jupyter_lab/Topic_12-Constrained_ordination.ipynb

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    "**Author:** Prof. AJ Smit\n",
    "\n",
    "Department of Biodiversity and Conservation Biology\n",
    "\n",
    "University of the Western Cape"
   ]
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    "# Topic 12: Constrained ordination\n",
    "\n",
    "Up to now we have applied indirect gradient analyses. The lecture slides mention several constrained ordinations and provide some theory for three of them, viz. **Redundancy Analysis (RDA)**, **Canonical Correspondence Analysis (CCA)**, and **distance-based Reducndancy Analysis (db-RDA)**. Constrained ordination is sometimes called 'direct gradient analysis'.\n",
    "\n",
    "Constrained ordination is used to extract and summarise the variation in a set of response variables (species data in the case of ecology) that can be explained by some explanatory variables ('constraints'), such as measurements of environmental properties at the places where the species data were collected from. \n",
    "\n",
    "**RDA** is a direct gradient analysis that highlights linear relationships between components of response variables, i.e. variables that are 'redundant' with (i.e. 'explained' by) a set of predictors. RDA is an extension of a PCA with a multiple linear regression. The same constraints inherent in a PCA present themselves in an RDA. Use **vegan**'s `rda()` to perform an RDA.\n",
    "\n",
    "**CCA** is the extension of a CA with multiple regression, and is therefore also based on 𝝌2-metric (dissimilarities). We do not have a choice of spcifying which dissimilarity meric to use. CCA performs best when species distribution follows a unimodal model. Use **vegan**'s `cca()` to perform an RDA.\n",
    "\n",
    "**db-RDA** can be viewed as the extension of a PCoA with multiple regressions. As with a PCoA, we also benefit from being able to specify any dissimilarity matrix as input, and hence this approach is more versatile compared to RDA or CCA. I prefer the db-RDA implemented in **vegan**'s `capscale()`. The help file states: \"Distance-based redundancy analysis (dbRDA) is an ordination method similar to Redundancy Analysis (rda), but it allows non-Euclidean dissimilarity indices, such as Manhattan or Bray–Curtis distance.\"\n",
    "\n",
    "## The seaweed dataset\n",
    "For this example we will use the seaweed data of Smit et al. (2017). For information about the study, see [here](https://github.com/ajsmit/Quantitative_Ecology/blob/main/_GitBook/LaTeX/The_seaweed_data.pdf).\n",
    "\n",
    "I use two data sets. The first, $Y$ (in the file `seaweeds.csv`), comprises distribution records of 847 macroalgal species within each of 58 × 50 km-long sections of the South African coast (updated from Bolton and Stegenga  2002). This represents *ca*. 90% of the known seaweed flora of South Africa, but excludes some very small and/or very rare species for which data are insufficient. The data are from verifiable literature sources and John Bolton and Rob Anderson's own collections, assembled from information collected by teams of phycologists over three decades (Bolton 1986; Stegenga et al. 1997; Bolton and Stegenga 2002; De Clerck et al. 2005). \n",
    "\n",
    "The second, $E$ (in `env.csv`), is a dataset of *in situ* coastal seawater temperatures (Smit et al. 2013) derived from daily measurements over up to 40 years.\n",
    "\n",
    "## Set-up the analysis environment"
   ]
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      "── \u001b[1mAttaching packages\u001b[22m ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──\n",
      "\n",
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      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
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      "\n",
      "Loading required package: permute\n",
      "\n",
      "Loading required package: lattice\n",
      "\n",
      "This is vegan 2.5-7\n",
      "\n",
      "\n",
      "Attaching package: ‘gridExtra’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    combine\n",
      "\n",
      "\n"
     ]
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   ],
   "source": [
    "library(tidyverse)\n",
    "library(betapart)\n",
    "library(vegan)\n",
    "library(gridExtra)\n",
    "library(grid)\n",
    "library(gridBase)\n",
    "library(tidyr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b148cf-0908-4b44-9940-885cae01a910",
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   "source": [
    "Load the seaweed data:"
   ]
  },
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   "execution_count": 2,
