").addClass(errClass).css("position", "absolute")
- .css("top", el.offsetTop)
- .css("left", el.offsetLeft)
- // setting width can push out the page size, forcing otherwise
- // unnecessary scrollbars to appear and making it impossible for
- // the element to shrink; so use max-width instead
- .css("maxWidth", el.offsetWidth)
- .css("height", el.offsetHeight);
- errorDiv.text(err.message);
- $el.after(errorDiv);
-
- // Really dumb way to keep the size/position of the error in sync with
- // the parent element as the window is resized or whatever.
- var intId = setInterval(function() {
- if (!errorDiv[0].parentElement) {
- clearInterval(intId);
- return;
- }
- errorDiv
- .css("top", el.offsetTop)
- .css("left", el.offsetLeft)
- .css("maxWidth", el.offsetWidth)
- .css("height", el.offsetHeight);
- }, 500);
- }
- }
- },
- clearError: function(el) {
- var $el = $(el);
- var display = $el.data("restore-display-mode");
- $el.data("restore-display-mode", null);
-
- if (display === "inline" || display === "inline-block") {
- if (display)
- $el.css("display", display);
- $(el.nextSibling).filter(".htmlwidgets-error").remove();
- } else if (display === "block"){
- $el.css("visibility", "inherit");
- $(el.nextSibling).filter(".htmlwidgets-error").remove();
- }
- },
- sizing: {}
- };
-
- // Called by widget bindings to register a new type of widget. The definition
- // object can contain the following properties:
- // - name (required) - A string indicating the binding name, which will be
- // used by default as the CSS classname to look for.
- // - initialize (optional) - A function(el) that will be called once per
- // widget element; if a value is returned, it will be passed as the third
- // value to renderValue.
- // - renderValue (required) - A function(el, data, initValue) that will be
- // called with data. Static contexts will cause this to be called once per
- // element; Shiny apps will cause this to be called multiple times per
- // element, as the data changes.
- window.HTMLWidgets.widget = function(definition) {
- if (!definition.name) {
- throw new Error("Widget must have a name");
- }
- if (!definition.type) {
- throw new Error("Widget must have a type");
- }
- // Currently we only support output widgets
- if (definition.type !== "output") {
- throw new Error("Unrecognized widget type '" + definition.type + "'");
- }
- // TODO: Verify that .name is a valid CSS classname
-
- // Support new-style instance-bound definitions. Old-style class-bound
- // definitions have one widget "object" per widget per type/class of
- // widget; the renderValue and resize methods on such widget objects
- // take el and instance arguments, because the widget object can't
- // store them. New-style instance-bound definitions have one widget
- // object per widget instance; the definition that's passed in doesn't
- // provide renderValue or resize methods at all, just the single method
- // factory(el, width, height)
- // which returns an object that has renderValue(x) and resize(w, h).
- // This enables a far more natural programming style for the widget
- // author, who can store per-instance state using either OO-style
- // instance fields or functional-style closure variables (I guess this
- // is in contrast to what can only be called C-style pseudo-OO which is
- // what we required before).
- if (definition.factory) {
- definition = createLegacyDefinitionAdapter(definition);
- }
-
- if (!definition.renderValue) {
- throw new Error("Widget must have a renderValue function");
- }
-
- // For static rendering (non-Shiny), use a simple widget registration
- // scheme. We also use this scheme for Shiny apps/documents that also
- // contain static widgets.
- window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
- // Merge defaults into the definition; don't mutate the original definition.
- var staticBinding = extend({}, defaults, definition);
- overrideMethod(staticBinding, "find", function(superfunc) {
- return function(scope) {
- var results = superfunc(scope);
- // Filter out Shiny outputs, we only want the static kind
- return filterByClass(results, "html-widget-output", false);
- };
- });
- window.HTMLWidgets.widgets.push(staticBinding);
-
- if (shinyMode) {
- // Shiny is running. Register the definition with an output binding.
