Recent pace of change in human impact on the world's ocean.

Humans interact with the oceans in diverse and profound ways. The scope, magnitude, footprint and ultimate cumulative impacts of human activities can threaten ocean ecosystems and have changed over time, resulting in new challenges and threats to marine ecosystems. A fundamental gap in understanding how humanity is affecting the oceans is our limited knowledge about the pace of change in cumulative impact on ocean ecosystems from expanding human activities - and the patterns, locations and drivers of most significant change. To help address this, we combined high resolution, annual data on the intensity of 14 human stressors and their impact on 21 marine ecosystems over 11 years (2003-2013) to assess pace of change in cumulative impacts on global oceans, where and how much that pace differs across the ocean, and which stressors and their impacts contribute most to those changes. We found that most of the ocean (59%) is experiencing significantly increasing cumulative impact, in particular due to climate change but also from fishing, land-based pollution and shipping. Nearly all countries saw increases in cumulative impacts in their coastal waters, as did all ecosystems, with coral reefs, seagrasses and mangroves at most risk. Mitigation of stressors most contributing to increases in overall cumulative impacts is urgently needed to sustain healthy oceans

A Coalescent Sampler Successfully Detects Biologically Meaningful Population Structure Overlooked by F‐Statistics

Assessing the geographic structure of populations has relied heavily on Sewell Wright\u27s F‐statistics and their numerous analogues for many decades. However, it is well appreciated that, due to their nonlinear relationship with gene flow, F statistics frequently fail to reject the null model of panmixia in species with relatively high levels of gene flow and large population sizes. Coalescent genealogy samplers instead allow a model‐selection approach to the characterization of population structure, thereby providing the opportunity for stronger inference. Here, we validate the use of coalescent samplers in a high gene flow context using simulations of a stepping‐stone model. In an example case study, we then re‐analyze genetic datasets from 41 marine species sampled from throughout the Hawaiian archipelago using coalescent model selection. Due to the archipelago\u27s linear nature, it is expected that most species will conform to some sort of stepping‐stone model (leading to an expected pattern of isolation by distance), but F‐statistics have only supported this inference in ~10% of these datasets. Our simulation analysis shows that a coalescent sampler can make a correct inference of stepping‐stone gene flow in nearly 100% of cases where gene flow is ≤100 migrants per generation (equivalent to FST = 0.002), while F‐statistics had mixed results. Our re‐analysis of empirical datasets found that nearly 70% of datasets with an unambiguous result fit a stepping‐stone model with varying population sizes and rates of gene flow, although 37% of datasets yielded ambiguous results. Together, our results demonstrate that coalescent samplers hold great promise for detecting weak but meaningful population structure, and defining appropriate management units

Evolving coral reef conservation with genetic information

Targeted conservation and management programs are crucial for mitigating anthropogenic threats to declining biodiversity. Although evolutionary processes underpin extant patterns of biodiversity, it is uncommon for resource managers to explicitly consider genetic data in conservation prioritization. Genetic information is inherently relevant to management because it describes genetic diversity, population connectedness, and evolutionary history; thereby typifying their behavioral traits, physiological climate tolerance, evolutionary potential, and dispersal ability. Incorporating genetic information into spatial conservation prioritization starts with reconciling the terminology and techniques used in genetics and conservation science. Genetic data vary widely in analyses and their interpretations can be challenging even for experienced geneticists. Therefore, identifying objectives, decision rules, and implementations in decision support tools specifically for management using genetic data is challenging. Here, we outline a framework for eight genetic system characteristics, their measurement, and how they could be incorporated in spatial conservation prioritization for two contrasting objectives: biodiversity preservation vs maintaining ecological function and sustainable use. We illustrate this framework with an example using data from Tridacna crocea (Lamarck, 1819) (boring giant clam) in the Coral Triangle. We find that many reefs highlighted as conservation priorities with genetic data based on genetic subregions, genetic diversity, genetic distinctness, and connectivity are not prioritized using standard practices. Moreover, different characteristics calculated from the same samples resulted in different spatial conservation priorities. Our results highlight that omitting genetic information from conservation decisions may fail to adequately represent processes regulating biodiversity, but that conservation objectives related to the choice of genetic system characteristics require careful consideration

Diversity from genes to ecosystems : a unifying framework to study variation across biological metrics and scales

This work was assisted through participation in “Next Generation Genetic Monitoring” Investigative Workshop at the National Institute for Mathematical and Biological Synthesis, sponsored by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Hawaiian fish community data were provided by the NOAA Pacific Islands Fisheries Science Center's Coral Reef Ecosystem Division (CRED) with funding from NOAA Coral Reef Conservation Program. O.E.G. was supported by the Marine Alliance for Science and Technology for Scotland (MASTS). A. C. and C. H. C. were supported by the Ministry of Science and Technology, Taiwan. P.P.-N. was supported by a Canada Research Chair in Spatial Modelling and Biodiversity. K.A.S. was supported by National Science Foundation (BioOCE Award Number 1260169) and the National Center for Ecological Analysis and Synthesis. All data used in this manuscript are available in DRYAD (https://doi.org/dx.doi.org/10.5061/dryad.qm288) and BCO-DMO (http://www.bco-dmo.org/project/552879).Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organisation (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap we present a unifying framework for the measurement of biodiversity across hierarchical levels of organisation. Our weighted, information-based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon’s entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.) we applied the framework to a real dataset on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics, and eco-evolutionary dynamics.Publisher PDFPeer reviewe

Knowledge sharing about deep-sea ecosystems to inform conservation and research decisions

