lines-distill.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abroad-mileage",
   "metadata": {},
   "source": [
    "# Line transect density estimation\n",
    "\n",
    "## Example analysis of line transect data.\n",
    "\n",
    "Based on https://examples.distancesampling.org/Distance-lines/lines-distill.html by Eric Rexstad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accredited-product",
   "metadata": {},
   "source": [
    "### Make the data available for R session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cloudy-fence",
   "metadata": {},
   "source": [
    "We assume that the [Distance](https://cran.r-project.org/web/packages/Distance/index.html) package has been installed on your computer. The R workspace `wren_lt` contains detections of winter wrens *(Troglodytes troglodytes)* from the line transect surveys of Buckland (**Point transect surveys for songbirds: robust methodologies**, S.T. Buckland. The Auk, Vol 123(2), pp. 345-345. 2006, https://doi.org/10.1093/auk/123.2.345)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "premium-tiffany",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: mrds\n",
      "\n",
      "This is mrds 2.2.4\n",
      "Built: R 4.0.2; ; 2020-12-02 11:32:45 UTC; unix\n",
      "\n",
      "\n",
      "Attaching package: ‘Distance’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:mrds’:\n",
      "\n",
      "    create.bins\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(Distance)\n",
    "data(wren_lt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liable-gender",
   "metadata": {},
   "source": [
    "Examine the first few rows of `wren_lt` using the function `head()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "rising-hacker",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Region.Label</th><th scope=col>Area</th><th scope=col>Sample.Label</th><th scope=col>Effort</th><th scope=col>object</th><th scope=col>distance</th><th scope=col>Study.Area</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Montrave</td><td>33.2</td><td>1</td><td>0.416</td><td> 5</td><td>15</td><td>Montrave 4</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Montrave</td><td>33.2</td><td>1</td><td>0.416</td><td> 6</td><td>80</td><td>Montrave 4</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Montrave</td><td>33.2</td><td>1</td><td>0.416</td><td> 7</td><td>35</td><td>Montrave 4</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Montrave</td><td>33.2</td><td>1</td><td>0.416</td><td> 8</td><td>55</td><td>Montrave 4</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>Montrave</td><td>33.2</td><td>1</td><td>0.416</td><td>12</td><td>12</td><td>Montrave 4</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Montrave</td><td>33.2</td><td>1</td><td>0.416</td><td>13</td><td>75</td><td>Montrave 4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & Region.Label & Area & Sample.Label & Effort & object & distance & Study.Area\\\\\n",
       "  & <fct> & <dbl> & <chr> & <dbl> & <int> & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & Montrave & 33.2 & 1 & 0.416 &  5 & 15 & Montrave 4\\\\\n",
       "\t2 & Montrave & 33.2 & 1 & 0.416 &  6 & 80 & Montrave 4\\\\\n",
       "\t3 & Montrave & 33.2 & 1 & 0.416 &  7 & 35 & Montrave 4\\\\\n",
       "\t4 & Montrave & 33.2 & 1 & 0.416 &  8 & 55 & Montrave 4\\\\\n",
       "\t5 & Montrave & 33.2 & 1 & 0.416 & 12 & 12 & Montrave 4\\\\\n",
       "\t6 & Montrave & 33.2 & 1 & 0.416 & 13 & 75 & Montrave 4\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | Region.Label &lt;fct&gt; | Area &lt;dbl&gt; | Sample.Label &lt;chr&gt; | Effort &lt;dbl&gt; | object &lt;int&gt; | distance &lt;int&gt; | Study.Area &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | Montrave | 33.2 | 1 | 0.416 |  5 | 15 | Montrave 4 |\n",
       "| 2 | Montrave | 33.2 | 1 | 0.416 |  6 | 80 | Montrave 4 |\n",
       "| 3 | Montrave | 33.2 | 1 | 0.416 |  7 | 35 | Montrave 4 |\n",
       "| 4 | Montrave | 33.2 | 1 | 0.416 |  8 | 55 | Montrave 4 |\n",
       "| 5 | Montrave | 33.2 | 1 | 0.416 | 12 | 12 | Montrave 4 |\n",
       "| 6 | Montrave | 33.2 | 1 | 0.416 | 13 | 75 | Montrave 4 |\n",
       "\n"
      ],
      "text/plain": [
       "  Region.Label Area Sample.Label Effort object distance Study.Area\n",
       "1 Montrave     33.2 1            0.416   5     15       Montrave 4\n",
       "2 Montrave     33.2 1            0.416   6     80       Montrave 4\n",
       "3 Montrave     33.2 1            0.416   7     35       Montrave 4\n",
       "4 Montrave     33.2 1            0.416   8     55       Montrave 4\n",
       "5 Montrave     33.2 1            0.416  12     12       Montrave 4\n",
       "6 Montrave     33.2 1            0.416  13     75       Montrave 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(wren_lt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abandoned-combine",
   "metadata": {},
   "source": [
    "Determine the number of detection distances that are not missing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "equipped-disease",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "156"
      ],
      "text/latex": [
       "156"