- // The definition itself will not be the output binding, instead
- // we will make an output binding object that delegates to the
- // definition. This is because we foolishly used the same method
- // name (renderValue) for htmlwidgets definition and Shiny bindings
- // but they actually have quite different semantics (the Shiny
- // bindings receive data that includes lots of metadata that it
- // strips off before calling htmlwidgets renderValue). We can't
- // just ignore the difference because in some widgets it's helpful
- // to call this.renderValue() from inside of resize(), and if
- // we're not delegating, then that call will go to the Shiny
- // version instead of the htmlwidgets version.
-
- // Merge defaults with definition, without mutating either.
- var bindingDef = extend({}, defaults, definition);
-
- // This object will be our actual Shiny binding.
- var shinyBinding = new Shiny.OutputBinding();
-
- // With a few exceptions, we'll want to simply use the bindingDef's
- // version of methods if they are available, otherwise fall back to
- // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
- // methods in the future, and we want them to be overrideable by
- // HTMLWidget binding definitions, then we'll need to add them to this
- // list.
- delegateMethod(shinyBinding, bindingDef, "getId");
- delegateMethod(shinyBinding, bindingDef, "onValueChange");
- delegateMethod(shinyBinding, bindingDef, "onValueError");
- delegateMethod(shinyBinding, bindingDef, "renderError");
- delegateMethod(shinyBinding, bindingDef, "clearError");
- delegateMethod(shinyBinding, bindingDef, "showProgress");
-
- // The find, renderValue, and resize are handled differently, because we
- // want to actually decorate the behavior of the bindingDef methods.
-
- shinyBinding.find = function(scope) {
- var results = bindingDef.find(scope);
-
- // Only return elements that are Shiny outputs, not static ones
- var dynamicResults = results.filter(".html-widget-output");
-
- // It's possible that whatever caused Shiny to think there might be
- // new dynamic outputs, also caused there to be new static outputs.
- // Since there might be lots of different htmlwidgets bindings, we
- // schedule execution for later--no need to staticRender multiple
- // times.
- if (results.length !== dynamicResults.length)
- scheduleStaticRender();
-
- return dynamicResults;
- };
-
- // Wrap renderValue to handle initialization, which unfortunately isn't
- // supported natively by Shiny at the time of this writing.
-
- shinyBinding.renderValue = function(el, data) {
- Shiny.renderDependencies(data.deps);
- // Resolve strings marked as javascript literals to objects
- if (!(data.evals instanceof Array)) data.evals = [data.evals];
- for (var i = 0; data.evals && i < data.evals.length; i++) {
- window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
- }
- if (!bindingDef.renderOnNullValue) {
- if (data.x === null) {
- el.style.visibility = "hidden";
- return;
- } else {
- el.style.visibility = "inherit";
- }
- }
- if (!elementData(el, "initialized")) {
- initSizing(el);
-
- elementData(el, "initialized", true);
- if (bindingDef.initialize) {
- var rect = el.getBoundingClientRect();
- var result = bindingDef.initialize(el, rect.width, rect.height);
- elementData(el, "init_result", result);
- }
- }
- bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
- evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
- };
-
- // Only override resize if bindingDef implements it
- if (bindingDef.resize) {
- shinyBinding.resize = function(el, width, height) {
- // Shiny can call resize before initialize/renderValue have been
- // called, which doesn't make sense for widgets.
