2c:T496,dashboard = html`

California Fisheries Climate Dashboard

Explore how California commercial fisheries respond to lagged ocean climate conditions, using Annual Mean RONI, commercial landings, ex-vessel value, and price per pound.

Dashboard Controls

${viewof selectedSpecies}

${viewof selectedLag}

${viewof selectedMetrics}

${climateChart} ${correlationCards} ${interpretationCard}

${sensitivityChart}

`2d:T644,controlPanel = html`

Dashboard Controls

Selected species

${selectedSpecies}

Climate lag

${selectedLag} years

Metrics displayed

${selectedMetrics.join(", ")}

Interpretation

Positive correlations suggest warm-water associations; negative correlations suggest cold-water associations.

`2e:T593,climateChart = Plot.plot({ width: 900, height: 450, marginBottom: 80, title: "Climate and Economics Response: Fishery Metrics and Lagged RONI", y: { label: "Standardized Value (z-score)", grid: true, domain: [-2, 3.5] }, x: { label: "Landing Year", tickFormat: d => String(d) }, color: { domain: [ "Commercial Landings", "Ex-vessel Value", "Price per Pound", "Annual Mean RONI" ], range: [ "#f28e2b", "#59a14f", "#af7aa1", "#000000" ], legend: true }, marks: [ Plot.rectY(bands, { x1: 1980, x2: 2025, y1: "y1", y2: "y2", fill: d => d.phase === "Warm" ? "#ff6b6b" : "#5dade2", fillOpacity: 0.16 }), Plot.ruleY([0], {stroke: "#555"}), Plot.lineY(plotData.filter(d => d.Variable !== "Annual Mean RONI"), { x: "Year", y: "Z_score", stroke: "Variable" }), Plot.lineY(plotData.filter(d => d.Variable === "Annual Mean RONI"), { x: "Year", y: "Z_score", stroke: "Variable", strokeDasharray: "6,4" }), Plot.dot(plotData, { x: "Year", y: "Z_score", fill: "Variable", r: 3 }), Plot.tip( plotData, Plot.pointerX({ x: "Year", y: "Z_score", title: d => `${d.Variable} Year: ${d.Year} z-score: ${d.Z_score.toFixed(2)}` }) ) ] })2f:T6ed,interpretationCard = html`

Climate Interpretation

${bestCorr.map(d => { const warm = d.Best_Pearson_r >= 0; const color = warm ? "#d73027" : "#4575b4"; const condition = warm ? "warmer" : "cooler"; const metric = metricPhrase.get(d["Fishery Metric"]) ?? d["Fishery Metric"].toLowerCase(); return html`

${d["Fishery Metric"]}

The strongest historical relationship between climate and ${metric} occurred at a ${d.Best_Lag}-year lag (r = ${d.Best_Pearson_r.toFixed(2)}). This ${warm ? "positive" : "negative"} relationship suggests that ${condition} ocean conditions were historically associated with higher ${metric} ${d.Best_Lag === 0 ? "during the same year." : `approximately ${d.Best_Lag} years later.`}

`; })}

These relationships represent historical correlations and should not be interpreted as evidence that climate directly causes changes in fishery performance. Biological processes, market dynamics, management regulations, and other environmental factors may also influence these observed patterns.

`30:T6b5,correlationCards = html`

Current Lag (${selectedLag} years)

${currentCorr.map(d => html`

${d["Fishery Metric"]}: r = ${d.Pearson_r.toFixed(2)} | ${d.Pearson_r >= 0 ? "Warm-water" : "Cold-water"} | n = ${d.n_years} years

`)}

Strongest Observed Association

${bestCorr.map(d => html`

${d["Fishery Metric"]}: lag ${d.Best_Lag} years | r = ${d.Best_Pearson_r.toFixed(2)} | ${d.Best_Pearson_r >= 0 ? "Warm-water" : "Cold-water"} | n = ${d.Best_n_years} years

