Rethinking Committee Work in the Research Enterprise: The Case of Regenerative Gatekeeping

Committees touch nearly every facet in the science, technology, engineering, and mathematics research enterprise. However, the role of gatekeeping through committee work has received little attention in Earth and space sciences. We propose a novel concept called, “regenerative gatekeeping” to challenge institutional inertia, cultivate belonging, accessibility, justice, diversity, equity, and inclusion in committee work. Three examples, a hiring committee process, a seminar series innovation, and an awards committee, highlight the need to self-assess policies and practices, ask critical questions and engage in generative conflict. Rethinking committee work can activate distributed mechanisms needed to promote change

Using Unoccupied Aerial Vehicles to Map and Monitor Changes in Emergent Kelp Canopy after an Ecological Regime Shift

Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy-forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource-focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management

Changing the culture of coastal, ocean, and marine sciences : strategies for individual and collective actions

Geoscience is plagued with structural and systemic barriers that prevent people of historically excluded groups from fully participating in, contributing to, and accruing the benefits of geosciences. A change in the culture of our learning and working environments is required to dismantle barriers and promote belonging, accessibility, justice, equity, diversity, and inclusion in our field. Inspired by a session organized at the 2020 Ocean Sciences Meeting, the goal of this paper is to provide a consolidated summary of a few innovative, broadening participation initiatives that are being led by various stakeholders in academia and research sectors (e.g., students, faculty, administrative leaders) at different institutional levels (e.g., universities, professional societies). The authors hope that the strategies outlined in this paper will inspire the coastal, ocean, and marine science community to take individual and collective actions that lead to a positive culture change

Integrating oceans into climate policy : any green new deal needs a splash of blue

Recent warnings from scientists suggest there is limited time to enact policies to avert wide‐ranging ecological and social damage from climate change. In the United States, discussions about comprehensive national policies to avert climate change have begun, with “Green New Deal” proposals and climate plans put forth by members of Congress and presidential candidates. Oceans are largely absent or separate from these nascent policy proposals. Here, we highlight a policy framework to develop terrestrial and ocean‐integrated policies that can complement and enhance terrestrial‐focused initiatives focused on four specific sectors: 1) energy; 2) transportation; 3) food security; and 4) habitat restoration. Given political friction and constrained budgets, an integrated policy framework offers greater potential to achieve a portfolio of mitigation and adaptation goals in a cost‐effective manner, beyond what could be realized with marine or terrestrial policy solutions alone

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce \u27soft windowing\u27, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Impact of predation by the invasive crab Hemigrapsus sanguineuson survival of juvenile blue mussels in western Long Island Sound

Hemigrapsus sanguineus (Asian Shore Crab) has shown a remarkable ability to colonize rocky intertidal communities along the east coast of the United States since its introduction in the late 1980s and is an important predator of juvenile Mytilus edulis (Blue Mussel) in invaded habitats. In this study, we used two field-caging experiments and the Kaplan-Meier model to assess the impact of predation by Asian Shore Crab on the survival of juvenile Blue Mussels in an intertidal habitat of western Long Island Sound along the Connecticut coastline. Five treatment levels (high-density enclosure, low-density enclosure, exclosure, partial cage, and open plot) were used in the 2007 experiment. The high-density enclosure treatment was omitted in the 2010 experiment since there was no statistically significant difference in the proportion of mussels surviving between low- and high-density crab treatments in 2007. In 2007, we measured a statistically significant difference in mussel mortality between exclosure and crab-enclosure cages, with crabs lowering the median survival time for mussels from 15.4 to 7.6 days. In 2010, we again measured a statistically significant difference in mussel mortality between exclosure and crab-enclosure cages, suggesting a crab effect on mussel survival. In the 2010 experiment, approximately 25% of the mussel mortality was attributable to crab predation, which reduced median survival time for mussels from 12.8 to 5.6 days. The median survival time for mussels exposed to the full complement of factors affecting survival (open plots and partial cages) was only 2–3 days. Our study shows that predation by Asian crabs may account for up to 25% of the Blue Mussel mortality in the intertidal zone at Black Rock Harbor. Further studies focusing on the importance of other biotic and abiotic factors are needed to understand the apparent declines in Blue Mussel populations and the interannual variability in recruitment success in this area

