Matthew S. Kendall

Multi-scale Data Used to Analyze the Spatial Distribution of French Grunts, Haemulon Flavolineatum, Relative to Hard and Soft Bottom in a Benthic Landscape

We evaluated the day-time distribution of juvenile and adult French grunts, Haemulon flavolineatum, relative to the spatial configuration of hard and soft bottom areas in a benthic landscape. Probability of juvenile presence on hard bottom sites was inversely correlated with distance to soft bottom. Adults presence at hard bottom sites showed no significant relationship with distance to soft bottom. A significant and positive relationship was found between presence of juveniles on hard bottom sites and area of soft bottom within 100 m, but no significant relationship was found for area of soft bottom within 500 m. Adults exhibited no significant relationship with area of soft bottom for either distance tested. These distributions are suspected to be the result of the combined influence of larval settlement patterns and foraging behaviors associated with hard and soft bottom. This study indicates that data collected at very fine scales can be analyzed in the context of the broad-scale mosaic of habitats in the benthic landscape to predict patterns of fish distribution. Such spatially explicit conclusions are not possible through analysis of fine-scale or broad-scale data alone.

The influence of male mating history on male–male competition and female choice in mating associations in the blue crab, Callinectes sapidus (Rathbun)

Male Callinectes sapidus allowed complete recovery of sperm resources and then mated a single time had significantly lower vas deferens weight than males allowed complete recovery of sperm resources but prevented from mating. In laboratory experiments, when a recently mated male (having low sperm volume) competed with a male that had not recently mated (having high sperm volume) for a single pubertal female, the female was just as likely to initiate pairing with the recently mated male as with a male that had not recently mated, despite possible reduction in her fertilization potential. At the end of trials in which stable pair formation occurred, recently mated males were paired significantly more often than males that had not recently mated. The combined effects of the lack of mate choice by females and high mating frequency of some males may result in many females in the population receiving low quantities of sperm.

Seascape Models Reveal Places to Focus Coastal Fisheries Management

To design effective marine reserves and support fisheries, more information on fishing patterns and impacts for targeted species is needed, as well as better understanding of their key habitats. However, fishing impacts vary geographically and are difficult to disentangle from other factors that influence targeted fish distributions. We developed a set of fishing effort and habitat layers at high resolution and employed machine learning techniques to create regional-scale seascape models and predictive maps of biomass and body length of targeted reef fishes for the main Hawaiian Islands. Spatial patterns of fishing effort were shown to be highly variable and seascape models indicated a low threshold beyond which targeted fish assemblages were severely impacted. Topographic complexity, exposure, depth, and wave power were identified as key habitat variables that influenced targeted fish distributions and defined productive habitats for reef fisheries. High targeted reef fish biomass and body length were found in areas not easily accessed by humans, while model predictions when fishing effort was set to zero showed these high values to be more widely dispersed among suitable habitats. By comparing current targeted fish distributions with those predicted when fishing effort was removed, areas with high recovery potential on each island were revealed, with average biomass recovery of 517% and mean body length increases of 59% on Oahu, the most heavily fished island. Spatial protection of these areas would aid recovery of nearshore coral reef fisheries.

Managers, modelers, and measuring the impact of species distribution model uncertainty on marine zoning decisions

Marine managers routinely use spatial data to make decisions about their marine environment. Uncertainty associated with this spatial data can have profound impacts on these management decisions and their projected outcomes. Recent advances in modeling techniques, including species distribution models (SDMs), make it easier to generate continuous maps showing the uncertainty associated with spatial predictions and maps. However, SDM predictions and maps can be complex and nuanced. This complexity makes their use challenging for non-technical managers, preventing them from having the best available information to make decisions. To help bridge these communication and information gaps, we developed maps to illustrate how SDMs and associated uncertainty can be translated into readily usable products for managers. We also explicitly described the potential impacts of uncertainty on marine zoning decisions. This approach was applied to a case study in Saipan Lagoon, Commonwealth of the Northern Mariana Islands (CNMI). Managers in Saipan are interested in minimizing the potential impacts of personal watercraft (e.g., jet skis) on staghorn Acropora (i.e., Acropora aspera, A. formosa, and A. pulchra), which is an important coral assemblage in the lagoon. We used a recently completed SDM for staghorn Acropora to develop maps showing the sensitivity of zoning options to three different prediction and three different uncertainty thresholds (nine combinations total). Our analysis showed that the amount of area and geographic location of predicted staghorn Acropora presence changed based on these nine combinations. These dramatically different spatial patterns would have significant zoning implications when considering where to exclude and/or allow jet skis operations inside the lagoon. They also show that different uncertainty thresholds may lead managers to markedly different conclusions and courses of action. Defining acceptable levels of uncertainty upfront is critical for ensuring that managers can make more informed decisions, meet their marine resource goals and generate favorable outcomes for their stakeholders.