- if (elementData(el, "initialized")) {
- bindingDef.resize(el, width, height, elementData(el, "init_result"));
- }
- };
- }
-
- Shiny.outputBindings.register(shinyBinding, bindingDef.name);
- }
- };
-
- var scheduleStaticRenderTimerId = null;
- function scheduleStaticRender() {
- if (!scheduleStaticRenderTimerId) {
- scheduleStaticRenderTimerId = setTimeout(function() {
- scheduleStaticRenderTimerId = null;
- window.HTMLWidgets.staticRender();
- }, 1);
- }
- }
-
- // Render static widgets after the document finishes loading
- // Statically render all elements that are of this widget's class
- window.HTMLWidgets.staticRender = function() {
- var bindings = window.HTMLWidgets.widgets || [];
- forEach(bindings, function(binding) {
- var matches = binding.find(document.documentElement);
- forEach(matches, function(el) {
- var sizeObj = initSizing(el, binding);
-
- var getSize = function(el) {
- if (sizeObj) {
- return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
- } else {
- var rect = el.getBoundingClientRect();
- return {w: rect.width, h: rect.height}
- }
- };
-
- if (hasClass(el, "html-widget-static-bound"))
- return;
- el.className = el.className + " html-widget-static-bound";
-
- var initResult;
- if (binding.initialize) {
- var size = getSize(el);
- initResult = binding.initialize(el, size.w, size.h);
- elementData(el, "init_result", initResult);
- }
-
- if (binding.resize) {
- var lastSize = getSize(el);
- var resizeHandler = function(e) {
- var size = getSize(el);
- if (size.w === 0 && size.h === 0)
- return;
- if (size.w === lastSize.w && size.h === lastSize.h)
- return;
- lastSize = size;
- binding.resize(el, size.w, size.h, initResult);
- };
-
- on(window, "resize", resizeHandler);
-
- // This is needed for cases where we're running in a Shiny
- // app, but the widget itself is not a Shiny output, but
- // rather a simple static widget. One example of this is
- // an rmarkdown document that has runtime:shiny and widget
- // that isn't in a render function. Shiny only knows to
- // call resize handlers for Shiny outputs, not for static
- // widgets, so we do it ourselves.
- if (window.jQuery) {
- window.jQuery(document).on(
- "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
- resizeHandler
- );
- window.jQuery(document).on(
- "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
- resizeHandler
- );
- }
-
- // This is needed for the specific case of ioslides, which
- // flips slides between display:none and display:block.
- // Ideally we would not have to have ioslide-specific code
- // here, but rather have ioslides raise a generic event,
- // but the rmarkdown package just went to CRAN so the
- // window to getting that fixed may be long.
- if (window.addEventListener) {
- // It's OK to limit this to window.addEventListener
- // browsers because ioslides itself only supports
- // such browsers.
- on(document, "slideenter", resizeHandler);
- on(document, "slideleave", resizeHandler);
- }
- }
-
- var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
- if (scriptData) {
- var data = JSON.parse(scriptData.textContent || scriptData.text);
- // Resolve strings marked as javascript literals to objects
- if (!(data.evals instanceof Array)) data.evals = [data.evals];
- for (var k = 0; data.evals && k < data.evals.length; k++) {
- window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
- }
- binding.renderValue(el, data.x, initResult);
- evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
- }
- });
- });
-
- invokePostRenderHandlers();
- }
-
-
- function has_jQuery3() {
- if (!window.jQuery) {
- return false;
- }
- var $version = window.jQuery.fn.jquery;
- var $major_version = parseInt($version.split(".")[0]);
- return $major_version >= 3;
- }
-
- /*
- / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
- / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
- / really means $(setTimeout(fn)).
- / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
- /
- / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
- / one tick later than it did before, which means staticRender() is
- / called renderValue() earlier than (advanced) widget authors might be expecting.
- / https://github.com/rstudio/shiny/issues/2630
- /
- / For a concrete example, leaflet has some methods (e.g., updateBounds)
- / which reference Shiny methods registered in initShiny (e.g., setInputValue).
- / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
- / delay execution of those methods (until Shiny methods are ready)
- / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
- /
- / Ideally widget authors wouldn't need to use this setTimeout() hack that
- / leaflet uses to call Shiny methods on a staticRender(). In the long run,
- / the logic initShiny should be broken up so that method registration happens
- / right away, but binding happens later.