`)}

`31:T421,sensitivityChart = Plot.plot({ width: 900, height: 900, marginLeft: 180, title: "Relative Climate Sensitivity Ranking (Commercial Landings)", x: { label: "Relative climate sensitivity |r|", grid: true }, y: { label: null, domain: sensitivityLandings.map(d => d["Species Name"]) }, color: { domain: ["Warm-water", "Cold-water"], range: ["#d73027", "#4575b4"], legend: true }, marks: [ Plot.barX(sensitivityLandings, { y: "Species Name", x: "Sensitivity", fill: "Association", opacity: d => d["Species Name"] === selectedSpecies ? 1 : 0.35, title: d => `${d["Species Name"]} Strongest lag: ${d.Best_Lag} years r = ${d.Best_Pearson_r.toFixed(2)} ${d.Association} n = ${d.Best_n_years} years` }), Plot.text( sensitivityLandings.filter(d => d["Species Name"] === selectedSpecies), { y: "Species Name", x: "Sensitivity", text: "Species Name", dx: 6, textAnchor: "start", fontWeight: "bold" } ) ] })32:[{"id":"ae120b59491205f923649e9a27222a5fcac55d8770b03db460fe69df927107da8a00aae746f20be69eb95a633948889e67382bbcb35a103bfb5ee9a5a1bab306","href":"https://static.observableusercontent.com/files/ae120b59491205f923649e9a27222a5fcac55d8770b03db460fe69df927107da8a00aae746f20be69eb95a633948889e67382bbcb35a103bfb5ee9a5a1bab306","name":"dashboard_sensitivity.csv","type":"text/csv","lastModified":1783143726942,"size":22550},{"id":"b646e0df7b05c35ce42a6b264fbb80e1eab3c9455bcaeee777ce1e76d1ea549f3f460093d31fa1db42efe102c5af656e7f4c4ca13606ff45f124eb5a4937bac7","href":"https://static.observableusercontent.com/files/b646e0df7b05c35ce42a6b264fbb80e1eab3c9455bcaeee777ce1e76d1ea549f3f460093d31fa1db42efe102c5af656e7f4c4ca13606ff45f124eb5a4937bac7","name":"dashboard_correlations.csv","type":"text/csv","lastModified":1783143727155,"size":201131},{"id":"6c40e9f04f70205ea82bb18f631001dde3bebd59861a24c9e11053bf1ce6b5547b5e08a8af8d72672896006fd24f18a9ed481af5f41fee49bef060d5738305a2","href":"https://static.observableusercontent.com/files/6c40e9f04f70205ea82bb18f631001dde3bebd59861a24c9e11053bf1ce6b5547b5e08a8af8d72672896006fd24f18a9ed481af5f41fee49bef060d5738305a2","name":"dashboard_timeseries.csv","type":"text/csv","lastModified":1783143730624,"size":14183049}] 2a:{"title":"California Fisheries Climate Dashboard","cells":[{"id":0,"value":"# California Fisheries Climate Dashboard","pinned":false,"hidden":false,"mode":"md"},{"id":78,"value":"This interactive dashboard explores historical relationships between California commercial fisheries and ocean climate using lagged annual mean Relative Oceanic Niño Index (RONI). Users can investigate species-specific climate responses over time and across fishery metrics (commercial landings, ex-vessel value, and price per pound) while comparing relative climate sensitivity among commercial fisheries.","pinned":false,"hidden":false,"mode":"md"},{"id":98,"value":"$2c","pinned":true,"hidden":false,"mode":"ojs"},{"id":16,"value":"ts = FileAttachment(\"dashboard_timeseries.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs"},{"id":18,"value":"corr = FileAttachment(\"dashboard_correlations.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs"},{"id":20,"value":"sensitivity = FileAttachment(\"dashboard_sensitivity.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs"},{"id":22,"value":"ts.slice(0,5)","pinned":true,"hidden":false,"mode":"ojs"},{"id":24,"value":"ts.length","pinned":true,"hidden":false,"mode":"ojs"},{"id":26,"value":"corr.length","pinned":true,"hidden":false,"mode":"ojs"},{"id":28,"value":"sensitivity.length","pinned":true,"hidden":false,"mode":"ojs"},{"id":30,"value":"species = [...new Set(ts.map(d => d[\"Species Name\"]))].sort()","pinned":true,"hidden":false,"mode":"ojs"},{"id":32,"value":"species.length","pinned":true,"hidden":false,"mode":"ojs"},{"id":34,"value":"viewof selectedSpecies = Inputs.select(\n species,\n {\n label: \"Species\",\n value: \"Halibut, California\"\n }\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":36,"value":"selectedSpecies","pinned":true,"hidden":false,"mode":"ojs"},{"id":38,"value":"viewof selectedLag = Inputs.range([0, 10], {\n step: 1,\n value: 5,\n label: \"Climate lag in years\"\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":40,"value":"viewof selectedMetrics = Inputs.checkbox(\n [\n \"Commercial Landings\",\n \"Ex-vessel Value\",\n \"Price per Pound\",\n \"Annual Mean RONI\"\n ],\n {\n label: \"Display metrics\",\n value: [\"Commercial Landings\", \"Annual Mean RONI\"]\n }\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":42,"value":"plotData = ts\n .filter(d =>\n d[\"Species Name\"] === selectedSpecies &&\n d.Lag === selectedLag &&\n selectedMetrics.includes(d.Variable)\n )\n .map(d => ({\n ...d,\n YearLabel: String(d.Year)\n }))","pinned":true,"hidden":false,"mode":"ojs"},{"id":44,"value":"plotData.slice(0, 10)","pinned":true,"hidden":false,"mode":"ojs"},{"id":83,"value":"$2d","pinned":true,"hidden":false,"mode":"ojs"},{"id":46,"value":"$2e","pinned":true,"hidden":false,"mode":"ojs"},{"id":60,"value":"bands = [\n {phase: \"Warm\", y1: 0, y2: 3.5},\n {phase: \"Cool\", y1: -2, y2: 0}\n]","pinned":true,"hidden":false,"mode":"ojs"},{"id":87,"value":"currentCorr = corr.filter(d =>\n d[\"Species Name\"] === selectedSpecies &&\n d.Lag === selectedLag\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":89,"value":"bestCorr = sensitivity.filter(d =>\n d[\"Species Name\"] === selectedSpecies\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":106,"value":"metricPhrase = new Map([\n [\"Commercial Landings\", \"commercial landings\"],\n [\"Ex-vessel Value\", \"total ex-vessel value\"],\n [\"Price per Pound\", \"average ex-vessel price per pound\"]\n])","pinned":true,"hidden":false,"mode":"ojs"},{"id":108,"value":"$2f","pinned":true,"hidden":false,"mode":"ojs"},{"id":91,"value":"$30","pinned":true,"hidden":false,"mode":"ojs"},{"id":93,"value":"sensitivityLandings = sensitivity\n .filter(d => d[\"Fishery Metric\"] === \"Commercial Landings\")\n .map(d => ({\n ...d,\n Sensitivity: Math.abs(d.Best_Pearson_r),\n Association: d.Best_Pearson_r >= 0 ? \"Warm-water\" : \"Cold-water\"\n }))\n .sort((a, b) => b.Sensitivity - a.Sensitivity)","pinned":true,"hidden":false,"mode":"ojs"},{"id":95,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}