Philippa Kaur

The Wuikinuxv, Kitasoo/Xai’xais, Heiltsuk and Nuxalk First Nations hold Indigenous rights to their territories, where all data were collected. Scientific staff who are members of these Nations or who work directly for them had direct approvals from Indigenous rights holders and were exempt from other research permit requirements. Collaborating DFO scientists worked in partnership with the First Nations to collect data in their territories..Sampling targeted rocky reefs, the preferred habitat for most Sebastidae38, which we located through local Indigenous knowledge or a bathymetric model49. Data were collected by four fishery-independent methods—shallow diver transects, mid-depth video transects, deep video transects, and hook-and-line sampling—detailed in earlier publications32,33,34,35,50,51 and summarized in Table 1. Data had a spatial resolution of ≤ 130 m2 and each sampling location (N = 2936 for Sebastidae, 2654 for sponges, 2321 for corals) was ascribed to a 1-km2 planning unit within the standardized grid used to design the MPA network (N = 632 for Sebastidae, 525 for sponges, 529 for corals, 516 inclusive of surveys for all taxonomic groups).Table 1 Survey methods used for data collection.Full size tableAlthough sampling encompassed 11 years (2006–2007, 2013–2021: Table 1), 84% of 1-km2 planning units were sampled during only one year (Appendix S2). Analyses, therefore, focus on spatial variability in species distributions and do not address temporal variability within planning units. When all years and methods are combined, 1-km2 planning units had a median of 3 samples (range = 1 to 80, Q1 = 2, Q3 = 6) (i.e., sum of dive transects, video sub-transects, and hook-and-line sessions). Supplementary Data Set 1 reports sampling effort by 1-km2 planning unit, survey type, and year (see Data Availability for link to these data).For each 1-km2 planning unit, u, we calculated hotspot indices for Sebastidae (BSEB,u), structural corals (BCor,u), and large-bodied sponges (BSp,u). These indices did not consider cup corals, whip-like corals or encrusting corals or sponges.As detailed below (Eqs. 1–4), each species of Sebastidae or genera of corals contributed to BSEB,u or BCor,u, according to their abundance weighted by Wt: a conservation prioritization score based on taxon characteristics. For the 26 species of Sebastidae that we observed, Wt equaled the sum of scores for (1) fishery vulnerability, using intrinsic population growth rate, r, as a proxy variable52,53, (2) depletion level, using the ratio of recent biomass to unfished biomass as a proxy variable, (3) ecological role, with trophic level as proxy, and (4) evolutionary distinctiveness14 (Table 2; Appendix S3). Because several rockfishes are very long-lived (i.e., have low values for r) and depleted, maximum potential scores were twice as large for fishery vulnerability and depletion level than for ecological role and evolutionary distinctiveness. Data for depletion level and evolutionary distinctiveness were unavailable for some species, and score calculations (detailed in Table 2) account for missing values (Appendix S3).Table 2 Criteria and equations used to calculate the conservation prioritization score, Wt, for each species of Sebastidae and for each taxa of structural corals.Full size tableFor the 6 genera of structural corals analyzed (Appendix S4), Wt depended on mean height (estimated from video transect images: Table 1), which correlates positively with vulnerability to physical damage from bottom-contact fishing gear (including longer time to recovery)20,54,55 and with strength of ecological role (e.g., amount of biogenic habitat and carbon sequestration increases with height)44,56 (Table 2, Appendix S4). Wt for corals did not include depletion level due to lack of data.The hotspot index for large-bodied sponges, BSp,u did not differentiate between species characteristics (i.e., ({W}_{t}=1)) and we pooled the abundances of all observed species of Hexactinellidae (Aphrocallistes vastus, Farrea occa, Heterochone calyx, Rhabdocalyptus dawsoni, Staurocalyptus dowlingi) and Demospongiae (Mycale cf loveni). This approach is consistent with regional fishery bodies worldwide, which treat large-bodied sponges as a single functional group57.To derive hotspot indices for each taxonomic group (Sebastidae, structural corals, or large-bodied sponges), we first developed a set of candidate generalized linear mixed models (GLMM) to explain relative abundance data for rockfish, corals, and sponges. For each GLMM, we estimated ({lambda }_{t,i,l}), the expected counts (or expected percent cover) for taxa t obtained with survey method i at point location l. (Point locations are individual dive transects, video transect bins, or hook-and-line timed sessions: Table 1.) Specifically,$${lambda }_{t,i,l}=gleft(beta {X}_{t,i,l}right)$$
