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opencc-by-4.0Dec 2023View details →

Figure 2 in Occurrence of the Ambrosia Beetle Xyleborinus exiguus (Walker) (Curculionidae: Scolytinae: Xyleborini) on the Island of Oʻahu, Hawaiʻi, USA

Figure 2. One of five most parsimonious trees found in an exhaustive search. The clade comprising individuals from Hawaiʻi and Papua New Guinea was unresolved in the strict consensus of the five most parsimonious trees. Numbers at nodes are bootstrap values (>90%) determined with 500 pseudo-replicates using branch and bound searches.

opencc-by-4.0Dec 2023View details →

Figure 1 in Occurrence of the Ambrosia Beetle Xyleborinus exiguus (Walker) (Curculionidae: Scolytinae: Xyleborini) on the Island of Oʻahu, Hawaiʻi, USA

Figure 1. Habitus of Xyleborinus exiguus female, from Hawaiʻi, A. lateral, B. declivity, D. dorsal; and X. saxesenii female, C. declivity, E. dorsal.

Figure 2. Tree heliotrope growing along a in Of turtles and trees: Nutritional analysis of tree heliotrope (Heliotropium foertherianum) leaves consumed by green turtles (Chelonia mydas) in Hawaiʻi

Figure 2. Tree heliotrope growing along a seawater canal at Nan Madol, Pohnpei, FSM. Photo by Gregory A. Koob, US FWS, 2016

Figure 1 in Of turtles and trees: Nutritional analysis of tree heliotrope (Heliotropium foertherianum) leaves consumed by green turtles (Chelonia mydas) in Hawaiʻi

Figure 1. Green turtle consuming floating, senescent tree heliotrope leaves in the Hilton Waikoloa Lagoon, Kona, Hawaiʻi. Photo by M. Rice

Table 2 in Of turtles and trees: Nutritional analysis of tree heliotrope (Heliotropium foertherianum) leaves consumed by green turtles (Chelonia mydas) in Hawaiʻi

Table 2. Comparison of nutritional content of senescent leaves of Heliotropium foertherianum and some other foods reported in Hawaiian green turtle diets. C:N = carbon:nitrogen ratio. Values are mean values. Carbon, nitrogen, protein, fat, and lignin values are based on dry weight of the plant material. Energy values are based on ash-free dry weight. NM = not measured.<table><tbody><tr><th></th><th>% H 2 O content</th><th>% Nitrogen C:N</th><th>% Crude Protein</th><th>% Fat</th><th>% Lignin</th><th>Energy, Kcal/kg Source</th></tr></tbody><tbody><tr><th>H. foertherianum, senescent leaves, seasons combined</th><td>87.9</td><td>0.645</td><td>47.5</td><td>5.45</td><td>2.22</td><td>13.76</td><td>4603</td><td>This paper</td></tr><tr><th>Ahnfeltiopsis concinna thalli</th><td>68.0</td><td>1.7</td><td>21.5</td><td>10.8</td><td>1.9</td><td>0.62</td><td>2846</td><td>McDermid et al. 2007, 2015</td></tr><tr><th>Pterocladiella capillacea thalli</th><td>77.8</td><td>2.7</td><td>14.2</td><td>16.9</td><td>2.3</td><td>3.7</td><td>3220</td><td>McDermid et al. 2007, 2015</td></tr><tr><th>Paspalum vaginatum leaves</th><td>77.5</td><td>2.2</td><td>23.2</td><td>17.2</td><td>NM</td><td>11.5</td><td>4006</td><td>McDermid et al. 2015</td></tr><tr><th>Halophila hawaiiana leaves</th><td>90.3</td><td>2.3</td><td>NM</td><td>14.4</td><td>3.8</td><td>NM</td><td>1696</td><td>McDermid et al. 2007</td></tr></tbody></table>

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Joseph F. Rock Herbarium (Univ. of Hawaiʻi at Mānoa).