      ],
      "text/markdown": [
       "156"
      ],
      "text/plain": [
       "[1] 156"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(!is.na(wren_lt$distance))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alleged-double",
   "metadata": {},
   "source": [
    "### Examine the distribution of detection distances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "important-basic",
   "metadata": {},
   "source": [
    "Show the distribution of perpendicular distances for winter wren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "saving-legislature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gQIECDQqEALV5BelLHdNBnfczL9dFJFUhVGdSXpeskBydOSRybPTN6RaAQIECBA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",
      "text/plain": [
       "Plot with title “Winter wren line transects”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(wren_lt$distance, xlab=\"Distance (m)\", main=\"Winter wren line transects\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "african-grammar",
   "metadata": {},
   "source": [
    "### Specify unit conversions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mathematical-deadline",
   "metadata": {},
   "source": [
    "> A guaranteed way to produce incorrect results from your analysis is to misspecify the units distances are measured. The ds function has an argument convert.units where the user provides a value to report density in proper units. Providing an incorrect value will result in estimates that are out by orders of magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "northern-peter",
   "metadata": {},
   "outputs": [],
   "source": [
    "conversion.factor <- convert_units(\"meter\", \"kilometer\", \"hectare\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "creative-friendly",
   "metadata": {},
   "source": [
    "### Fitting a simple detection function model with ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bigger-lodging",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting half-normal key function\n",
      "\n",
      "Key only model: not constraining for monotonicity.\n",
      "\n",
      "AIC= 1418.188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "wren.hn <- ds(data=wren_lt, key=\"hn\", adjustment=NULL,\n",
    "              convert.units=conversion.factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "breeding-syracuse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Summary for distance analysis \n",
       "Number of observations :  156 \n",
       "Distance range         :  0  -  100 \n",
       "\n",
       "Model : Half-normal key function \n",
       "AIC   : 1418.188 \n",
       "\n",
       "Detection function parameters\n",
       "Scale coefficient(s):  \n",
       "            estimate        se\n",
       "(Intercept) 4.105816 0.1327744\n",
       "\n",
       "                      Estimate          SE         CV\n",
       "Average p             0.685037  0.05678866 0.08289868\n",
       "N in covered region 227.724931 21.47288594 0.09429308\n",
       "\n",
       "Summary statistics:\n",
       "    Region Area CoveredArea Effort   n  k       ER    se.ER      cv.ER\n",
       "1 Montrave 33.2       193.2   9.66 156 19 16.14907 1.226096 0.07592366\n",
       "\n",
       "Abundance:\n",
       "  Label Estimate       se        cv      lcl      ucl       df\n",
       "1 Total 39.13286 4.399026 0.1124126 31.30227 48.92235 74.24692\n",
       "\n",
       "Density:\n",
       "  Label Estimate        se        cv       lcl      ucl       df\n",
       "1 Total   1.1787 0.1325008 0.1124126 0.9428394 1.473565 74.24692"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(wren.hn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disabled-lawrence",
   "metadata": {},
   "source": [
    "### The summary function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "jewish-cuisine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SMmkuWtZUXKSlF2J+ref5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BBBBIkIAHMmii\n/E3JvqQtYnAB1UZ5VHk4HU1SgyEM0bSPMlGpqJ2iFR6++2hltJLZ/Lpu9ZZPCvr6QXrrLTSN\nLpWLXmCQZnxpne/Dei+90EWQLxfMbB5gwpfXlXfGKnM75hFAAAEEalggulyght+Wt0MAAQQQ\nSKiACxIPTHC64rNHVygVtSu14hHFZ4wy29T0g28zFvqsjguuzPaL9IPpmQs173uIdkov831J\nhTYXbL7f6I9ZT2yqxy78fLZrluJ7p7zdeUp2IXaDlv1TifZDszQEEEAAAQQQQAABBBCIo4DP\nnrgweF0Zn87Dmr6izFO8zpef+XK5zNZeD7xuWHrh7pouUT5WLlN+o5yr+AyOn7+DErWJmvFz\nb1OOUdzOUrzsZaWPsrPiobjnKHMVrztIcdtW8ePr/SCrXaLHXrdbxvIR6WUPanqg4qLvxfSy\nUzSN2kjN+LmTlF6K389ns7zMz6UhgAACCCCAAAIIIIBAzAWiAslFQBTf2zNbcZF0ldJKyW7Z\nBZLXu6iYoWS+zpt63FnJbF31wPf5eLs30it81uYmZbESPd+XtB2nbJ9edrOmboUWSH7tC5SF\nSvTaLv6yzyr5Sg0Xal9lbGeLe5X1FRoCCCCAAAIIIIAAAgggUJCAi4yNlU5K00qe2VLrG2Vt\n4+d0UDbMWl4VD71vvlfql0r9Sl5wU63/lVLZMVTyMqxGAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBAoR+H9sZEhUniuKVwAA\nAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “Half normal model, wren line transects”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutpoints <- c(0,5,10,15,20,30,40,50,65,80,100)\n",
    "plot(wren.hn, breaks=cutpoints, main=\"Half normal model, wren line transects\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}