Small instream infrastructure: Comparative methods and evidence of environmental and ecological responses

1. Around the globe, instream infrastructures such as dams, weirs, and culverts associated with roads are wide‐spread and continue to be constructed. There is limited documentation of smaller infrastructure because of mixed regulation and laws related to instream construction, as well as difficulty in documentation because of their size and frequency in waterscapes. 2. We reviewed evidence of different methods used to quantify environmental and ecological responses (positive, negative, or neutral) to dams, weirs, and culverts. 3. Most studies (78% of 87) in our review evaluated dams or weirs, and more than half evaluated environmental or ecological responses at more than one of these structures. More than half of the studies used spatial (disturbed–undisturbed in the same or a different catchment) rather than temporal (before–after construction or before–after destruction) comparative methods. Evaluations also tended to focus on ecological variables, most specifically on fish community responses (just over a quarter) to infrastructure. 4. More than half (58%) of the evaluations at dams, weirs, or culverts reported negative environmental or ecological responses. Discrepancies in responses recorded for different infrastructure types could be partially explained by the focus on ecological responses in reviewed studies and related metrics used for evaluations (e.g. biotic groups, richness, and abundance), the imbalance of studies at different infrastructure types, and discrepancies in spatial and temporal scales of evaluations compared to those at which the variables respond to infrastructure. 5. Despite the abundance of road culverts greatly exceeding the number of small or large dams worldwide, they were evaluated in only 22% of studies that we reviewed. Our findings underscore the need for studies to not only better understand local but also cumulative impacts of these smaller infrastructure, as these could be greater than those caused by large infrastructure depending on their location, density, and type, among other factors. Such studies are needed to inform infrastructure planning and watershed management

Statistically reinforced machine learning for nonlinear patterns and variable interactions

Most statistical models assume linearity and few variable interactions, even though real‐world ecological patterns often result from nonlinear and highly interactive processes. We here introduce a set of novel empirical modeling techniques which can address this mismatch: statistically reinforced machine learning. We demonstrate the behaviors of three techniques (conditional inference tree, model‐based tree, and permutation‐based random forest) by analyzing an artificially generated example dataset that contains patterns based on nonlinearity and variable interactions. The results show the potential of statistically reinforced machine learning algorithms to detect nonlinear relationships and higher‐order interactions. Estimation reliability for any technique, however, depended on sample size. The applications of statistically reinforced machine learning approaches would be particularly beneficial for investigating (1) novel patterns for which shapes cannot be assumed a priori, (2) higher‐order interactions which are often overlooked in parametric statistics, (3) context dependency where patterns change depending on other conditions, (4) significance and effect sizes of variables while taking nonlinearity and variable interactions into account, and (5) a hypothesis using parametric statistics after identifying patterns using statistically reinforced machine learning techniques

Perceptions of a curriculum vitae clinic for conservation science students

We led a curriculum vitae (CV) clinic aimed at student participants attending the 28th International Congress for Conservation Biology (ICCB 2017) in Cartagena, Colombia. The CV Clinic was a pilot program consisting of resources to assist with developing an effective CV and involving preconference and at-conference reviews of student attendees' CVs. Here, we explore our experiences in organizing the CV Clinic as well as nonparticipant and participant perceptions of the clinic. We used an online standardized interview form to gather qualitative data on nonparticipant and participant perceptions of the CV Clinic, and to explore how such a CV Clinic program could best align with student needs. Most respondents who submitted their CV for review ahead of ICCB 2017 (n = 9) found the template and guidance useful. Half of the respondents who did not participate in the CV Clinic perceived the clinic as duplicating services provided by their academic institutions. Both participant and nonparticipant respondents perceived value in such a CV Clinic, but also believed that adjustments could be made to make the CV review part of a broader professional development program lead by Society for Conservation Biology (SCB). Key lessons learned from the CV Clinic include the need to: (a) document and evaluate professional development initiatives within SCB; (b) better understand and account for the diversity of student needs before program creation; and (c) pilot and evaluate appropriateness of different locations, frequency, and duration of professional development programs

