translocation warming. Our results suggest that the higher sensitivity of soils with high organic matter content to climate change is related to the stability and metabolic capacity of major bacterial decomposers, which is important for predicting soil-climate feedback.

Inter-phylum negative interactions affect soil bacterial community dynamics and functions during soybean development under long-term nitrogen fertilization

Understanding interspecies interactions is essential to predict the response of microbial communities to exogenous perturbation. Herein, rhizospheric and bulk soils were collected from five developmental stages of soybean, which grew in soils receiving 16-year nitrogen inputs. Bacterial communities and functional profiles were examined using high-throughput sequencing and quantitative PCR, respectively. The objective of this study was to identify the key bacterial interactions that influenced community dynamics and functions. We found that the stages of soybean development outcompeted nitrogen fertilization management in shaping bacterial community structure, while fertilization treatments significantly shaped the abundance distribution of nitrogen functional genes. Temporal variations in bacterial abundances increased in bulk soils, especially at the stage of soybean branching, which helps to infer underlying negative interspecies interactions. Members of Cyanobacteria and Actinobacteria actively engaged in inter-phylum negative interactions in bulk soils and soybean rhizosphere, respectively. Furthermore, the negative interactions between nitrogen-fixing functional groups and the reduction of nifH gene abundance were coupled during soybean development, which may help to explain the linkages between population dynamics and functions. Overall, these findings highlight the importance of inter-phylum negative interactions in shaping the correlation patterns of bacterial communities and in determining soil functional potential.

Entropy in Landscape Ecology: A Quantitative Textual Multivariate Review

This paper presents a multivariate textual analysis of more than 1300 papers on entropy in ecology. There are six main insights that emerged. First, there is a large body of literature that has addressed some aspect of entropy in ecology, most of which has been published in the last 5-10 years. Second, the vast majority of these papers focus on species distribution, species richness, relative abundance or trophic structure and not landscape-scale patterns or processes, pe se. Third, there have been few papers addressing landscape-level questions related to entropy. Fourth, the quantitative analysis with hierarchical clustering identified a strongly nested structure among papers that addressed entropy in ecology. Fifth, there is clear differentiation of papers focused on landscape-level applications of entropy from other papers, with landscape focused papers clustered together at each level of the hierarchy in a relatively small and closely associated group. Sixth, this group of landscape-focused papers was substructured between papers that explicitly adopted entropy measures to quantify the spatial pattern of landscape mosaics, often using variations on Boltzmann entropy, versus those that utilize Shannon entropy measures from information theory, which are not generally explicit in their assessment of spatial configuration. This review provides a comprehensive, quantitative assessment of the scope, trends and relationships among a large body of literature related to entropy in ecology and for the first time puts landscape ecological research on entropy into that context.

Location and Species Matters: Variable Influence of the Environment on the Gene Flow of Imperiled, Native and Invasive Cottontails

The environment plays an important role in the movement of individuals and their associated genes among populations, which facilitates gene flow. Gene flow can help maintain the genetic diversity both within and between populations and counter the negative impact of genetic drift, which can decrease the fitness of individuals. Sympatric species can have different habitat preferences, and thus can exhibit different patterns of genetic variability and population structure. The specialist-generalist variation hypothesis (SGVH) predicts that specialists will have lower genetic diversity, lower effective population sizes (Ne), and less gene flow among populations. In this study, we used spatially explicit, individual-based comparative approaches to test SGVH predictions in two sympatric cottontail species and identify environmental variables that influence their gene flow. New England cottontail (Sylvilagus transitionalis) is the only native cottontail in the Northeast US, an early successional habitat specialist, and a species of conservation concern. Eastern cottontail (S. floridanus) is an invasive species in the Northeast US and a habitat generalist. We characterized each species' genomic variation by developing double-digest Restriction-site Associated DNA sequence single nucleotide polymorphism markers, quantified their habitat with Geographic Information System environmental variables, and conducted our analyses at multiple scales. Surprisingly, both species had similar levels of genetic diversity and eastern cottontail's Ne was only higher than New England cottontail in one of three subregions. At a regional level, the population clusters of New England cottontail were more distinct than eastern cottontail, but the subregional levels showed more geographic areas of restricted gene flow for eastern cottontail than New England cottontail. In general, the environmental variables had the predicted effect on each species' gene flow. However, the most important environmental variable varied by subregion and species, which shows that location and species matter. Our results provide partial support for the SGVH and the identification of environmental variables that facilitate or impede gene flow can be used to help inform management decisions to conserve New England cottontail.

