Prediction of mesophotic coral distributions in the Au‘au Channel, Hawaii

The primary objective of this study was to predict the distribution of mesophotic hard corals in the Au‘au Channel in the Main Hawaiian Islands (MHI). Mesophotic hard corals are light-dependent corals adapted to the low light conditions at approximately 30 to 150 m in depth. Several physical factors potentially influence their spatial distribution, including aragonite saturation, alkalinity, pH, currents, water temperature, hard substrate availability and the availability of light at depth. Mesophotic corals and mesophotic coral ecosystems (MCEs) have increasingly been the subject of scientific study because they are being threatened by a growing number of anthropogenic stressors. They are the focus of this spatial modeling effort because the Hawaiian Islands Humpback Whale National Marine Sanctuary (HIHWNMS) is exploring the expansion of its scope—beyond the protection of the North Pacific Humpback Whale (Megaptera novaeangliae)—to include the conservation and management of these ecosystem components. The present study helps to address this need by examining the distribution of mesophotic corals in the Au‘au Channel region. This area is located between the islands of Maui, Lanai, Molokai and Kahoolawe, and includes parts of the Kealaikahiki, Alalākeiki and Kalohi Channels. It is unique, not only in terms of its geology, but also in terms of its physical oceanography and local weather patterns. Several physical conditions make it an ideal place for mesophotic hard corals, including consistently good water quality and clarity because it is flushed by tidal currents semi-diurnally; it has low amounts of rainfall and sediment run-off from the nearby land; and it is largely protected from seasonally strong wind and wave energy. Combined, these oceanographic and weather conditions create patches of comparatively warm, calm, clear waters that remain relatively stable through time. Freely available Maximum Entropy modeling software (MaxEnt 3.3.3e) was used to create four separate maps of predicted habitat suitability for: (1) all mesophotic hard corals combined, (2) Leptoseris, (3) Montipora and (4) Porites genera. MaxEnt works by analyzing the distribution of environmental variables where species are present, so it can find other areas that meet all of the same environmental constraints. Several steps (Figure 0.1) were required to produce and validate four ensemble predictive models (i.e., models with 10 replicates each). Approximately 2,000 georeferenced records containing information about mesophotic coral occurrence and 34 environmental predictors describing the seafloor’s depth, vertical structure, available light, surface temperature, currents and distance from shoreline at three spatial scales were used to train MaxEnt. Fifty percent of the 1,989 records were randomly chosen and set aside to assess each model replicate’s performance using Receiver Operating Characteristic (ROC), Area Under the Curve (AUC) values. An additional 1,646 records were also randomly chosen and set aside to independently assess the predictive accuracy of the four ensemble models. Suitability thresholds for these models (denoting where corals were predicted to be present/absent) were chosen by finding where the maximum number of correctly predicted presence and absence records intersected on each ROC curve. Permutation importance and jackknife analysis were used to quantify the contribution of each environmental variable to the four ensemble models

Pacific Portraits: The People Behind the Scenes at Pacific University (Volume One)

When a dormitory toilet is clogged, who’s the guy charged with fixing it? Who assures that benefits and work-study monies are paid and accounted for on time? And who is tasked with ensuring Luau goes off without a hitch or that students from Saudi Arabia know how to navigate the cultural idiosyncrasies of an American university? Meet the people who work behind the scenes at Pacific University—the community of staff and faculty—as captured by Pacific’s own creative writing and photography students. Their jobs and lives are varied, but their dedication to ensuring a dynamic educational experience in all its varieties is common between them. This book strives to capture and share their stories through the creative efforts of the students their work serves.https://commons.pacificu.edu/beetree/1001/thumbnail.jp

Anti-inflammatory activities of Coleus forsteri (formerly Plectranthus forsteri) extracts on human macrophages and chemical characterization

International audienceIntroduction: Formerly named Plectranthus forsteri , Coleus forsteri (Benth.) A.J.Paton, 2019 is a Lamiaceae traditionally used to treat flu-like symptoms and shock-related ecchymosis, especially in the Pacific region. Few studies investigated chemical composition and anti-inflammatory potential of this plant. Method: Herein, we investigated anti-inflammatory potential of C. forsteri ethanolic (ePE) and cyclohexane (cPE) plant extract on LPS-induced human macrophages models and quantified cytokines and quinolinic acid (QUIN) as inflammatory markers. Results: Our results show that extract of ePE and cPE significantly inhibit inflammatory cytokine IL-6 and TNF-α induced by LPS on PMA-derived THP-1 macrophages. QUIN production is also diminished under ePE and cPE treatment in activated human monocyte-derived macrophages (MDMs). Seven abietane diterpenes were characterized from C. forsteri cPE including coleon U ( 1 ), coleon U-quinone ( 2 ), 8α,9α-epoxycoleon U-quinone ( 3 ), horminone or 7α-hydroxyroyleanone ( 4 ), 6β,7α-dihydroxyroyleanone ( 5 ), 7α-acetoxy-6β-hydroxyroyleanone ( 6 ) and 7α-formyloxy-6β-hydroxyroyleanone ( 7 ). Discussion: We discussed potential contributions of these molecules from C. forsteri extracts for their anti-inflammatory activities