Bending the curve of global freshwater biodiversity loss: an emergency recovery plan

Despite their limited spatial extent, freshwater ecosystems host remarkable biodiversity, including one-third of all vertebrate species. This biodiversity is declining dramatically: Globally, wetlands are vanishing three times faster than forests, and freshwater vertebrate populations have fallen more than twice as steeply as terrestrial or marine populations. Threats to freshwater biodiversity are well documented but coordinated action to reverse the decline is lacking. We present an Emergency Recovery Plan to bend the curve of freshwater biodiversity loss. Priority actions include accelerating implementation of environmental flows; improving water quality; protecting and restoring critical habitats; managing the exploitation of freshwater ecosystem resources, especially species and riverine aggregates; preventing and controlling nonnative species invasions; and safeguarding and restoring river connectivity. We recommend adjustments to targets and indicators for the Convention on Biological Diversity and the Sustainable Development Goals and roles for national and international state and nonstate actors

From descriptive to predictive distribution models: a working example with Iberian amphibians and reptiles

BACKGROUND: Aim of the study was to identify the conditions under which spatial-environmental models can be used for the improved understanding of species distributions, under the explicit criterion of model predictive performance. I constructed distribution models for 17 amphibian and 21 reptile species in Portugal from atlas data and 13 selected ecological variables with stepwise logistic regression and a geographic information system. Models constructed for Portugal were extrapolated over Spain and tested against range maps and atlas data. RESULTS: Descriptive model precision ranged from 'fair' to 'very good' for 12 species showing a range border inside Portugal ('edge species', kappa (k) 0.35–0.89, average 0.57) and was at best 'moderate' for 26 species with a countrywide Portuguese distribution ('non-edge species', k = 0.03–0.54, average 0.29). The accuracy of the prediction for Spain was significantly related to the precision of the descriptive model for the group of edge species and not for the countrywide species. In the latter group data were consistently better captured with the single variable search-effort than by the panel of environmental data. CONCLUSION: Atlas data in presence-absence format are often inadequate to model the distribution of species if the considered area does not include part of the range border. Conversely, distribution models for edge-species, especially those displaying high precision, may help in the correct identification of parameters underlying the species range and assist with the informed choice of conservation measures

Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines

Indicators of river system hydromorphological character and dynamics: understanding current conditions and guiding sustainable river management

The work leading to this paper received funding from the EU’s FP7 programme under Grant Agreement No. 282656 (REFORM). The Indicators were developed within the context of REFORM deliverable D2.1, therefore all partners involved in this deliverable contributed to some extent to their discussion and development

Characterizing geomorphological change to support sustainable river restoration and management

The hydrology and geomorphology of most rivers has been fundamentally altered through a long history of human interventions including modification of river channels, floodplains, and wider changes in the landscape that affect water and sediment delivery to the river. Resultant alterations in fluvial forms and processes have negatively impacted river ecology via the loss of physical habitat, disruption to the longitudinal continuity of the river, and lateral disconnection between aquatic, wetland, and terrestrial ecosystems. Through a characterization of geomorphological change, it is possible to peel back the layers of time to investigate how and why a river has changed. Process rates can be assessed, the historical condition of rivers can be determined, the trajectories of past changes can be reconstructed, and the role of specific human interventions in these geomorphological changes can be assessed. To achieve this, hydrological, geomorphological, and riparian vegetation characteristics are investigated within a hierarchy of spatial scales using a range of data sources. A temporal analysis of fluvial geomorphology supports process-based management that targets underlying problems. In this way, effective, sustainable management and restoration solutions can be developed that recognize the underlying drivers of geomorphological change, the constraints imposed on current fluvial processes, and the possible evolutionary trajectories and timelines of change under different future management scenarios. Catchment/river basin planning, natural flood risk management, the identification and appraisal of pressures, and the assessment of restoration needs and objectives would all benefit from a thorough temporal analysis of fluvial geomorphology

Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10−9) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10−4-2.2 × 10−7. Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in genera