A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data

Aedes mosquitoes are projected to continue expanding their ranges, which could expose millions more humans to the diseases they carry. The implementation of vector control methods ranging from traditional (e.g., insecticides) to cutting edge (e.g., genetic modification) could be improved with landscape connectivity maps and increased understanding of the factors that affect mosquito dispersal. Here we present an iterative random forest method for integrating genetic and environmental data to map landscape connectivity. We achieve a correlation of 0.83 between the model’s predicted genetic distance and actual genetic distance. We produce a genetic connectivity map for the southern tier of the United States and discuss important factors to consider in mosquito control, e.g., the release of genetically modified mosquitoes.

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza–Edwards distance (CSE) performs better than linearized F_ST. The correlation (R) between the model’s predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti’s range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning

Vector control is an effective strategy for reducing vector-borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub-Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross-validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems.

Circulating bacterial signature is linked to metabolic disease and shifts with metabolic alleviation after bariatric surgery

BACKGROUND

The microbiome has emerged as an environmental factor contributing to obesity and type 2 diabetes (T2D). Increasing evidence suggests links between circulating bacterial components (i.e., bacterial DNA), cardiometabolic disease, and blunted response to metabolic interventions. In this aspect, thorough next-generation sequencing-based and contaminant-aware approaches are lacking. To address this, we tested whether bacterial DNA could be amplified in the blood of subjects with obesity and high metabolic risk under strict experimental and analytical control and whether a putative bacterial signature is related to metabolic improvement after bariatric surgery.

METHODS

Subjects undergoing bariatric surgery were recruited into sex- and BMI-matched subgroups with (n = 24) or without T2D (n = 24). Bacterial DNA in the blood was quantified and prokaryotic 16S rRNA gene amplicons were sequenced. A contaminant-aware approach was applied to derive a compositional microbial signature from bacterial sequences in all subjects at baseline and at 3 and 12 months after surgery. We modeled associations between bacterial load and composition with host metabolic and anthropometric markers. We further tested whether compositional shifts were related to weight loss response and T2D remission. Lastly, bacteria were visualized in blood samples using catalyzed reporter deposition (CARD)-fluorescence in situ hybridization (FISH).

RESULTS

The contaminant-aware blood bacterial signature was associated with metabolic health. Based on bacterial phyla and genera detected in the blood samples, a metabolic syndrome classification index score was derived and shown to robustly classify subjects along their actual clinical group. T2D was characterized by decreased bacterial richness and loss of genera associated with improved metabolic health. Weight loss and metabolic improvement following bariatric surgery were associated with an early and stable increase of these genera in parallel with improvements in key cardiometabolic risk parameters. CARD-FISH allowed the detection of living bacteria in blood samples in obesity.

CONCLUSIONS

We show that the circulating bacterial signature reflects metabolic disease and its improvement after bariatric surgery. Our work provides contaminant-aware evidence for the presence of living bacteria in the blood and suggests a putative crosstalk between components of the blood and metabolism in metabolic health regulation.

Seed-borne, endospheric and rhizospheric core microbiota as predictors of plant functional traits across rice cultivars are dominated by deterministic processes

A host-plant and its associated microbiota depend on one another. However, the assembly process and the functioning of host-associated microbiota are poorly understood. Herein, rice was used as model plant to investigate the assemblage of bacterial microbiota, including those in the seed, root endosphere and rhizosphere. We also assessed the degree to which endosphere and rhizosphere communities were influenced by vertical transmission through seed and identified the core microbes that potentially associated with plant phenotypic properties. Plant microhabitat, rather than subspecies type, was the major driver shaping plant-associated bacterial microbiota. Deterministic processes were primarily responsible for community assembly in all microhabitats. The influence of vertical transmission from seed to root-associated bacterial communities appeared to be quite weak (endosphere) or even absent (rhizosphere). A core microbial community composed of 15 generalist species persisted across different microhabitats and represented key connectors in networks. Host-plant functional traits were linked to the relative abundance of these generalist core microbes and could be predicted from them using machine learning algorithms. Overall, bacterial microbiota is assembled by host-plant interactions in a deterministic-based manner. This study enhances our understanding of the driving mechanisms and associations of microbiota in various plant microhabitats and provides new perspectives to improve plant performance.