National Oceanic and Atmospheric Administration Report 149

From about this document: This report describes the development and assessment of four spatially explicit predictive models describing mesophotic coral presence in the Au'au Channel Region in the Main Hawaiian Islands

Identifying Suitable Locations for Mesophotic Hard Corals Offshore of Maui, Hawai'i.

Mesophotic hard corals (MHC) are increasingly threatened by a growing number of anthropogenic stressors, including impacts from fishing, land-based sources of pollution, and ocean acidification. However, little is known about their geographic distributions (particularly around the Pacific islands) because it is logistically challenging and expensive to gather data in the 30 to 150 meter depth range where these organisms typically live. The goal of this study was to begin to fill this knowledge gap by modelling and predicting the spatial distribution of three genera of mesophotic hard corals offshore of Maui in the Main Hawaiian Islands. Maximum Entropy modeling software was used to create separate maps of predicted probability of occurrence and uncertainty for: (1) Leptoseris, (2) Montipora, and (3) Porites. Genera prevalence was derived from the in situ presence/absence data, and used to convert relative habitat suitability to probability of occurrence values. Approximately 1,300 georeferenced records of the occurrence of MHC, and 34 environmental predictors were used to train the model ensembles. Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) values were between 0.89 and 0.97, indicating excellent overall model performance. Mean uncertainty and mean absolute error for the spatial predictions ranged from 0.006% to 0.05% and 3.73% to 17.6%, respectively. Depth, distance from shore, euphotic depth (mean and standard deviation) and sea surface temperature (mean and standard deviation) were identified as the six most influential predictor variables for partitioning habitats among the three genera. MHC were concentrated between Hanaka'ō'ō and Papawai Points offshore of western Maui most likely because this area hosts warmer, clearer and calmer water conditions almost year round. While these predictions helped to fill some knowledge gaps offshore of Maui, many information gaps remain in the Hawaiian Archipelago and Pacific Islands. This approach may be used to identify other potentially suitable areas for MHCs, helping scientists and resource managers prioritize sites, and focus their limited resources on areas that may be of higher scientific or conservation value

A Phylogeographic Description of <i>Histoplasma capsulatum</i> in the United States

Histoplasmosis is one of the most under-diagnosed and under-reported endemic mycoses in the United States. Histoplasma capsulatum is the causative agent of this disease. To date, molecular epidemiologic studies detailing the phylogeographic structure of H. capsulatum in the United States have been limited. We conducted genomic sequencing using isolates from histoplasmosis cases reported in the United States. We identified North American Clade 2 (NAm2) as the most prevalent clade in the country. Despite high intra-clade diversity, isolates from Minnesota and Michigan cases were predominately clustered by state. Future work incorporating environmental sampling and veterinary surveillance may further elucidate the molecular epidemiology of H. capsulatum in the United States and how genomic sequencing can be applied to the surveillance and outbreak investigation of histoplasmosis

Maps of environmental predictors.

<p>These maps show the 10x10 m predictors that were used to develop MaxEnt models and spatial predictions for Montipora, Leptoseris and Porites. Asterisks denote predictors that were included at multiple spatial scales. The red inset boxes show fine-scale detail for predictors that are difficult to see.</p

Test AUC values for <i>Montipora</i>, <i>Leptoseris</i> and <i>Porites</i>.

<p>Test AUC values computed by MaxEnt using the 50% out-of-bag presences and background points, and a separate AUC computation in R using true absences and the same 50% out-of-bag presences used by MaxEnt.</p

Uncertainty and error associated with MHC predictions.

<p>These maps show the spatial uncertainty (i.e., standard error) and spatial errors (i.e., difference between observed and predicted values) associated with <i>Montipora</i>, <i>Leptoseris</i> and <i>Porites</i> spatial predictions averaged across the 10 model replicates. Errors were divided into classes based on the MAE, and summarized by ROV transect for display purposes. Each +/- symbol on the map denotes the mean error along a single ROV transect.</p

Kriging parameters.

<p>The input parameters used to develop 10x10 m surfaces for euphotic depth and sea surface temperature using ordinary kriging. These parameters minimized the root mean square error of the final surfaces.</p