Shifting Thresholds: Rapid Evolution of Migratory Life Histories in Steelhead/Rainbow Trout, Oncorhynchus mykiss

International audienc

Marine Ecology Progress Series 415:247

ABSTRACT: Accurate efficient estimation of actual and potential species distribution is a critical requirement for effective ecosystem-based management and marine protected area design. In this study we tested the applicability of a terrestrial landscape modeling technique in a marine environment for predicting the distribution of ecologically and economically important groundfish, using 3 species of rockfish at Cordell Bank National Marine Sanctuary (CBNMS) as a model system. Autoclassification of multibeam bathymetry along with georeferenced submersible video transect data of the seafloor and demersal fishes were used to model the abundance and distribution of rockfish. Generalized linear models (GLMs) were created using habitat classification analyses of high-resolution (3 m) digital elevation models combined with fish presence/absence observations. Model accuracy was assessed using a reserved subset of the observation data. The resulting probability of occurrence models generated at 3 m resolution for the entire 120 km 2 study area proved reliable in predicting the distribution of all the species. The accuracies of the models for Sebastes rosaceus, S. flavidus and S. elongatus were 96, 92 and 92%, respectively. The probability of occurrence of S. flavidus and S. rosaceus was highest in the high relief rocky areas and lowest in the low relief, soft sediment areas. The model for S. elongatus had an opposite pattern, with the highest predicted probability of occurrence taking place in the low relief, soft sediment areas and a lower probability of occurrence in the rocky areas. These results indicate that site-specific and species-specific algorithmic habitat classification applied to high-resolution bathymetry data can be used to accurately extrapolate the results from in situ video surveys of demersal fishes across broad areas of habitat. KEY WORDS: Ecosystem-based management · Rockfish · Groundfish · GLMs · Marine protected area · Fishery management Resale or republication not permitted without written consent of the publisher Mar Ecol Prog Ser 415: [247][248][249][250][251][252][253][254][255][256][257][258][259][260][261] 2010 Traditional methods of distributional estimates have typically relied upon broad extrapolation from narrowly constrained or sparse data sets, or very costly intense sampling efforts carried out over broad areas (Margules & Austin 1991 In terrestrial environments, spatially explicit habitat suitability modeling has emerged as an efficient tool for generating accurate patterns and predictions of species abundance and distribution (Austin et al. 1994 More recently, the use of habitat modeling as a technique to estimate species distributions has been implemented in the marine environment (i.e. Along the west coast of North America, rockfish and other groundfish are the basis for broad-scale fisheries One method for acquiring visual observational data with spatial information that has become a standard method for quantifying rockfish is the use of a manned submersible Although spatially referenced visual methods have become useful in quantifying the abundance of groundfish species, extrapolating those observations to areas where no visual data exist must be based upon predictions of the character of unobserved habitats. As a result, the ability to create predictions of species occurrence from high resolution in situ visual surveys could be greatly enhanced when integrated with broad-scale seafloor landscape models derived from high-resolution multibeam bathymetry and sidescan sonar data. Combining broad-scale, high-resolution habitat data with spatially restricted observational data allows for multiscale habitat-based community assessments. Recent advances in the acquisition and processing of bathymetric and backscatter data now enable the classification of seafloor habitats with spatial resolutions on the order of a few meters to upwards of hundreds of meters The purpose of this study was to determine how well spatial predictive modeling techniques traditionally used in terrestrial systems, such as GLMs, can be used to create species-specific habitat models that predict the distribution of demersal fishes over broad areas. We assumed that, because demersal species are associated with specific seafloor habitat types in the marine environment just as ground dwelling species are in terrestrial environments, it is likely that the methods used to successfully predict species distribution in terrestrial environments could be applied to marine systems. Using Cordell Bank and 3 species of rockfish -yellowtail rockfish Sebastes flavidus, rosy rockfish S. rosaceus, and greenstriped rockfish S. elongatus -as a 248 Young et al.: Model prediction of rockfish occurrence model system, we created species-specific habitat suitability models using a combination of georeferenced video data and habitat parameters derived from highresolution topographic maps of Cordell Bank. The expectation was that predicted patterns of species distribution would correlate with actual observed species occurrence from video footage not used to parameterize the model. If successful, these types of models would be useful for helping to understand the relationships of species with their habitats and, in