- */
- function maybeStaticRenderLater() {
- if (shinyMode && has_jQuery3()) {
- window.jQuery(window.HTMLWidgets.staticRender);
- } else {
- window.HTMLWidgets.staticRender();
- }
- }
-
- if (document.addEventListener) {
- document.addEventListener("DOMContentLoaded", function() {
- document.removeEventListener("DOMContentLoaded", arguments.callee, false);
- maybeStaticRenderLater();
- }, false);
- } else if (document.attachEvent) {
- document.attachEvent("onreadystatechange", function() {
- if (document.readyState === "complete") {
- document.detachEvent("onreadystatechange", arguments.callee);
- maybeStaticRenderLater();
- }
- });
- }
-
-
- window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
- // If no key, default to the first item
- if (typeof(key) === "undefined")
- key = 1;
-
- var link = document.getElementById(depname + "-" + key + "-attachment");
- if (!link) {
- throw new Error("Attachment " + depname + "/" + key + " not found in document");
- }
- return link.getAttribute("href");
- };
-
- window.HTMLWidgets.dataframeToD3 = function(df) {
- var names = [];
- var length;
- for (var name in df) {
- if (df.hasOwnProperty(name))
- names.push(name);
- if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
- throw new Error("All fields must be arrays");
- } else if (typeof(length) !== "undefined" && length !== df[name].length) {
- throw new Error("All fields must be arrays of the same length");
- }
- length = df[name].length;
- }
- var results = [];
- var item;
- for (var row = 0; row < length; row++) {
- item = {};
- for (var col = 0; col < names.length; col++) {
- item[names[col]] = df[names[col]][row];
- }
- results.push(item);
- }
- return results;
- };
-
- window.HTMLWidgets.transposeArray2D = function(array) {
- if (array.length === 0) return array;
- var newArray = array[0].map(function(col, i) {
- return array.map(function(row) {
- return row[i]
- })
- });
- return newArray;
- };
- // Split value at splitChar, but allow splitChar to be escaped
- // using escapeChar. Any other characters escaped by escapeChar
- // will be included as usual (including escapeChar itself).
- function splitWithEscape(value, splitChar, escapeChar) {
- var results = [];
- var escapeMode = false;
- var currentResult = "";
- for (var pos = 0; pos < value.length; pos++) {
- if (!escapeMode) {
- if (value[pos] === splitChar) {
- results.push(currentResult);
- currentResult = "";
- } else if (value[pos] === escapeChar) {
- escapeMode = true;
- } else {
- currentResult += value[pos];
- }
- } else {
- currentResult += value[pos];
- escapeMode = false;
- }
- }
- if (currentResult !== "") {
- results.push(currentResult);
- }
- return results;
- }
- // Function authored by Yihui/JJ Allaire
- window.HTMLWidgets.evaluateStringMember = function(o, member) {
- var parts = splitWithEscape(member, '.', '\\');
- for (var i = 0, l = parts.length; i < l; i++) {
- var part = parts[i];
- // part may be a character or 'numeric' member name
- if (o !== null && typeof o === "object" && part in o) {
- if (i == (l - 1)) { // if we are at the end of the line then evalulate
- if (typeof o[part] === "string")
- o[part] = tryEval(o[part]);
- } else { // otherwise continue to next embedded object
- o = o[part];
- }
- }
- }
- };
-
- // Retrieve the HTMLWidget instance (i.e. the return value of an
- // HTMLWidget binding's initialize() or factory() function)
- // associated with an element, or null if none.
- window.HTMLWidgets.getInstance = function(el) {
- return elementData(el, "init_result");
- };
-
- // Finds the first element in the scope that matches the selector,
- // and returns the HTMLWidget instance (i.e. the return value of
- // an HTMLWidget binding's initialize() or factory() function)
- // associated with that element, if any. If no element matches the
- // selector, or the first matching element has no HTMLWidget
- // instance associated with it, then null is returned.
- //
- // The scope argument is optional, and defaults to window.document.
- window.HTMLWidgets.find = function(scope, selector) {
- if (arguments.length == 1) {
- selector = scope;
- scope = document;
- }
-
- var el = scope.querySelector(selector);
- if (el === null) {
- return null;
- } else {
- return window.HTMLWidgets.getInstance(el);
- }
- };
-
- // Finds all elements in the scope that match the selector, and
- // returns the HTMLWidget instances (i.e. the return values of
- // an HTMLWidget binding's initialize() or factory() function)
- // associated with the elements, in an array. If elements that
- // match the selector don't have an associated HTMLWidget
- // instance, the returned array will contain nulls.