Natural history specimen data linked to collectors and determiners held within, "Joseph F. Rock Herbarium (Univ. of Hawaiʻi at Mānoa)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/96beb7d8-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/96beb7d8-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/96beb7d8-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/96beb7d8-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.

opencc-zeroFeb 2023View details →

NOAA NCCOS Assessment: Agency priorities for mapping coral reef ecosystems in Hawaiʻi, 2022-07-08 to 2022-08-01

Description NOAA's Coral Reef Conservation Program (CRCP) has identified a need for priority locations based on emerging management requirements in shallow coral reef areas (up to 40 meters) surrounding the main Hawaiian Islands. The priorities provided by participating agencies will inform research and monitoring activities, address current and future management needs, and maximize opportunities to leverage and complement existing regional efforts. To meet this need, NOAA’s National Centers for Coastal Ocean Science (NCCOS) developed a systematic, quantitative approach and online GIS application to gather seafloor mapping priorities from researchers and coral reef managers. Participants placed virtual coins into a grid overlaid on the project area to express the location of their mapping priorities. They also used pull-down menus to indicate specific mapping data needs and the rationale for their selections. Participants’ inputs were compiled and analyzed to identify high priority areas along with their justifications and requirements. A total of 17 participant groups entered their mapping priorities into the online tool. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in Hawaiʻi. Purpose: The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coasts of the main Hawaiian Islands. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection. Methods: An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the prioritization process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online prioritization tool, the study area was divided into 1786 hexagonal grid cells 2.6 km2 in size. Existing relevant spatial datasets (e.g., bathymetry, protected areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. Each participant was provided with 540 virtual coins to place into grid cells to denote their mapping needs. They were instructed to place more coins in grid cells that were higher priority. A maximum of 54 coins could be placed into an individual grid cell by each respondent. Participants also selected from a drop-down list of predefined management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also selected what map product requirements were needed in priority cells by selecting a minimum of one, to a maximum of two choices from the following list: delineations of large topographic features, delineations of hard vs. soft bottom, models of habitat suitability for key taxa or communities, delineations of substrate type (e.g. sand, mud, coral, rock), models of presence/absence or density of corals, identification of coral species and their local environments, documentation of individual specimen condition. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 2.6 km2 grid cells used in this prioritization and their associated coin values overall, as well as by management use and map product requirement. Other summary values include the number of participants, number of participating groups, number of management uses, and number of map product requirements. Additionally, coins for microscale (identification of coral species and their local environments and documentation of individual specimen condition), mesoscale (delineations of substrate type, models of presence/absence/density of corals), and regional (delineations of topographic features, delineations of hard vs. soft bottom, models of habitat suitability) requirements were summarized. Also included is a ranking of each grid cell based on the total number of coins, management uses, and participating groups allocating coins in the respective cell. For a complete description of the process and analysis see: Kraus et al. 2023, in prep.

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Data for: Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi

Bremer, L.L. (1,2), McGuire, G. (3,4), DeMaagd, N. (1,5), Trauernicht, C. (5)   1 University of Hawaiʻi Economic Research Organization, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822 2 Water Resources Research Center, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822 3 Department of Geography and Environment, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822 4 Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, 96720 5 Department of Natural Resources and Environmental Management, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822   Bremer, L.L., McGuire, G., Hastings Silao, Z., Kurashima, N., Ticktin, T., Crow, S.E., Giardina, P.C., Winter, K.B., DeMaagd, N., and C. Trauernicht. Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi   Multi-strata agroforestry land use scenarios were created using data from the: 2020 State of Hawaiʻi Agricultural Baseline (Perroy and Collier 2020), the Hawaiʻi Carbon Assessment (Jacobi et al. 2017), current (Giambelluca et al. 2013) and RCP 8.5 mid-century projected (Ellison-Timm et al. 2015) rainfall rasters, state land use zoning (SLUC, 2020), slope, elevation, and historical colluvial agroforestry maps (Kurashima et al. 2019). The following rasters display multi-strata agroforestry land-use scenarios.  <ul> <li> “MS_currentclimate_landuse_scenario” represents potential multi-strata agroforestry under current rainfall. </li> <li> “MS_RCP85_midcentury_landuse_scenario” represents potential multi-strata agroforestry under projected RCP 8.5 mid-century rainfall.  </li> </ul> For both scenarios: 1 = dry (550-1500 mm) multistrata agroforestry; 2 = mesic (1500-3000 mm) multi-strata agroforestry; 3 = wet (>3000 mm) multi-strata agroforestry. Estimates of projected changes in above-ground carbon were estimated by comparing modeled AGC in agroforestry scenarios to baseline AGC for current forest (Asner et al. 2016) and estimates of AGC in non-forest vegetation (Selmants et al. 2017). The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid century rainfall. <ul> <li> “AGC_change_currentclimate” </li> <li> “AGC_change_RCP85_midcentury” </li> </ul> For both scenarios: -2 = significant decrease (projected maximum < baseline); -1 = trend decrease (projected maximum >  baseline > projected mean; 1 = weak increase (projected minimum < baseline < projected mean); 2 = strong increase (projected minimum  > baseline). Estimates of projected changes in soil carbon under each scenario were estimated using global meta-analyses (Cardinael et al. 2018; Chaterjee et al. 2018; De Stefano and Jacobson 2017), studies of multi-strata agroforestry transitions in similar climates and soil types, and land-use change studies in Hawaiʻi. The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid-century rainfall.  <ul> <li> “SoilC_currentclimate” </li> <li> “SoilC_RCP85_midcentury” </li> </ul> For both scenarios, 1 = increase low confidence; 10 = increase medium confidence; 100 = increase high confidence; 2 = no change low confidence; 20 = no change medium confidence; 200 = no change high confidence; 3 =unclear (insufficient data); 4 = unclear (mixed evidence). Projected synergies and tradeoffs in AGC and soil C under each scenario were estimated by combining the soil C and AGC results. The following rasters display projected synergies and tradeoffs in soil C and AGC under current and RCP 8.5 mid-century rainfall.  <ul> <li> “AGC_Soil_Bivariate_currentclimate” </li> <li> “AGC_Soil_Bivariate_RCP85_midcentury” </li> </ul> For both scenarios: the first value is soil C category: 7 = unknown/uncertain; 8= increase; 9= no change; and the second value is AGC category: 0 = decrease; 1 = no change; 2 = increase.   <h2>References:</h2> Asner, G. P., Sousan, S., Knapp, D. E., Selmants, P. C., Martin, R. E., Hughes, R. F., & Giardina, C. P. (2016). Rapid forest carbon assessments of oceanic islands: A case study of the Hawaiian archipelago. 11(1). <a href="https://doi.org/10.1186/s13021-015-0043-4">https://doi.org/10.1186/s13021-015-0043-4</a> Cardinael, R., Umulisa, V., Toudert, A., Olivier, A., Bockel, L., & Bernoux, M. (2018). Revisiting IPCC Tier 1 coefficients for soil organic and biomass carbon storage in agroforestry systems. Environmental Research Letters, 13. <a href="https://doi.org/10.1088/1748-9326/aaeb5f/meta">https://doi.org/10.1088/1748-9326/aaeb5f/meta</a> Chaterjee, N., Nair, P. K. R., Chakraborty, S., & Nair, V. D. (2018). Changes in soil carbon stocks across the forest-agrofoest-agriculture/pasture continuum in various agroecological regions: A meta-analysis. Agriculture, Ecosystems & Environment, 266, 55–67. <a href="https://doi.org/10.1016/j.agee.2018.07.014">https://doi.org/10.1016/j.agee.2018.07.014</a> De Stefano, A., & Jacobson, M. G. (2017). soil carbon sequestration in agroforestry systems: A meta-analysis. Agroforestry Systems. <a href="https://doi.org/10.1007/s10457-017-0147-9">https://doi.org/10.1007/s10457-017-0147-9</a> Elison Timm, O., Giambelluca, T. W., & Diaz, H. F. (2015). Statistical downscaling of rainfall changes in Hawaiʻi based on the CMIP5 global model projections. Journal of Geophysical Research: Atmospheres. <a href="https://doi.org/10.1002/2014JD22059">https://doi.org/10.1002/2014JD22059</a> Giambelluca, T. W., Chen, Q., Frazier, A. G., Price, J. P., Chen, Y. L., Chu, P. S., Eischeid, J. K., & Delparte, D. M. (2013). Online Rainfall Atlas of Hawaiʻi. Bulletin Of the American Meteorological Society, 94, 313–316. <a href="https://doi.org/10.1175/BAMS-D-11-00228.1">https://doi.org/10.1175/BAMS-D-11-00228.1</a> Jacobi, J. D., Price, J. P., Fortini, L. B., Gon III, S. M., & Berkowitz, P. (2017). Carbon Assessment of Hawaiʻi Land Cover Map [Map]. USGS.  <a href="https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b">https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b</a> Kurashima, N., Fortini, L., & Ticktin, T. (2019). The potential of indigenous agricultural food production under climate change in Hawaiʻi. Nature Sustainability. <a href="https://doi.org/10.1038/s41892-019-0226-1">https://doi.org/10.1038/s41892-019-0226-1</a> Selmants, P. C., Giardina, C. P., Sousan, S., Knapp, D. E., Kimball, H., Hawbaker, T. J., Moreno, A., Seirer, J., Running, S. W., Miura, T., Bergstrom, R., Hughes, R. F., Litton, C. M., & Asner, G. P. (2017). Baseline Carbon Storage and Carbon Fluxes in Terrestrial Ecosystems of Hawaiʻi. USGS. State Land Use Commission. (2020). State Land Use District Boundaries [Map]. Hawaiʻi Statewide GIS Program.