(1)
$${C}_{t,i,l}mathrm {, or ,} {D}_{t,i,l}sim fleft({lambda }_{t,i,l}right)$$
(2)
where g was the link function for the GLMM and f the distribution for the likelihood function modelling either the observed counts C (negative binomial) for Sebastidae and structural corals or a combination of counts (negative binomial) and percent cover D (beta distribution) for large-bodied sponges. We used multiple GLMMs to model large-bodied sponges because deep video transects recorded actual counts whereas dive or mid-depth video transects recorded percent cover categories (Table 1).For each taxonomic group, we estimated a set of coefficients (beta) for the vector of X covariates that best estimated counts or percent cover. Our hypothesized covariates included the 1-km2 planning unit (modelled as a random intercept to control for repeated measures within a given planning unit), survey method, depth (including both linear or a 2nd order polynomial), and taxa. Each GLMM controlled for sample effort as an offset—effort was measured either as area covered by dive transects or video bins, or the duration of hook-and-line sessions. We also tested for possible covariate’s effects on the dispersion parameter (for the negative binomial GLMMs) and zero-inflation terms (for both the negative binomial and beta GLMMs). The best set of covariates to predict counts or percent cover were then chosen based on AIC model selection criteria. All models were fitted using ‘glmmTMB’58 in R version 4.0.259, and simulated residuals and diagnostic tests performed for each best-fit model using the package ‘DHARMa’60. For example, our best model for Sebastidae counts predicted 2% fewer zero counts than were observed.We applied depth and survey method selectivity criteria to reduce excessive zeroes in the count data that may be biologically unjustified (Appendix S5). For all taxon, if i detected t, then the method was valid for that taxon. If i did not detect t and t is a Sebastidae, then the method was valid (i.e., count = 0) only if the overall 10th and 90th percentiles of depths sampled by that method encompassed the expected depth range of t (Appendix S5). If i did not detect t and t is a coral or sponge (which are rarer than Sebastidae), then the method is valid only if the depth of the sampling event exceeded or equaled the minimum expected depth of t. Also, hook-and-line gear cannot systematically sample sessile benthic organisms or planktivores and this method was valid only for non-planktivorous Sebastidae (Appendix S5).Using the best-fit models from above, we calculated the expected count (or percent cover) per unit of effort, (mu), for taxa t observed with method i at each planning unit u:$${mu }_{t,i,u}=frac{{sum }_{l=1}^{{n}_{i,u}}left({lambda }_{t,i,l}right)}{{sum }_{l=1}^{{n}_{i,u}}left({mathrm{E}}_{t,i,l}right)}$$
(3)
where ({n}_{i,u}) was the total number of point locations sampled by that method within the planning unit and effort was either the cumulative area covered by dive or video surveys or the cumulative duration of hook-and-line sampling sessions within the planning unit. Because survey methods differed in their maximum values and potential biases (e.g., field of view is greater for divers than for video cameras; hook-and-line gear samples one fish at a time while visual methods can observe multiple fish simultaneously),({mu }_{t,i,u}) was rescaled as a min–max normalization,({mu }_{t,i,u}^{^{prime}}) (i.e., difference between the observed value and the minimum value across all u, divided by the range of values across all u).The hotspot index for each of Sebastidae, structural corals, and large-bodied sponges (denoted as taxonomic group g) was then calculated for each planning unit as:$${B}_{g,u }={sum }_{t=1}^{{n}_{s,g}}{sum }_{i=1}^{{n}_{m,g}}{mu }_{t,i,u}^{^{prime}}{W}_{t}$$
(4)
where Wt was the taxon-specific weighing factor (Table 2, Appendices S3, S4), ({n}_{s,g}) was the number of species in taxonomic group g, and ({n}_{m,g}) was the number of valid methods to sample group g.For each 1-km2 planning unit where all taxonomic groups were surveyed (N = 518), we then calculated the overall hotspot index:$${B}_{o,u }=H{sum }_{g=1}^{G}{B}_{g,u}.$$
(5)