- //
- // The scope argument is optional, and defaults to window.document.
- window.HTMLWidgets.findAll = function(scope, selector) {
- if (arguments.length == 1) {
- selector = scope;
- scope = document;
- }
-
- var nodes = scope.querySelectorAll(selector);
- var results = [];
- for (var i = 0; i < nodes.length; i++) {
- results.push(window.HTMLWidgets.getInstance(nodes[i]));
- }
- return results;
- };
-
- var postRenderHandlers = [];
- function invokePostRenderHandlers() {
- while (postRenderHandlers.length) {
- var handler = postRenderHandlers.shift();
- if (handler) {
- handler();
- }
- }
- }
-
- // Register the given callback function to be invoked after the
- // next time static widgets are rendered.
- window.HTMLWidgets.addPostRenderHandler = function(callback) {
- postRenderHandlers.push(callback);
- };
-
- // Takes a new-style instance-bound definition, and returns an
- // old-style class-bound definition. This saves us from having
- // to rewrite all the logic in this file to accomodate both
- // types of definitions.
- function createLegacyDefinitionAdapter(defn) {
- var result = {
- name: defn.name,
- type: defn.type,
- initialize: function(el, width, height) {
- return defn.factory(el, width, height);
- },
- renderValue: function(el, x, instance) {
- return instance.renderValue(x);
- },
- resize: function(el, width, height, instance) {
- return instance.resize(width, height);
- }
- };
-
- if (defn.find)
- result.find = defn.find;
- if (defn.renderError)
- result.renderError = defn.renderError;
- if (defn.clearError)
- result.clearError = defn.clearError;
-
- return result;
- }
-})();
diff --git a/docs/libs/jquery-3.6.0/jquery-3.6.0.min.js b/docs/libs/jquery-3.6.0/jquery-3.6.0.min.js
deleted file mode 100644
index c4c6022..0000000
--- a/docs/libs/jquery-3.6.0/jquery-3.6.0.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
-/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
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- 5 Timor SSF nutrient profiles | Modelling scenarios for nutrient-sensitive fisheries management
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Timor SSF nutrient profiles
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Methods
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In this section, we identified recurrent nutritional profiles based on RC data , then, we predicted and explained the nutritional profiles on the basis of the fishing strategy and environmental factors.
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Data analysis design and subset division
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As a first step we addressed the inherent imbalance in the RC data , a critical aspect for ensuring accurate and unbiased analysis. Notably, a substantial portion of the data, exceeding 40%, is from Atauro, with gill net being the most frequently reported gear type across all the municipalities. To mitigate the skew caused by this overrepresentation, we strategically divided the dataset into four distinct subsets:
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-Atauro GN: Focused on data from Atauro using gill nets.
Atauro AG: Included data from Atauro using fishing methods other than gill nets.
Mainland GN: Comprised of gill net data from all municipalities excluding Atauro.
Mainland AG: Encompassed data from all other municipalities using non-gill net fishing methods.
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This subdivision of the dataset was intended to reduce biases and enhance analytical precision. Furthermore, by isolating gill net data, we were able to specifically examine the impact of mesh size on the prediction of nutrient profiles in gill net catches, providing a more focused and detailed analysis of this gear type’s influence on nutritional outcomes.
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Clustering and Classification
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After data partition, we identified recurrent nutritional profiles for each dataset. We assessed the total within sum of square (WSS) of six nutrient concentrations—excluding selenium—to identify the optimal number of clusters (distinctive nutritional profiles). Once established the optimal number of clusters for each dataset, we proceeded with the K-means clustering method to organize the data into distinct groups based on similarities in nutrient concentrations. Each trip was grouped based on its nutrient concentration profile, thereby enabling us to discern patterns and categorize trips according to their nutritional profile. The K-means algorithm functions by assigning each data point to the nearest cluster, based on the mean value of the points in the cluster. This iterative process continues until the assignment of points to clusters no longer changes, indicating that the clusters are as distinct as possible. The result is a set of clusters that represent unique nutritional profiles, each characterized by a specific combination of nutrient concentrations.