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opencc-zeroAug 2022View details →

Tetramolopium stemmermanniae sp. nov. (Asteraceae), a new species found at Pōhakuloa Training Area, Hawaiʻi Island

A new species endemic to Hawaiʻi Island, Tetramolopium stemmermanniae, is described and illustrated. Molecular and morphological evidence support Tetramolopium stemmermanniae as being distinct from Tetramolopium arenarium var. arenarium, Tetramolopium consanguineum ssp. leptophyllum, and Tetramolopium humile ssp. humile, which occur at Pōhakuloa Training Area, Hawaiʻi Island. Tetramolopium stemmermanniae shares an upright and multibranched habit as that of Tetramolopium arenarium var. arenarium and Tetramolopium consanguineum ssp. leptophyllum. It differs in ray and disc flower color and number, and in having an open, paniculate inflorescence. We provide a description of the new taxon, include a key to the Tetramolopium species of Hawaiʻi, and a brief description of the habitat where it occurs.

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CLONALITY CONTRIBUTES TO THE SPREAD OF AVRAINVILLEA LACERATA (BRYOPSIDALES, CHLOROPHYTA) IN HAWAIʻI

The excel file includes all multilocus genotypes. The first sheet includes all raw multilocus genotypes prior to allele dosage estimation. The second sheet is in GenAPoPop format and includes all multilocus genotypes after allele dosage correction using allele peak height.  Population acronyms: ewaA – ʻEwa Beach, Lagoon East mlb – Maunalua Bay ewaB – ʻEwa Beach, Kaloi The code for each individual sampled during 2018, 2019, and 2021 is Av_sample # (01-260). The code for each individual sampled during 2022 is Av_3-digit sample # (001-040). The code for each individual sampled during 2023 is Av mound #_H holdfast #_B blade #.   This project was supported by Alabama Academy of Science student research award (to BMT), the Phycological Society of America Grant In Aid of Research (to BMT), the UAB Graduate Student Government (GSG) Professional Development and Travel Award (to BMT), the UAB Harold Martin Outstanding Student Development Award (to BMT), CLONIX-2D (ANR-18-CE32-0001 to SS and SAKH), start-up funds from the College of Arts and Sciences at UAB (to SAKH and MLH), the National Science Foundation (NSF) DEB-2113745 (to SAKH), and National Fish and Wildlife Foundation (#74235 to SAKH and HLS). SAKH was supported by the NSF CAREER award DEB-2141971 and the Norma Lang Early Career Fellowship from the Phycological Society of America.

opencc-by-4.0Aug 2024View details →

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Table 1 in Of turtles and trees: Nutritional analysis of tree heliotrope (Heliotropium foertherianum) leaves consumed by green turtles (Chelonia mydas) in Hawaiʻi