turn, could provide a tool for managers to characterize essential fish habitat, aiding in the implementation of marine spatial management and location of new MPAs. METHODS Natural history. The study site for this project is Cordell Bank, an 8 × 15 km granitic formation located 40 km west of Point Reyes, California (38.02°N, 123.44°W), with pinnacles coming to within 40 m of the water's surface and surrounding depths being greater than 350 m Data acquisition and processing. Multibeam and backscatter data were collected by the Seafloor Mapping Lab at California State University, Monterey Bay, aboard the RV 'VenTresca' at Cordell Bank National Marine Sanctuary, California, over a period of 8 d in After acquisition, the multibeam data were imported into CARIS HIPS software where they were corrected for the effects of attitude, tide and sound velocity, and all erroneous data soundings were removed using standard hydrographic data cleaning procedures (CARIS 2006). After cleaning, the data were exported as XYZ (easting, northing, depth) data at regularly spaced (3 m) intervals from CARIS and, once verified that there were no spikes or erroneous soundings remaining, the data were exported as a digital elevation model (DEM) in Environmental Systems Research Institute (ESRI) grid format for GIS analysis. A DEM is a raster data set that consists of elevation values at regularly spaced intervals. A comprehensive biological survey of Cordell Bank was conducted using the submersible vessel 'Delta' over 12 d in September and October of 2002. Sixty strip transects (2 m wide and 15 min in duration) were run across a variety of habitats ranging from 34 to 350 m in depth. During transects, the submersible remained within 1 to 2 m off the seafloor and traveled at speeds between 0.4 and 0.9 knots. All transects were videotaped with an externally mounted video camera, and the in situ observer's counts and descriptions were recorded and later transcribed in the laboratory. All fish within 2 m of the submersible were identified and counted. These presence/absence data were then converted into ESRI shapefile format for ArcGIS analysis. Habitat analysis. GIS landscape analyses were performed on the DEM to delineate those habitats that many species of rockfish are known to prefer, including rocky areas with high relief or areas of large boulders and stones. Within ArcGIS 9 (ESRI 1999 to 2006), various tools were used to derive 4 habitat descriptor rasters from the DEM that would be used in the models to delineate these habitat types and predict the occurrence of rockfish on Cordell Bank: these were depth, slope, aspect, vector ruggedness and topographic position index (TPI) rasters Vector ruggedness measure (VRM) grids were created using the Terrain Tools toolbox for ArcGIS From the VRM map, a 'distance to rock' raster was derived. The study area was classified into 'rock' and 'soft sediment' by choosing a cutoff value in the VRM of 0.001 and reclassifying the map. This cutoff value was chosen through trial and error and found to be the optimal value because it adequately separated the areas into the different substrate types. After classifying the map into rock and soft sediment, the raster was converted to polygons. From the polygons, the Euclidean distance tool was used to extrapolate out a distance-to-rock value for the entire extents of the study area. Distance to rock was expected to be a good predictor of the distribution of rockfish because, al-250 Young et al.: Model prediction of rockfish occurrence though they may not be found directly above a rock feature, they are usually found in close proximity The final habitat metric derived was topographic position index (TPI), which indicates the position of a given point relative to the overall surrounding landscape GLMs. The three species of rockfish chosen for this analysis, Sebastes flavidus, S. elongatus and S. rosaceus, were selected based on the types of habitat in which they are commonly found and because they are commercially and economically important. S. flavidus and S. rosaceus are commonly found in high-relief rocky habitats while S. elongatus is found in low-relief muddy habitats The presence and absence points were separated into training and evaluation data sets. Seventy percent of the presence and absence points were randomly selected for use in the training of the models while the remaining 30% were set aside as the 'observed' data set for evaluating the accuracy of model predictions. The absence data were also derived from the biological observation data. Points along transects where the species of interest was not observed were treated as absence points. The absence data were also dividedinto training (70%) and evaluation (30%) data sets. The presence and absence point location shapefiles were used together with the DEM and derived habitat parameter rasters in ArcGIS to create predictive models using the Marine Geospatial Ecology Toolbox (MGET) The GLM within MGET samples the values of each of the predictor rasters (e.g. slope, rugosity, depth) at each presence and absence point location and then uses those data to create prediction rasters that display the probability of species occurrence for unsampled locations using a binomial logistic regression model . A stepwise Akaike's information criterion (AIC) analysis selects the coefficients to determine the best fit model The GLM analyses were performed using the