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Subsequent to the clustering, we conducted Permutational Multivariate Analysis of Variance (PERMANOVA) to validate the clustering methodology across four distinct datasets: Atauro AG, Atauro GN, Mainland AG, and Mainland GN. PERMANOVA is a robust non-parametric statistical test that evaluates whether there are significant differences between groups. Unlike traditional ANOVA, PERMANOVA does not rely on assumptions of normality and is therefore suitable for ecological data, which often do not follow normal distributions. Our PERMANOVA analysis was conducted on each of the four subsets on a distance matrix representing pairwise dissimilarities in nutrient concentrations across all fishing trips. This approach allowed us to test the hypothesis that the nutrient profiles of fishing trips within the same cluster are more similar to each other than to trips in different clusters.
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Finally, we performed a XGBoost model to each data subset to predict the nutritional profiles based on the fishing strategy, habitat and season. We employed the XGBoost algorithm due to its effectiveness in preventing overfitting and its ability to highlight key predictors. We used mesh size, habitat, quarter of the year, and vessel type as predictors for gill net subsets. For other gear types, the models used habitat x gear interaction, habitat, gear type, quarter of the year, and vessel type as predictors.
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Model tuning was conducted dynamically, adjusting several parameters including the number of trees, tree depth, loss reduction, sample size, and early stopping. The 4 data subsets were split into training (80%) and testing (20%) sets, with 10-fold cross-validation applied to the training set for enhanced accuracy and generalizability. The models’ performance was assessed using accuracy, ROC AUC, sensitivity, and specificity, providing a comprehensive understanding of their ability to accurately distinguish between different nutritional profiles. The ROC curves and AUC values offered an additional layer of model effectiveness evaluation.
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We employed SHapley Additive exPlanations (SHAP) values to dissect and quantify the influence of various predictors on the nutritional profiles predicted by our XGBoost models. SHAP values, rooted in cooperative game theory, offer a nuanced approach to understanding machine learning model outputs. They decompose a model’s prediction into contributions from each feature, illuminating not only the significance of these features but also the direction of their impact on the prediction. Specifically, for subsets involving gill net fishing methods (Atauro GN and Mainland GN), our focus was on understanding the impact of mesh size. In contrast, for the other subsets (Atauro AG and Mainland AG), which included different fishing methods, we concentrated on analyzing how the habitat and gear type interacted and influenced the nutritional profile predictions.
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Results
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Clusters
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The WSS analysis indicated that either 4 or 5 clusters were the best for organizing each subset of our data. We decided to use 5 clusters for all subsets to maintain uniformity across our analyses and to better represent the varied patterns in nutrient profiles.
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The bar chart (Figure 5.1) displaying nutrient adequacy across nutrient profiles indicated the number of individuals meeting the Recommended Nutrient Intake (RNI) per 1kg of catch for various nutrients. The profiles are the result of k-means clustering, reflecting distinct groupings based on the type and quantity of nutrients present in the catch. For the Atauro dataset using all gear types (Panel a),we observe diverse distributions of nutrient adequacy across the profiles Specifically, clusters 3 and 5 exhibit a notably higher content of vitamin A relative to the other clusters, whereas calcium and protein appear more evenly distributed among all nutrient profiles. The distribution of zinc varies greatly, with cluster 1 showing the greatest concentration. Iron is most abundant in cluster 4, distinguishing it from the rest.
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For the subset of data from Atauro using only gill net gear (Panel b), the distribution is characterized by higher proportions of calcium in clusters 2 and 4. Additionally, clusters 1 and 4 stand out due to their higher vitamin A content….etc…etc…
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The scatter plot from the k-means clustering (Figure 5.2) showed the distribution of nutrient profiles across different clusters in each data subset. The first two principal components explained a significant portion of the variance, indicating distinct groupings in nutrient profiles among the fishing trips.