Table 1. Results of chemical analysis of senescent leaves of Heliotropium foertherianum from Kona, HI. All values are mean values. C:N = carbon:nitrogen ratio, ADF = acid detergent fiber, NDF = neutral detergent fiber. SE represents the standard error of the mean. Values of carbon, nitrogen, ash, protein, fat, fiber, lignin, energy and phenol are based on dry matter. n=number of sub-samples.<table><tbody><tr><th></th><th>% Dry Matter</th><th>% Nitrogen</th><th>C:N</th><th>% Ash</th><th>% Crude</th><th>% Crude</th><th>% ADF</th><th>% NDF</th><th>% Lignin</th><th>Energy, Kcal/kg</th><th>Total Phenol, mg/g</th></tr></tbody><tbody><tr><th></th><td>(SE)</td><td>(SE)</td><td>(SE)</td><td>(SE)</td><td>Protein</td><td>Fat</td><td>(SE)</td><td>(SE)</td><td>(SE)</td><td>(SE)</td><td>(SE)</td></tr><tr><th>Oct 2016</th><td>97.38</td><td>0.65</td><td>46.03</td><td>2.46</td><td>5.25</td><td>2.43</td><td>31.26</td><td>40.70</td><td>13.91</td><td>4556.4</td><td>584</td></tr><tr><th></th><td>(0.29)</td><td>(0.003)</td><td>(1.58)</td><td>(0.07)</td><td></td><td></td><td>(0.26)</td><td>(0.21)</td><td>(0.22)</td><td>(42.0)</td><td>(6.7)</td></tr><tr><th></th><td>n=2</td><td>n=10</td><td>n=10</td><td>n=2</td><td>n=1</td><td>n=1</td><td>n=2</td><td>n=2</td><td>n=2</td><td>n=3</td><td>n=3</td></tr><tr><th>Jan 2017</th><td>96.49</td><td>0.64</td><td>48.94</td><td>2.48</td><td>5.65</td><td>2.00</td><td>31.82</td><td>42.35</td><td>13.60</td><td>4649.8</td><td>573</td></tr><tr><th></th><td>(0.05)</td><td>(0.35)</td><td>(1.56)</td><td>(0.04)</td><td></td><td></td><td>(0.22)</td><td>(0.47)</td><td>(0.42)</td><td>(32.9)</td><td>(10.5)</td></tr><tr><th></th><td>n=3</td><td>n=10</td><td>n=10</td><td>n=3</td><td>n=1</td><td>n=1</td><td>n=2</td><td>n=2</td><td>n=2</td><td>n=3</td><td>n=3</td></tr></tbody></table>

publicMay 2023View details →

Tetramolopium stemmermanniae sp. nov. (Asteraceae), a new species found at Pōhakuloa Training Area, Hawaiʻi Island

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publicDec 2025View details →

The structural and functional impacts of invasive Psidium cattleianum in forests on the Island of Hawaiʻi

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publicOct 2024View details →

Dietary niche variation in an invasive omnivore: the effects of habitat on feral pig (Sus scrofa) resource use in Hawaiʻi

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Data from: MetaBARFcoding: DNA-barcoding of regurgitated prey yields insights into Christmas Shearwater (Puffinus nativitatis) foraging ecology at Hōlanikū (Kure Atoll), Hawaiʻi

Morphological identification of digested prey remains from a generalist predator can be challenging, especially when attempting to match degraded remains to taxonomic keys. DNA techniques, whereby prey is sequenced and matched to large public nucleotide sequence databases, are increasingly being used to augment morphological identification. We used "metaBARFcoding" (DNA metabarcoding) to target a region of the cytochrome c oxidase subunit I mitochondrial gene to identify prey in highly-digested regurgitations from Christmas Shearwaters Puffinus nativitatis at Hōlanikū (Kure Atoll). Metabarcoding was used to bulk-process 92 water samples from regurgitations collected from 2009-2017, providing an overview of the seabird's diet. We additionally Sanger sequenced 100 prey items from 50 randomly chosen regurgitations to verify that metabarcoding characterized key components of the diet. The metabarcoding technique identified 87 unique taxa from 29 families of fish and squid, spanning diverse taxa, including reef-associated, pelagic-oceanic, and mesopelagic species. Rare prey (frequency of occurrence < 5% of samples) constituted 66% of the species richness, demonstrating the highly diverse diet of this generalist predator. Overall, 81% of the families detected in the contemporary diet were previously documented in Christmas Shearwater diets from the Northwestern Hawaiian Islands. Our results indicate that metabarcoding the cytochrome c oxidase subunit I (COI) region is useful in identifying a wide range of taxa from highly digested regurgitations, thus facilitating this approach to study seabird diets.