fish presence/absence and habitat raster data for each species separately, with the same general methods applied to each species. Each time the MGET GLM tool was used, the appropriate fish presence/absence point locations were specified along with the habitat parameter rasters of interest. These rasters included the bathymetric DEM and derived parameters such as aspect, slope, VRM and TPI at the broad and fine scale levels. Because results from preliminary models suggested that VRM and slope were correlated, these variables were not used together in any of the models. Separate models containing either VRM or slope were tested to determine which model was a more effective predictor of fish distribution in each case and the best model was then chosen for analysis. The GLM tool produces a predictive map of species occurrence based on the model and input raster layers, which is converted to ArcGIS format for visualization and further analysis. Once completed, each of the models was tested for their predictive capabilities by comparing the predicted occurrence of fish to the observed presence and absence points (the 30% of the observational data set aside for model evaluation and not used in the creation of the model). Model validation. To test model accuracy, Cohen's Kappa values were calculated for each of the speciesspecific models to determine the agreement between the predicted and observed presence and absence values. Cohen's Kappa is a statistical test that measures the agreement between categorical terms. It is similar to percent agreement but is more robust in that it takes into account agreement occurring by chance A basic assumption of GLMs is independence in the residual errors The R statistical package was used to generate spatial correlograms using Moran's I coefficients to test whether the GLMs used in this study were influenced by SA. In particular, the spatial locations of the fish presence/absence points used to create the model (the response variables) were tested against the habitat parameters for the occurrence of SA. Moran's I measures how similar samples of a given variable are over varying spatial distances and usually ranges from -1.0 to 1.0, where negative values represent negative spatial autocorrelation and positive values represent positive spatial autocorrelation. A Moran's I value of '0' indicates no spatial autocorrelation. After creating the correlograms, an ANOVA was used to determine whether the observed spatial autocorrelation was significant. Comparison between GLMs and autologistic regression (ALR). If significant spatial autocorrelation was found, an ALR model was run on the response variables to determine whether there was a significant change in the explanatory power of the model coefficients RESULTS Model accuracies As expected, the GLM for Sebastes flavidus predicted the highest probability of occurrence in rocky high-relief habitats and lower probability in low-relief, soft sediment habitats Comparison of the evaluation data set to model predictions resulted in 69% agreement for presence and 97% for absence locations. The overall accuracy of the model was 92% with a statistically significant Cohen's Kappa Mar Ecol Prog Ser 415 Spatial autocorrelation Spatial autocorrelation was significant for all variables except slope and aspect in the models for Sebas- the ALR showed that, although spatial autocorrelation was reduced at the finer scales, the reduction was not substantial and it did not completely eliminate the spatial autocorrelation for Sebastes flavidus and S. rosaceus. For S. elongatus, the level of spatial autocorrelation was very similar whether autocovariance was included as a term in the model or not Comparison of GLM and ALR models The GLM and ALR models differed for Sebastes flavidus and S. rosaceus but remained similar for S. elongatus. For S. flavidus, all the variables found to be significant in the GLM became insignificant when autocovariance was included as a term in the ALR model. Distance to rock and the 30 m TPI were nearly significant, but only the autocovariance term was found to be a significant predictor in the ALR In contrast to the other 2 species, the GLM and ALR for Sebastes elongatus remained very similar. All the coefficients remained similar and the significance of the variables changed by only a negligible amount DISCUSSION Evaluation of modeling methods The application of GLMs to high-resolution seafloor terrain data proved to be a highly efficient and effective method for creating accurate, species-specific habitat maps for those rockfish species at Cordell Bank. Previous studies have shown that rockfish prefer sloping terrain In addition, the VRM analysis gives information on the amount of available rocky substrate. Because slope and VRM are correlated, the stronger predictor of fish presence/absence was used as a predictor in the models. As shown in other studies In all the models, either depth 2 or a combination of depth and depth 2 were important variables for explaining the distribution of the 3 species of rockfish used in this study. This result is consistent with other studies in which both the abundance and number of rockfish species increase with depth between 151 and 250 m and then decrease below this range The TPI analyses showed the difference in habitat types when analyzed at differing scales. The fine scale analyses had fewer habitat classes than did the broad scale and the