where H is Shannon’s evenness index, with proportional abundance of each taxonomic group represented by BSEB,u, BCor,u, and BSp,u.Hotspot index values were normalized as the proportion of the maximum value and converted to decile ranks. Relationships between decile ranks and index values were nonlinear (Appendix S6).For Sebastidae, large-bodied sponges, and the overall hotspot index, we defined hotspots as planning units containing decile ranks 9 or 10: criterion which we deemed appropriate for the small spatial scales of conservation planning being used for the central portion of the Northern Shelf Bioregion (16-km2 planning units in Fig. 2). We are aware that other studies define hotspots based on a narrower range of values (e.g., top 10%26; top 2.5%28) but their context is generally one in which conservation planning is done at a much greater scale (e.g., ≈50,000-km2 grid cells26;1° latitude × 1° longitude grid cells28). For structural corals, which had near-zero index values in all but the top-ranking planning units (Appendix S6), we defined hotspots as planning units containing decile rank 10.Maximum depths sampled within planning units were deepest in the Mainland Fjord and shallowest in the Aristazabal Banks Upwelling Upper Ocean Subregion (Appendix S7). Accordingly, we used multiple logistic regression implemented with the ‘glm’ function in R to estimate the probabilities hotspot occurrence within 1-km2 planning units in relation to maximum depth sampled (including a 2nd-order polynomial) and Upper Ocean Subregion. Competing models were compared with AIC model selection procedures.Following the directive of Central Coast First Nations, decile rank distributions were mapped as 16-km2 planning units, u16 (N = 283 for Sebastidae, 264 for sponges, 263 for corals, 260 inclusive of surveys for all taxonomic groups), thereby protecting sensitive locations that would be revealed at smaller scales. To do so, we took the average between the maximum index value and the mean of the remainder of index values among the 1-km2 planning units, u, contained within each u16, and converted these values into decile ranks. This approach balances conservation prioritization among u16 that may have good average index values for multiple u, and u16 with a single high-ranking u among multiple low-scoring u. Relationships between decile ranks and hotspot index values also were nonlinear at this scale (Appendix S6). The same hotspot definitions developed for u apply to u16.Eighty one percent of 16-km2 planning units were sampled during only one or two years (Appendix S2). When all years and methods are combined, 16-km2 planning units had a median of 6 samples (range = 1 to 110, Q1 = 3, Q3 = 13). Supplementary Data Set 2 reports sampling effort by 16-km2 planning unit, survey type, and year (see Data Availability for link to these data). More

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Netting seabirds is great fun. And it’s crucial for science and conservation.In this photo, taken in July, I’m heading out to capture birds on Inishtrahull, Ireland’s northernmost island. Lying about 10 kilometres northeast of the mainland, the island is home to thousands of seabirds during the summer nesting season, including storm petrels (Hydrobates pelagicus), Manx shearwaters (Puffinus puffinus) and fulmars (Fulmarus glacialis). The fulmars are experiencing a population crash, which I’m investigating.Migratory birds are protected here, but we need to know where they go when they leave their nests. I attach an identification band and a light-level geolocator — a sensor that helps to estimate location from day length — to every bird I catch. A few birds get GPS monitors, but we dole those out carefully, because each costs about £1,000 (US$1,368).The birds tend to nest on cliffs, and on a bad day I’ll catch just three. Some days I get as many as 12. Shearwaters are a challenge, because they nest only at night: I have to use a torch and watch my step.The birds don’t enjoy getting caught, but the stress is only temporary. The data they provide help us to understand their migration patterns. Fulmars spend almost their entire lives at sea. I’m interested in finding out how often they share waters with long-line fishers, which would be a potentially fatal scenario for the birds. That’s not the only threat: a study has found that more than half of beached North Sea fulmars have large amounts of plastic in their stomachs (see go.nature.com/3cosy8j).The lighthouse behind me is now home to the Inishtrahull Bird Observatory, a base for birdwatchers. I’m the founding chairman, but the observatory, part of a network of monitoring spots stretching 1,200 kilometres from Scotland to southern Ireland, will outlive me. It will be a centre for science and education for years to come.

