Explore the vast range of titles available.

Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes.

Connecting scientific research and government policy is essential for achieving objectives in sustaining biodiversity in an economic context. Our approach to connecting theoretical ecology, applied ecology, and policy was devised using principles of restoration ecology and the requisite methodology to restore biodiverse ecosystems. Using a threatened ecological community (TEC) with >120 plant species, we posit our approach as a guide for interpreting and achieving regulatory compliance (i.e., government conditions) enacted to manage or offset environmental impacts of development. We inform the scientific approach necessary to delivering outcomes appropriate to policy intent and biodiverse restoration through theoretical and applied research into the ecological restoration of the highly endemic flora of banded ironstone formations of the Mid West of Western Australia. Our approach (1) defines scale-appropriate restoration targets that meet regulatory compliance (e.g., Government of Western Australia Ministerial Conditions); (2) determines the optimal method to return individual plant species to the restoration landscape; (3) develops a conceptual model for our system, based on existing restoration frameworks, to optimize and facilitate the pathway to the restoration of a vegetation community (e.g., TEC) using diverse research approaches; and (4) develops an assessment protocol to compare restoration achievements against the expected regulatory outcomes using our experimental restoration trials as a test example. Our approach systematically addressed the complex challenges in setting and achieving restoration targets for an entire vegetation community, a first for a semiarid environment. We interpret our approach as an industry application relevant to policy- or regulator-mediated mine restoration programs that seek to return biodiverse species assemblages at landscape scales.

This paper presents the Dominant Species-Physiognomy-Ecological (DSPE) classification system developed for large-scale differentiation of plant ecological communities from high-spatial resolution remote sensing images. In this system, the plant ecological communities are defined with the inference of dominant species, physiognomy, and shared ecological settings by incorporating multiple strata. The DSPE system was implemented in a cool-temperate climate zone at a regional scale. The deep recurrent neural networks with bootstrap resampling method were employed for evaluating performance of the DSPE classification using Sentinel-2 images at 10 m spatial resolution. The performance of differentiating DSPE communities was compared with the differentiation of higher, Dominant Genus-Physiognomy-Ecological (DGPE) communities. Overall, there was a small difference in the classification between 58 DSPE communities (F1-score = 85.5%, Kappa coefficient = 84.7%) and 45 DGPE communities (F1-score = 86.5%, Kappa coefficient = 85.7%). However, the class wise accuracy analysis showed that all 58 DSPE communities were differentiated with more than 60% accuracy, whereas more than 70% accuracy was obtained for the classification of all 45 DGPE communities. Since all 58 DSPE communities were classified with more than 60% accuracy, the DSPE classification system was still effective for the differentiation of plant ecological communities from satellite images at a regional scale, indicating its applications in other regions in the world.

The Shuaishui River originates from the southern mountainous area of Anhui Province and is an important water source for local residents. The ecological environment of this basin has been seriously damaged because of the effects of human disturbance. In August 2016, a field study of five units of the Shuaishui River Basin was conducted to understand the fish community structure and assess the ecological health status. A total of 43 fish species were collected from the entire river basin, and they belonged to 4 Orders, 10 Families, and 31 Genera. The maximum number of species belonged to the family Cyprinidae, and the main trophic guild was omnivorous fish. Among the five units, species number was the highest in unit 2 (27 species) and the lowest in unit 3 (12 species). The dominant species in the five units were mainly typical mountain-stream fish, such as Zacco platypus, Acrossocheilus fasciatus, and Vanmanenia stenosoma. In some areas, Varicorhinus barbatulus or Rhinogobius cliffordpopei also showed great dominance because of the impacts of the local habitat conditions. Redundancy analysis showed that altitude, water velocity, stream order, and water surface width were the main factors that influenced the distribution and species composition of the fish. Eigenvalues of the first two axes were 0.183 and 0.082 and explained 40.9% and 18.3% of the species-environment relationship variables, respectively. The ecological health of the five units and the entire basin was assessed based on the arithmetic mean of three indicators, namely, number of classification units, Shannon-Wiener index, and Berger-Parker dominance index. The results indicated that the ecological health status was relatively poor in unit 3, general in units 1 and 5, and good in units of 2 and 4. The status of the entire basin was general. To the best of our knowledge, this is the first comprehensive assessment of the ecological health of the Shuaishui River Basin, and it has great significance for the ecological management and protection of this basin.

Understanding the stability of ecological communities is a matter of increasing importance in the context of global environmental change. Yet it has proved to be a challenging task. Different metrics are used to assess the stability of ecological systems, and the choice of one metric over another may result in conflicting conclusions. Although each of the multitude of metrics is useful for answering a specific question about stability, the relationship among metrics is poorly understood. Such lack of understanding prevents scientists from developing a unified concept of stability. Instead, by investigating these relationships we can unveil how many dimensions of stability there are (i.e., in how many independent components stability metrics can be grouped), which should help build a more comprehensive concept of stability. Here we simultaneously measured 27 stability metrics frequently used in ecological studies. Our approach is based on dynamical simulations of multispecies trophic communities under different perturbation scenarios. Mapping the relationships between the metrics revealed that they can be lumped into 3 main groups of relatively independent stability components: early response to pulse, sensitivities to press, and distance to threshold. Selecting metrics from each of these groups allows a more accurate and comprehensive quantification of the overall stability of ecological communities. These results contribute to improving our understanding and assessment of stability in ecological communities.