habitat patches were much smaller. When included in the models, Sebastes flavidus was closely associated with the fine-scale habitats while S. elongatus was more closely associated with the broadscale habitats. Depending on the species of fish, either broad or fine scale TPI might better explain and predict a species distribution. Regardless of scale, the TPI analysis does capture features that are important to the distribution of species, which is similar to results from other studies Distance to rock was a significant predictor for the occurrence of Sebastes flavidus and S. rosaceus found in the high-relief rocky habitats. Although these species may not be found directly on top of the rocky habitat, they are found in close proximity to it. This result agrees with those found in other studies where proximity to peaks was found to be a good predictor of rockfish occurrence Another potential flaw with the Sebastes elongatus model could be the small number of observations recorded for this species. Although not applied here, different methods, such as ordination, may be more suitable in situations where very few presence points exist. This method is based on reciprocal averaging of species and site scores and is better at dealing with data that have many absences or zeroes Spatial autocorrelation The analysis of spatial autocorrelation revealed that all variables were spatially autocorrelated, especially at the finer spatial scales. All of the variables followed the same general pattern with positive spatial autocorrelation at the finer scales and then fluctuations between positive and negative spatial autocorrelation. This pattern shows that these 3 species of rockfish and the variables they are associated with have a patchy distribution up to about 500 m and then a more random distribution at the larger spatial scales, which shows that rockfish are associated with certain types of habitats and are not found randomly throughout Cordell Bank. Although, in the past, spatial autocorrelation in ecological data was believed to inflate Type I errors and increase bias in statistical analyses, new studies are showing that it does not necessarily cause bias (DinizFilho et al. 2003). However, spatial autocorrelation analysis should still be conducted as it can serve as an important method for elucidating the mechanisms affecting the spatial structure of populations (DinizFilho et al. 2003). To account for spatial autocorrelation we ran ALR models, which include autocovariance as a variable and, therefore, account for the spatial autocorrelation in the data. In some cases, the ALR caused some of the variables found important to the distribution of fish in the GLMs to become insignificant. The comparison between the models (i.e. AIC) showed that the ALR models were more robust; however, because we could not predict the autocovariance over the entire study area, we could not create a predictive distribution grid and were unable to compare the predictive power of the models using an evaluation data set. In future studies, more exploration into the use of kriging to create a map of spatial autocorrelation over the extent of the study area would allow for the creation of predictive distribution maps that include spatial autocorrelation as a variable (Augustin et al. 1996 Limitations of approach A potential problem with the models used in this study is that they rely only on indirect predictor variables (e.g. slope, VRM, topographic position, distance to rock), which have no direct physiological relevance for species' performance. Relying solely on indirect variables tends to limit the geographical range across which a model can be applied successfully. Therefore, these models could be improved if resource variables (e.g. nutrients, food) and other direct variables (e.g. temperature, pH) were incorporated into the GLMs rather than simply relying on benthic habitat features Another important variable that these models do not take into account is fishing pressure, a factor that can limit the geographical extent of a given model's applic-258 Young et al.: Model prediction of rockfish occurrence ability. A model that is developed in a protected area where little fishing effort occurs could adequately define suitable habitat and accurately predict the presence/absence of fish. However, if the same model were transferred to a location of higher fishing pressure, the model may predict suitable habitat well but not actual presence. This study only explored the use of GLMs to determine the probability of occurrence of the rockfish species. Another type of model that could potentially provide more robust predictions is the GAM. GAMs are similar to GLMs in that they can be used with data that are nonlinear and have nonconstant variance Finally, these models are based on a 'snapshot' of habitat use by these fish species. More robust models in the future will include movement data and provide more understanding of the scale at which species are associated with their habitat, as well as their habitat use over longer time periods and how often they move between different habitat types. Acoustic tracking of fish will enable more robust modeling approaches to predicting the distribution of fish. The combination of these limitations can make it difficult to transfer these spatially explicit models to other locations to predict the presence/absence of fish. However, Implications for management Despi