The Marianas Trench Marine National Monument (MNM) currently extends policy-based protection to deep-sea ecosystems contained within it, but managers require better understanding of the current knowledge and knowledge gaps about these ecosystems to guide decision-making. To address this need, we present a case study of the Marianas Trench MNM using in-depth interviews to determine scientists’ (1) current understanding of anthropogenic drivers of change and system vulnerability in deep-sea ecosystems; and (2) perceptions of the least understood deep-sea ecosystems and processes in the Marianas Trench MNM, and which of these, if any, should be research priorities to fill knowledge gaps about these systems and the impacts from anthropogenic drivers of change. Interview respondents shared similar views on the current knowledge of deep-sea ecosystems and potential anthropogenic drivers of change in the Marianas Trench MNM. Respondents also identified trench and deep pelagic (bathyal, abyssal, and hadal zones) ecosystems as the least understood, and highlighted climate change, litter and waste, mining and fishing, and interactions between these drivers of change as critical knowledge gaps. To fill key knowledge gaps and inform conservation decision-making, respondents identified the need for monitoring networks and time-series data. Our approach demonstrates how in-depth interviews can be used to elicit knowledge to inform decision-making in data-limited situations

Neuropathogenic Forms of Huntingtin and Androgen Receptor Inhibit Fast Axonal Transport

AbstractHuntington's and Kennedy's disease are autosomal dominant neurodegenerative diseases caused by pathogenic expansion of polyglutamine tracts. Expansion of glutamine repeats must in some way confer a gain of pathological function that disrupts an essential cellular process and leads to loss of affected neurons. Association of huntingtin with vesicular structures raised the possibility that axonal transport might be altered. Here we show that polypeptides containing expanded polyglutamine tracts, but not normal N-terminal huntingtin or androgen receptor, directly inhibit both fast axonal transport in isolated axoplasm and elongation of neuritic processes in intact cells. Effects were greater with truncated polypeptides and occurred without detectable morphological aggregates

Advancing the integration of spatial data to map human and natural drivers on coral reefs

<div><p>A major challenge for coral reef conservation and management is understanding how a wide range of interacting human and natural drivers cumulatively impact and shape these ecosystems. Despite the importance of understanding these interactions, a methodological framework to synthesize spatially explicit data of such drivers is lacking. To fill this gap, we established a transferable data synthesis methodology to integrate spatial data on environmental and anthropogenic drivers of coral reefs, and applied this methodology to a case study location–the Main Hawaiian Islands (MHI). Environmental drivers were derived from time series (2002–2013) of climatological ranges and anomalies of remotely sensed sea surface temperature, chlorophyll-<i>a</i>, irradiance, and wave power. Anthropogenic drivers were characterized using empirically derived and modeled datasets of spatial fisheries catch, sedimentation, nutrient input, new development, habitat modification, and invasive species. Within our case study system, resulting driver maps showed high spatial heterogeneity across the MHI, with anthropogenic drivers generally greatest and most widespread on O‘ahu, where 70% of the state’s population resides, while sedimentation and nutrients were dominant in less populated islands. Together, the spatial integration of environmental and anthropogenic driver data described here provides a first-ever synthetic approach to visualize how the drivers of coral reef state vary in space and demonstrates a methodological framework for implementation of this approach in other regions of the world. By quantifying and synthesizing spatial drivers of change on coral reefs, we provide an avenue for further research to understand how drivers determine reef diversity and resilience, which can ultimately inform policies to protect coral reefs.</p></div

Parsing human and biophysical drivers of coral reef regimes

Coral reefs worldwide face unprecedented cumulative anthropogenic effects of interacting local human pressures, global climate change and distal social processes. Reefs are also bound by the natural biophysical environment within which they exist. In this context, a key challenge for effective management is understanding how anthropogenic and biophysical conditions interact to drive distinct coral reef configurations. Here, we use machine learning to conduct explanatory predictions on reef ecosystems defined by both fish and benthic communities. Drawing on the most spatially extensive dataset available across the Hawaiian archipelago-20 anthropogenic and biophysical predictors over 620 survey sites-we model the occurrence of four distinct reef regimes and provide a novel approach to quantify the relative influence of human and environmental variables in shaping reef ecosystems. Our findings highlight the nuances of what underpins different coral reef regimes, the overwhelming importance of biophysical predictors and how a reef's natural setting may either expand or narrow the opportunity space for management interventions. The methods developed through this study can help inform reef practitioners and hold promises for replication across a broad range of ecosystems. © 2019 The Author(s

Ocean currents help explain population genetic structure

Management and conservation can be greatly informed by considering explicitly how environmental factors influence population genetic structure. Using simulated larval dispersal estimates based on ocean current observations, we demonstrate how explicit consideration of frequency of exchange of larvae among sites via ocean advection can fundamentally change the interpretation of empirical population genetic structuring as compared with conventional spatial genetic analyses. Both frequency of larval exchange and empirical genetic difference were uncorrelated with Euclidean distance between sites. When transformed into relative oceanographic distances and integrated into a genetic isolation-by-distance framework, however, the frequency of larval exchange explained nearly 50 per cent of the variance in empirical genetic differences among sites over scales of tens of kilometres. Explanatory power was strongest when we considered effects of multiple generations of larval dispersal via intermediary locations on the long-term probability of exchange between sites. Our results uncover meaningful spatial patterning to population genetic structuring that corresponds with ocean circulation. This study advances our ability to interpret population structure from complex genetic data characteristic of high gene flow species, validates recent advances in oceanographic approaches for assessing larval dispersal and represents a novel approach to characterize population connectivity at small spatial scales germane to conservation and fisheries management