Informed breeding dispersal following stochastic changes to patch quality in a pond-breeding amphibian

The unidirectional movement of animals between breeding patches (i.e. breeding dispersal) has profound implications for the ecological and evolutionary dynamics of spatially structured populations. In spatiotemporally variable environments, individuals are expected to adjust their dispersal decisions according to information gathered on the environmental and/or social cues that reflect the fitness prospects in a given breeding patch (i.e. informed dispersal). A paucity of empirical work limited our understanding of the ability of animals to depart from low-quality breeding patches and settle in high-quality breeding patches. We examined the capacity of individuals to respond to stochastic changes in habitat quality via informed breeding dispersal in a pond-breeding amphibian. We conducted a 5-year (2015-2019) capture-recapture study of boreal toads Anaxyrus boreas boreas (n = 1,100) that breed in beaver ponds in western Wyoming, USA. During early spring of 2017, an extreme flooding event destroyed several beaver dams and resulted in the loss of breeding habitat. We used multi-state models to investigate how temporal changes in pond characteristics influenced breeding dispersal, and determine whether movement decisions were in accordance with prospects for reproductive fitness. Boreal toads more often departed from low-quality breeding ponds (without successful metamorphosis) and settled in high-quality breeding ponds (with successful metamorphosis). Movement decisions were context-dependent and associated with pond characteristics altered by beaver dam destruction. Individuals were more likely to depart from shallow ponds with high vegetation cover and settle in deep ponds with low vegetation cover. The probability of metamorphosis was related to the same environmental cues, suggesting that boreal toads assess the fitness prospects of a breeding patch and adjust movement decisions accordingly (i.e. informed breeding dispersal). We demonstrated that stochastic variability in environmental conditions and habitat quality can underpin dispersal behaviour in amphibians. Our study highlighted the mechanistic linkages between habitat change, movement behaviour and prospects for reproductive performance, which is critical for understanding how wild animals respond to rapid environmental change.

Contrasting patterns in the occurrence and biomass centers of gravity among fish and macroinvertebrates in a continental shelf ecosystem

The distribution of a group of fish and macroinvertebrates (n = 52) resident in the US Northeast Shelf large marine ecosystem were characterized with species distribution models (SDM), which in turn were used to estimate occurrence and biomass center of gravity (COG). The SDMs were fit using random forest machine learning and were informed with a range of physical and biological variables. The estimated probability of occurrence and biomass from the models provided the weightings to determine depth, distance to the coast, and along-shelf distance COG. The COGs of occupancy and biomass habitat tended to be separated by distances averaging 50 km, which approximates half of the minor axis of the subject ecosystem. During the study period (1978-2018), the biomass COG has tended to shift to further offshore positions whereas occupancy habitat has stayed at a regular spacing from the coastline. Both habitat types have shifted their along-shelf distances, indicating a general movement to higher latitude or to the Northeast for this ecosystem. However, biomass tended to occur at lower latitudes in the spring and higher latitude in the fall in a response to seasonal conditions. Distribution of habitat in relation to depth reveals a divergence in response with occupancy habitat shallowing over time and biomass habitat distributing in progressively deeper water. These results suggest that climate forced change in distribution will differentially affect occurrence and biomass of marine taxa, which will likely affect the organization of ecosystems and the manner in which human populations utilize marine resources.

The role of the environment in the spatial dynamics of an extensive hybrid zone between two neotropical cats

Identifying factors that create and maintain a hybrid zone is of great interest to ecology, evolution and, more recently, conservation biology. Here, we investigated the role of environmental features in shaping the spatial dynamics of a hybrid zone between the southern tigrina, Leopardus guttulus, and Geoffroy's cat, L. geoffroyi, testing for exogenous selection as the main force acting on its maintenance. These Neotropical felid species are mainly allopatric, with a restricted area of sympatry in the ecotone between the Atlantic Forest and Pampa biomes. As both biomes have experienced high rates of anthropogenic habitat alteration, we also analysed the influence of habitat conversion on the hybrid zone structure. To do this, we used 13 microsatellite loci to identify potential hybrids and generated ecological niche models for them and their parental species. We compared the influence of variables on parental species and hybrid occurrence and calculated the amount of niche overlap among them. Parental species showed different habitat requirements and predicted co-occurrence was restricted to the forest-grassland mosaic of the ecotone. However, hybrids were found beyond this area, mainly in the range of L. geoffroyi. Hybrids demonstrated higher tolerance to habitat alteration than parental types, with a probability of occurrence that was positively related with mosaics of cropland areas and remnants of natural vegetation. These results indicate that exogenous selection alone does not drive the dynamics of the hybrid zone, and that habitat conversion influences its structure, potentially favouring hybrids over parental species.