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The PERMANOVA analyses (Table 5.1) revealed statistically significant differences between clusters, suggesting robust groupings based on the nutrient profiles. The pseudo-F statistics were remarkably high in all cases, indicating strong differentiation between clusters. Specifically, the R² values were 0.86, 0.82, 0.85, and 0.92 for Atauro AG, Atauro GN, Mainland AG, and Mainland GN respectively, indicating that between 82% to 92% of the variance in nutrient concentrations was explained by the clusters. The high R² values underscore the distinctness of the clusters, reinforcing the validity of the K-means clustering.
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These findings were consistent across all the datasets, with p-values below 0.001, providing clear evidence to reject the null hypothesis of no difference between clusters. Hence, the PERMANOVA results robustly support the effectiveness of the K-means algorithm in capturing meaningful patterns in nutrient profiles.
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Table 5.1: Results of PERMANOVA analysis assessing the homogeneity of nutrient profiles within fishing trip clusters. The analysis was conducted across four datasets: Atauro with all gears (atauro_AG), Atauro with gill nets (atauro_GN), Mainland with all gears (mainland_AG), and Mainland with gill nets (mainland_GN). For each dataset, the term ‘clusters’ represents the within-group sum of squares (SUMOFSQS), which measures the variance within the nutritional profiles, while ‘Residual’ represents the variance between nutritional profiles Degrees of Freedom (DF), R-squared values (R2), and associated statistics indicate the strength and significance of the clustering. The R2 value quantifies the proportion of variance explained by the clusters.
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XGBoost model
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In the analysis of the XGBoost model’s predictive performance, both quantitative and visual assessments were conducted, detailed in Table 5.2 and Figure 5.3, respectively. The Receiver Operating Characteristic (ROC) curves (see ML model interpretation ) presented in Figure 5.3 offer a graphical evaluation of the model’s sensitivity and specificity across four subsets of fishing data, categorized by region and gear type. These curves plot the true positive rate against the false positive rate for each nutritional profile group identified within the data.
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An examination of the ROC curves reveals variability in the model’s ability to distinguish between nutritional profile groups. The areas under the curves (AUC) provide a numerical measure of the model’s discriminative power, with a value of 1 representing perfect prediction and 0.5 indicating no discriminative power. While none of the profile groups reach perfection, several demonstrate substantial AUC values, indicating a robust ability to classify observations accurately.
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In comparing these visual findings with the statistical data from Table 5.2, it is observed that subsets from Atauro (both with all gears and gill nets) yield higher AUC, accuracy, and kappa statistics, suggesting a more consistent and accurate classification of nutritional profiles. These subsets also show higher sensitivity and specificity, indicating a balanced predictive capability for identifying true positives and true negatives. Conversely, the Mainland subsets exhibit lower performance metrics, indicating a more challenging classification scenario. This is reflected in the ROC curves where the lines for the Mainland subsets are farther from the top-left corner, suggesting a lower true positive rate relative to the false positive rate compared to the Atauro subsets.
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The positive predictive value (PPV) and negative predictive value (NPV), which provide insight into the model’s precision and reliability, also align with the ROC curve analysis, showing higher values for the Atauro subsets. This indicates that when the model predicts a particular nutritional profile for these subsets, it is more likely to be correct. The Matthew’s correlation coefficient (MCC) values, a balanced measure of quality for binary classifications, corroborate the ROC analysis by indicating that the Atauro subsets maintain a higher quality of prediction across classes.
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In summary, the integrated analysis of Table 5.2 and Figure 5.3 reveals a differentiated performance of the XGBoost model across various subsets of fishing data. The model showcases commendable predictive strength in the Atauro subsets, with high AUC, accuracy, and kappa metrics indicating a reliable classification of nutritional profiles. The ROC curve analysis further supports this, with curves for Atauro subsets nearer to the desired top-left corner, denoting higher sensitivity and specificity. In contrast, the Mainland subsets, despite achieving moderate success, suggest an area for improvement, as seen by their relative distance from the optimal point on the ROC curves and lower performance metrics. This suggests that while the model is effective in identifying nutritional profiles in certain contexts, its performance is not uniformly high across all subsets.