Nature 599, 340 (2021)
doi: https://doi.org/10.1038/d41586-021-03055-8

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The present study compared DNA sequences in a whole genome between the long strain and standard genome samples as references or the short strain and standard ones in T. castaneum. The results of resequencing analysis showed variations of DNA sequence from the reference sequence in both long and short strains, and the variations were detected more frequently in the long strain in a whole genome. Small nucleotide variants (SNV), multi-nucleotide variants (MNV), deletion, insertion, and replacement were detected in a whole genome in long and short strains. The same DNA sequence variants sharing between long and short strains were removed for the analyses. The numbers of small variants in total were larger in long strains than short strains (Fig. 1, Tables S1 and S2). The most frequent type of small variants was SNV, and the proportions of SNV were 82.7% (93,233/112,783) in long strains and 82.8% (13,817/16,697) in short strains, respectively (Fig. 1A). The SNVs compared with the reference nucleotide occurred frequently between adenine and guanine or cytosine and thymine in both long and short strains (Fig. 1B), and the frequencies were up to three times as large as other base combinations, indicating more frequent transition and fewer transversion variants. Deletion and insertion ranged from one to nine bases in both long and short strains, with one base was frequently deleted or inserted (Fig. 1C). Homozygosity presented more frequently than heterozygosity in all linkage groups, but the rate of homozygosity to heterozygosity depended on the linkage groups (Fig. 1D). Homozygosity of variants was more frequent in linkage groups 3 (LG3), 5 (LG5) and 7 (LG7) than other linkage groups in both strains. The ratios of homozygosity to heterozygosity were the largest in LGX and LG2 in long and short strains, respectively.Figure 1Analytical results of small variants of DNA sequence in a whole genome level in long and short strains. Proportion of small variants as SNV, MNV, deletion, insertion, and replacement in long and short strains (A). The numbers of small variants are indicated as the diameter of a pie graph. Frequencies of the SNVs in both long and short strains were compared with the reference nucleotide (B). Insertion and deletion ranged from one to nine bases in both long and short strains (C). Frequency of homozygosity or heterozygosity and its ratio in all linkage groups in long and short strains (D).Full size imageThe variants distributed in cording and non-cording regions. Figure 2A shows the results of narrowing down the variants in genic region from the variants in a whole genome in the long and short strains, and then aggregating the variants information in the exon, intron, URT and other regions. In all genic region, numbers of variants were larger in long strain than short strain. Then, genes containing these variants were counted in each strain (Fig. 2B). In exon region, genes with nonsynonymous variants were more numerous in the long strain (3243) than the short strain (844), and 464 common genes containing different DNA sequence variants between the strains were detected (Fig. 2B). In the genes with synonymous variants or the genes with variants in intron or UTR, the numbers of genes in long strain were constantly larger than those in short strain (Fig. 2B). The functions of long-unique, short-unique and common genes with variants were sorted into four categories by enrichment analyses as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) ongoloty (KO) terms (Fig. 2C, Table S3). In the biological process, cellular component, and molecular function, and KEGG pathway, characteristics of nonsynonymous variants in long-unique, short-unique and common genes did not basically overlap among them, indicating specific selection of gene characteristics for each strain. Characteristics of synonymous variants were also sorted, but the synonymous variants may not influence the amino acid sequence of the gene and structure of the protein translated, rather these characteristics may be necessary to maintain the strain and preserved under artificial selection. Variants in intron and UTR may have potential effects on the gene expression, but should be investigated in detail in future study. Analyses of cis-regulatory elements might be important to understand regulation of gene expression, but the information on this region in T. castaneum is not available, therefore, the variants in cis-regulatory elements could not be analyzed.Figure 2Analytical results of the position of small variants in a whole genome in long and short strains (A) Numbers of variants in genic region including exon region, intron, UTR and other non-cording regions were indicated. As shown in parentheses, some ncRNAs and tRNAs were contained in exon, intron, and UTR regions. In short strain, there were five regions where two different genes overlap in 5′-UTR and 3′-UTR, respectively. Numbers of genes with variants in exon, intron and UTR regions in long and short strains (B). Numbers of long-unique, short-unique and common genes were shown by Venn diagrams. Common genes contain variants with different DNA sequences between long and short strains. Enrichment analyses of the function of genes with variants sorted into four categories (biological process, cellular component, molecular function, and KEGG pathway) (C). The heatmap is generated using the R package “gplots” (version 3.1.1, https://cran.r-project.org/web/packages/gplots/index.html). The list of each ontology shows the ID and term. The KO id is shown by a three- or four-letter organism code, the first-letter of the genus name and the first two- or three-letters of the species name of the scientific name of the organism, with pathway number. For example, Neuroactive ligand-receptor interaction of Tribolium castaneum is shown as “tca04080”.Full size imageTo explore the position of genes with variants associated with duration of death feigning in linkage groups, bulk segregant analysis was carried out (Fig. 3). The red approximate lines of the plot data crossed over the green threshold lines (P  More