opencc-zeroNov 2021View details →

FIGURE 1 in A new species of Phymastichus (Hymenoptera: Eulophidae: Tetrastichinae parasitic on Xyleborus beetles (Coleoptera: Curculionidae: Scolytinae) in Hawaiʻi and aspects of its biology, life history, and behavior

FIGURE 1. Comparison of Phymastichus holoholo sp. nov. (left column) and Phymastichus xylebori (right column). a: Phymastichus holoholo sp. nov. ♀ (ex Xyleborus affinis in Macadamia integrifolia trunk, Keaʻau, Hawaiʻi island); b: Phymastichus holoholo sp. nov. ♀ (ex Schefflera actinophylla branch, Kahana Bay, Oʻahu), apex of metasoma showing the crimped syntergum with sparse setae; c: Phymastichus holoholo sp. nov. ♀ (same data as (b)), a specimen with ovipositor exposed and visible; d: Phymastichus holoholo sp. nov. ♀ (same data as (b)), forewing; e: a specimen fitting the description of Phymastichus xylebori, ♀, (ex Macadamia integrifolia trunk, ʻĀhualoa, Hawaiʻi island); f: Phymastichus xylebori ♀ (ex Macadamia integrifolia trunk, Waimānalo, Oʻahu), apex of metasoma showing the crimped syntergum with a tuft of dense setae posteriorly and blade-like ovipositor; g: Phymastichus xylebori ♀ (same data as (f)), forewing.

FIGURE 4 in A new species of Phymastichus (Hymenoptera: Eulophidae: Tetrastichinae parasitic on Xyleborus beetles (Coleoptera: Curculionidae: Scolytinae) in Hawaiʻi and aspects of its biology, life history, and behavior

FIGURE 4. Parasitism of bark beetles by Phymastichus. a: Two Xyleborus ferrugineus beetles in octopus tree from Kahana Bay, Oʻahu with typical Phymastichus emergence holes. b: Xyleborus ferrugineus beetle in Acacia koa from Waʻahila ridge on Oʻahu with a (presumably) Phymastichus emergence hole. c: The same beetle as in (b) partially extracted from wood to show its position; d: X. ferrugineus from octopus tree near Kahana Bay, Oʻahu, broken open between the pro- and mesothorax to show Phymastichus pupae developing inside. On the left is the anterior part of the beetle with the male P. holoholo pupa positioned transversely inside. On the right is the rest of the beetle, the female pupa positioned with its head towards the back of the beetle; e: The pupae in (d) removed from the beetle, female on the right, male on the left; f,g: Xyleborus ferrugineus from octopus tree near Kahana Bay, Oʻahu with P. holoholo emergence holes; h: Xyleborus affinis from octopus tree near Kahana Bay, Oʻahu with a partially emerged Phymastichus holoholo adult female still inside. i: Hawaiian endemic Xyleborus lanaiensis from ʻālaʻa (Planchonella sandwicensis) with a (presumably) Phymastichus emergence hole; j: Xylosandrus crassiusculus from octopus tree near Kahana Bay, Oʻahu with a Phymastichus holoholo emergence hole.

FIGURE 3 in A new species of Phymastichus (Hymenoptera: Eulophidae: Tetrastichinae parasitic on Xyleborus beetles (Coleoptera: Curculionidae: Scolytinae) in Hawaiʻi and aspects of its biology, life history, and behavior

FIGURE 3. Phymastichus holoholo sp. nov. a: forewing ♀; b: hindwing ♀; c: antenna ♀; d: forewing ♂; e: hindwing ♂; f: antenna ♂; g: head (uncollapsed) ♀; h: dorsal view ♀.

FIGURE 2 in A new species of Phymastichus (Hymenoptera: Eulophidae: Tetrastichinae parasitic on Xyleborus beetles (Coleoptera: Curculionidae: Scolytinae) in Hawaiʻi and aspects of its biology, life history, and behavior

FIGURE 2. Phymastichus holoholo sp. nov. adults, holotype (♀) and allotype (♂). a: holotype ♀, side view; b: holotype ♀, dorsal view; c: holotype ♀, head, note that the scrobal area is somewhat collapsed due to drying of specimen; d: allotype ♂, side view; e: allotype ♂, dorsal view; f: allotype ♂, head. Scale bars: 500μm