How fire interacts with habitat loss and fragmentation

Biodiversity faces many threats and these can interact to produce outcomes that may not be predicted by considering their effects in isolation. Habitat loss and fragmentation (hereafter 'fragmentation') and altered fire regimes are important threats to biodiversity, but their interactions have not been systematically evaluated across the globe. In this comprehensive synthesis, including 162 papers which provided 274 cases, we offer a framework for understanding how fire interacts with fragmentation. Fire and fragmentation interact in three main ways: (i) fire influences fragmentation (59% of 274 cases), where fire either destroys and fragments habitat or creates and connects habitat; (ii) fragmentation influences fire (25% of cases) where, after habitat is reduced in area and fragmented, fire in the landscape is subsequently altered because people suppress or ignite fires, or there is increased edge flammability or increased obstruction to fire spread; and (iii) where the two do not influence each other, but fire interacts with fragmentation to affect responses like species richness, abundance and extinction risk (16% of cases). Where fire and fragmentation do influence each other, feedback loops are possible that can lead to ecosystem conversion (e.g. forest to grassland). This is a well-documented threat in the tropics but with potential also to be important elsewhere. Fire interacts with fragmentation through scale-specific mechanisms: fire creates edges and drives edge effects; fire alters patch quality; and fire alters landscape-scale connectivity. We found only 12 cases in which studies reported the four essential strata for testing a full interaction, which were fragmented and unfragmented landscapes that both span contrasting fire histories, such as recently burnt and long unburnt vegetation. Simulation and empirical studies show that fire and fragmentation can interact synergistically, multiplicatively, antagonistically or additively. These cases highlight a key reason why understanding interactions is so important: when fire and fragmentation act together they can cause local extinctions, even when their separate effects are neutral. Whether fire-fragmentation interactions benefit or disadvantage species is often determined by the species' preferred successional stage. Adding fire to landscapes generally benefits early-successional plant and animal species, whereas it is detrimental to late-successional species. However, when fire interacts with fragmentation, the direction of effect of fire on a species could be reversed from the effect expected by successional preferences. Adding fire to fragmented landscapes can be detrimental for species that would normally co-exist with fire, because species may no longer be able to disperse to their preferred successional stage. Further, animals may be attracted to particular successional stages leading to unexpected responses to fragmentation, such as higher abundance in more isolated unburnt patches. Growing human populations and increasing resource consumption suggest that fragmentation trends will worsen over coming years. Combined with increasing alteration of fire regimes due to climate change and human-caused ignitions, interactions of fire with fragmentation are likely to become more common. Our new framework paves the way for developing a better understanding of how fire interacts with fragmentation, and for conserving biodiversity in the face of these emerging challenges.

Antibiotic-associated dysbiosis affects the ability of the gut microbiota to control intestinal inflammation upon fecal microbiota transplantation in experimental colitis models

BACKGROUND

The gut microbiota plays a central role in host physiology and in several pathological mechanisms in humans. Antibiotics compromise the composition and functions of the gut microbiota inducing long-lasting detrimental effects on the host. Recent studies suggest that the efficacy of different clinical therapies depends on the action of the gut microbiota. Here, we investigated how different antibiotic treatments affect the ability of the gut microbiota to control intestinal inflammation upon fecal microbiota transplantation in an experimental colitis model and in ex vivo experiments with human intestinal biopsies.

RESULTS

Murine fecal donors were pre-treated with different antibiotics, i.e., vancomycin, streptomycin, and metronidazole before FMT administration to colitic animals. The analysis of the gut microbiome, fecal metabolome, and the immunophenotyping of colonic lamina propria immune cells revealed that antibiotic pre-treatment significantly influences the capability of the microbiota to control intestinal inflammation. Streptomycin and vancomycin-treated microbiota failed to control intestinal inflammation and were characterized by the blooming of pathobionts previously associated with IBD as well as with metabolites related to the presence of oxidative stress and metabolism of simple sugars. On the contrary, the metronidazole-treated microbiota retained its ability to control inflammation co-occurring with the enrichment of Lactobacillus and of innate immune responses involving iNKT cells. Furthermore, ex vivo cultures of human intestinal lamina propria mononuclear cells and iNKT cell clones from IBD patients with vancomycin pre-treated sterile fecal water showed a Th1/Th17 skewing in CD4⁺ T-cell populations; metronidazole, on the other hand, induced the polarization of iNKT cells toward the production of IL10.

CONCLUSIONS

Diverse antibiotic regimens affect the ability of the gut microbiota to control intestinal inflammation in experimental colitis by altering the microbial community structure and microbiota-derived metabolites. Video Abstract.