Proponents of development projects (e.g., new roads, mines, dams) are frequently required to assess and manage their impacts on threatened biodiversity. Here, we propose that the environmental legislation and standards that mandate such assessments are failing those threatened species and ecological communities listed as vulnerable. Using a case study of Australia's key environmental legislation, we highlight that vulnerable ecological communities receive no statutory protection, while vulnerable species are held to a less stringent standard in the impact assessment process compared with those that are endangered or critically endangered. In the 19 years since Australia's Environment Protection and Biodiversity Conservation Act 1999 was enacted, four times as many vulnerable species have declined in their threat status than have improved. Beyond Australia, we demonstrate the global relevance of this issue, as it applies to internationally recognized best practice impact assessment guidelines. These cases provide a cautionary tale: without greater attention and stricter assessment criteria in the impact assessment process, the vulnerable species of today risk becoming the endangered species of tomorrow, with all the attendant costs and missed opportunities for recovery that this implies.

1. A framework for the description and analysis of multilayer networks is established in statistical physics, and calls are increasing for their adoption by community ecologists. Multilayer networks in community ecology will allow space, time and multiple interaction types to be incorporated into species interaction networks.2. While the multilayer network framework is applicable to ecological questions, it is one thing to be able to describe ecological communities as multilayer networks and another for multilayer networks to actually prove useful for answering ecological questions. Importantly, documenting multilayer network structure requires substantially greater empirical investment than standard ecological networks. In response, we argue that this additional effort is worthwhile and describe a series of research lines where we expect multilayer networks will generate the greatest impact.3. Inter‐layer edges are the key component that differentiate multilayer networks from standard ecological networks. Inter‐layer edges join different networks—termed layers—together and represent ecological processes central to the species interactions studied (e.g., inter‐layer edges representing movement for networks separated in space). Inter‐layer edges may take a variety of forms, be species‐ or network‐specific, and be measured with a large suite of empirical techniques. Additionally, the sheer size of ecological multilayer networks also requires somechanges to empirical data collection around interaction quantification, collaborative efforts and collation in public databases.4. Network ecology has already touched on a wide swath of ecology and evolutionary biology. Because network stability and patterns of species linkage are the most developed areas of network ecology, they are a natural starting place for multilayer investigations. However, multilayer etworks will also provide novel insights to niche partitioning, the connection between traits and species’ interactions, and even the geographic mosaic of co‐evolution.5. Synthesis. Multilayer networks provide a formal way to bring together the study of species interaction networks and the processes that influence them. However, describing inter‐layer edges and the increasing amounts of data required represent challenges. The pay‐off for added investment will be ecological networks that describe the composition and capture the dynamics of ecological communities more completely and, consequently, have greater power for understanding the patterns and processes that underpin diversity in ecological communities.

How multiple types of non-trophic interactions map onto trophic networks in real communities remains largely unknown. We present the first effort, to our knowledge, describing a comprehensive ecological network that includes all known trophic and diverse non-trophic links among >100 coexisting species for the marine rocky intertidal community of the central Chilean coast. Our results suggest that non-trophic interactions exhibit highly nonrandom structures both alone and with respect to food web structure. The occurrence of different types of interactions, relative to all possible links, was well predicted by trophic structure and simple traits of the source and target species. In this community, competition for space and positive interactions related to habitat/refuge provisioning by sessile and/or basal species were by far the most abundant non-trophic interactions. If these patterns are corroborated in other ecosystems, they may suggest potentially important dynamic constraints on the combined architecture of trophic and non-trophic interactions. The nonrandom patterning of non-trophic interactions suggests a path forward for developing a more comprehensive ecological network theory to predict the functioning and resilience of ecological communities.

Human body is inhabited by vast number of microorganisms which form a complex ecological community and influence the human physiology, in the aspect of both health and diseases. These microbes show a relationship with the human immune system based on coevolution and, therefore, have a tremendous potential to contribute to the metabolic function, protection against the pathogen and in providing nutrients and energy. However, of these microbes, many carry out some functions that play a crucial role in the host physiology and may even cause diseases. The introduction of new molecular technologies such as transcriptomics, metagenomics and metabolomics has contributed to the upliftment on the findings of the microbiome linked to the humans in the recent past. These rapidly developing technologies are boosting our capacity to understand about the human body-associated microbiome and its association with the human health. The highlights of this review are inclusion of how to derive microbiome data and the interaction between human and associated microbiome to provide an insight on the role played by the microbiome in biological processes of the human body as well as the development of major human diseases.