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Table 5.2: Performance Metrics for XGBoost Model Across Fishing Data Subsets. This table provides a comprehensive overview of the predictive performance of an XGBoost classification model for four distinct subsets of fishing data: Atauro with all gears (ATAURO AG), Atauro with gill nets (ATAURO GN), Mainland with all gears (MAINLAND AG), and Mainland with gill nets (MAINLAND GN). Key performance indicators include ROC-AUC (area under the receiver operating characteristic curve), accuracy, Kappa (kap), sensitivity (sens), specificity (spec), positive predictive value (ppv), negative predictive value (npv), Matthew’s correlation coefficient (mcc), Youden’s J index (j_index), balanced accuracy (bal_accuracy), detection prevalence, precision, recall, and F measure (f_meas). The metrics collectively reflect the model’s ability to discriminate between nutritional profiles, its overall accuracy, and the balance between the sensitivity and specificity for each subset.
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The analysis of SHAP values from gill net models reveals the interaction between mesh size and habitat in predicting nutrient profiles. In the Atauro region, as depicted in Figure 5.4, smaller mesh sizes (below 40 mm) are consistently linked to a higher prediction of nutrient profile NP1 across various habitats, especially reefs, beaches, and mangroves. This suggests that smaller mesh sizes are generally effective across these diverse marine environments for predicting NP1.
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For nutrient profile NP2, there is a noticeable increase in SHAP values within the 40 to 60 mm mesh size range, with reefs and beaches showing this pattern most clearly. This indicates that medium mesh sizes are particularly predictive of NP2 in these ecological settings.
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Larger mesh sizes, specifically those between 60 and 70 mm, have been associated with nutrient profiles NP3 and NP4 across several habitats, including reefs, beaches, and mangroves. A more specific association is observed with mesh sizes between 70 and 80 mm, which are predominantly linked to predicting NP4. For the largest mesh sizes analyzed, nutrient profile NP5 emerges as the most likely prediction among the various profiles, especially in the Atauro data subset.
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The SHAP values derived from the mainland data present a more varied pattern. Small mesh sizes (less than 35 mm) used in deep water and FAD environments are linked with the prediction of nutrient profiles NP3 and NP4, with the latter also being associated with reef and beach habitats. Mesh sizes in the range of 40 to 65 mm are strong predictors for nutrient profiles NP1 and NP5. Profile NP1 is most commonly predicted in reef and FAD settings, while NP5 is typically associated with deeper waters. At the larger end of the mesh size spectrum, nutrient profile NP2 becomes the most probable prediction, particularly when fishing occurs in deeper habitats.
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SHAP results of all gears models …
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Preliminary considerations
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-By using a profiling approach, we can avoid overfishing and habitat depletion. Indeed, instead of focusing on just one species, we spread our fishing efforts across multiple fish groups when sourcing a particular nutrient.
The results suggest that in order to get a certain nutriotional supply (for example iron-rich foods) we can leverage on a diversified combination of gear types and habitats.
From the results we can infer that gathering more information, particularly from less represented environments and fishing practices, can lead to new opportunities to improve the supply of foods targeting specific nutritional needs.
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References | Modelling scenarios for nutrient-sensitive fisheries management
- 6 In simple terms | Modelling scenarios for nutrient-sensitive fisheries management
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In simple terms
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ML model interpretation
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-ROC Curve: The curve plots the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold settings. The true positive rate is on the y-axis, and the false positive rate is on the x-axis.
Performance: A perfect classifier would have a point in the upper left corner of the graph, where the true positive rate is 1 (or 100%) and the false positive rate is 0. The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test.
Diagonal Line: The dotted diagonal line represents a no-skill classifier (e.g., random guessing). A good classifier stays as far away from this line as possible (toward the upper left corner).
Area Under the Curve (AUC): The area under each ROC curve (AUC) is a measure of the test’s accuracy. An AUC of 0.5 suggests no discrimination (no better than random chance), while an AUC of 1.0 suggests perfect discrimination.
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ML model explanation
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-SHAP values: help in understanding how each predictor in the dataset contributed to each particular prediction. A high positive SHAP value for a feature increases the probability of a certain prediction, while a high negative SHAP value decreases it.
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