Marine biogeographic assessment of the main Hawaiian Islands : a collaborative investigation

An understanding of the distribution of marine benthic habitats and associated biota in the Main Hawaiian Islands (MHI) is necessary in order to assess potential direct and indirect effects of renewable energy development. Benthic habitats in the MHI can be divided into three broad categories based on their depth: shallow (<30 m), mesophotic (30-150 m) and deep (>150 m). Shallow-coral reefs provide numerous natural and economic benefits to the state’s economy and are much better studied than mesophotic and deep-water coral reefs. Approximately 75 percent of the shallow-water (<30 m) area around the MHI has been characterized using satellite imagery, although the percentage varies by island, with less area mapped around Hawaiʻi and the windward sides of Maui and Kahoʻolawe. Seventeen datasets from shallow reef monitoring programs were compiled into a standardized database of benthic cover. A qualitative assessment of the data indicates that percent cover of major benthic taxonomic groups (e.g., live coral, macroalgae) varies at both the island and local scales, with coral cover generally lower around the most northwestern islands. Recently published spatial predictive models of mesophotic hard coral distributions in the ʻAuʻau Channel provided maps of probability of occurrence for Leptoseris spp., Montipora spp. and Porites spp. These models were created using presence and absence records for these genera and a suite of environmental predictor variables. Probability of occurrence for mesophotic hard corals was highest in the warmer, clearer, and calmer waters off the western coast of Maui between Hanakaoʻo Point and Papawai Point. Although less data is available in deeper habitats, a variety of deep-sea corals (DSC) have been documented in the Hawaiian Archipelago. Using presence-only data and a suite of environmental predictor variables, spatial predictive models were created for eighteen DSC groups to identify areas most likely to contain deep-sea coral habitat around the MHI. The distributions of DSC presence records varied among groups; however, records were often concentrated in particular locations, such as Cross Seamount, Makapuʻu Point, Makalawena Bank, Lō‘ihi Seamount, and the southern edge of Penguin Bank. Areas predicted to contain highly suitable DSC habitat broadly aligned with the locations of DSC presence records. The environmental variables of depth, distance to shore and slope were consistently the most important predictors across all models. For both mesophotic corals and DSC, model results can be used to guide future exploration and research, particularly in areas where few records exist. 1 NOAA National Centers for Coastal Ocean Science, Biogeography Branch, Silver Spring, MD, U.S.A. 3 CSS-Dynamac, Fairfax, VA, U.S.A. 6 NOAA Office of National Marine Sanctuaries, Pahānaumokuākea Marine National Monument, Honolulu, HI, U.S.A. 7 NOAA Pacific Islands Fisheries Science Center, Protected Species Division, Honolulu, HI, U.S.A. 8 University of Hawaiʻi at Mānoa, Fisheries Ecology Research Lab, Hawaiʻi, U.S.A. Photo credit: Kurt Kawamoto (NOAA NMFS/PIFSC)

Reef Fish - Chapter 4 Fishes. In Marine Biogeographic Assessment of the Main Hawaiian Islands

15 NOAA Southwest Fisheries Science Center, Marine Mammal and Turtle Division, CA, U.S.A. 16 University of Hawaiʻi at Mānoa, Joint Institute for Marine and Atmospheric Research, HI, U.S.A. 17 University of Hawaiʻi at Mānoa, HI, U.S.A. 18 NOAA Pacific Islands Fisheries Science Center, Protected Species Division, Cetacean Research Program, Honolulu, HI, U.S.A. 19 Nicholas School of the Environment, Duke University, NC, U.S.A. 20 NOAA Alaskan Fisheries Science Center, National Marine Mammal Laboratory, WA, U.S.A. 21 NOAA Pacific Islands Fisheries Science Center, Protected Species Division, Hawaiian Monk Seal Research Program, Honolulu, HI, U.S.A. ABSTRACT Marine mammals are ecologically, economically and culturally important to Hawaiʻi. Reliable information on species space-use patterns is required to inform marine spatial planning, particularly for offshore renewable energy installations. This chapter provides distribution maps for marine mammals observed in the U.S. waters of the Main Hawaiian Islands from 1993 to 2014 using data integrated from multiple sources and spatial predictive modeling. At least 26 species of marine mammal (one seal and 25 cetaceans) have been recorded across the project area, of which eight species are listed as Endangered. This chapter has two sections: 6.1 Cetaceans, and 6.2 Hawaiian monk seal. For cetaceans, maps are provided for 22 species, including 15 showing locations of sightings and seven showing predicted spatial distributions. Sighting data from aircraft, ships and small research vessels were integrated and modeled using non-linear algorithms to map summer and winter distributions. These models were based on the statistical relationships between cetacean abundance and environmental variables at the locations of sightings. Model performance ranged from 17 to 59 percent PDE (percentage deviance explained). Highest performing models were achieved for common bottlenose dolphin (Tursiops truncatus; 59% summer), spinner dolphin (Stenella longirostris; 56% winter) and humpback whale (Megaptera novaeangliae; 37% winter). All categories of predictors (survey platform, temporal, climatic, atmospheric, geographic, physical and biological oceanographic, and topographic), contributed to models, with depth, slope, surface current direction and the strengths of temperature and chlorophyll fronts being relatively important environmental predictors across models. For Hawaiian monk seal (Monachus schauinslandi), we provide maps of sighting locations, individual space-use patterns and the newly released critical habitat maps, followed by discussion of priorities for future data collection to support marine spatial planning. Chapter 6 Marine Mammals