   "id": "23758853-1ecd-4abe-900f-8812e4f3e332",
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    {
     "data": {
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       "<ol class=list-inline><li>58</li><li>847</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 58\n",
       "\\item 847\n",
       "\\end{enumerate*}\n"
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       "1. 58\n",
       "2. 847\n",
       "\n",
       "\n"
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       "[1]  58 847"
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   "source": [
    "spp <- read.csv('/Users/ajsmit/Dropbox/R/workshops/Quantitative_Ecology/exercises/diversity/SeaweedsSpp.csv')\n",
    "spp <- dplyr::select(spp, -1)\n",
    "dim(spp)"
   ]
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   "source": [
    "## Set-up the data\n",
    "\n",
    "The first step involves the species table ($Y$). First I compute the Sørensen dissimilarity, which I then decompose into ‘nestedness-resultant’ ($\\beta_\\text{sne}$) and ‘turnover’ ($\\beta_\\text{sim}$) components using the `betapart.core()` and `betapart.pair()` functions of the **betapart** package (Baselga et al., 2017). These are placed into the matrices $Y1$ and $Y2$. It is not necessary to decompose into $Y1$ and $Y2$, but I do so here because I want to focus on the turnover component without a nestedness-resultant influence. Optionally, I can apply a CA, PCoA, or nMDS on $Y$ to find the major patterns in the community data. The formal analysis will use the species data in a distance-based redundancy analyses (db-RDA as per **vegan**'s `capscale()` function) by coupling it with $E$."
   ]
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   "source": [
    "Y.core <- betapart.core(spp) \n",
    "Y.pair <- beta.pair(Y.core, index.family = \"sor\")\n",
    "\n",
    "# Let Y1 be the turnover component (beta-sim):\n",
    "Y1 <- as.matrix(Y.pair$beta.sim)"
   ]
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   "source": [
    "It is now necessary to load the environmental data and some setup files that partition the 58 coastal sections (and the species and environmental data that fall within these sections) into bioregions.\n",
    "\n",
    "The thermal (environmental) data contain various variables, but in the analysis I use only some of them. These data were obtained from many sites along the South African coast, but using interpolation (not included here) I calculated the thermal properties for each of the coastal sections for which seaweed data are available. Consequently we have a data frame with 58 rows and a column for each of the thermal metrics."
   ]
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   "id": "633f2cd0-4760-4408-8751-7b4b2d6b1c0b",
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       "<ol class=list-inline><li>58</li><li>18</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 58\n",
       "\\item 18\n",
       "\\end{enumerate*}\n"
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       "1. 58\n",
       "2. 18\n",
       "\n",
       "\n"
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       "[1] 58 18"
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   "source": [
    "load(\"/Users/ajsmit/Dropbox/R/workshops/Quantitative_Ecology/exercises/diversity/SeaweedEnv.RData\")\n",
    "dim(env)"
   ]
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   "source": [
    "Note that they have the same number of rows as the seaweed data.\n",
    "\n",
    "I select only some of the thermal vars; the rest are collinear with some of the ones I import:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d212a33-e5cd-48a6-aba4-90e02ba9591a",
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   "source": [
    "E1 <- dplyr::select(env, febMean, febRange, febSD, augMean,\n",
    "                    augRange, augSD, annMean, annRange, annSD)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Next I calculate *z*-scores:"
   ]
  },
  {
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   "id": "89f52ff8-bde5-46bf-941b-c50555a52b34",
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   "source": [
    "E1 <- decostand(E1, method = \"standardize\")"
   ]
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   "source": [
    "Four bioregions are recognised for South Africa by Bolton and Anderson (2004) (the variable called `bolton`), namely the Benguela Marine Province (BMP; coastal sections **1**–**17**), the Benguela-Agulhas Transition Zone (B-ATZ; **18**–**22**), the Agulhas Marine Province (AMP; **19**–**43**/**44**) and the East Coast Transition Zone (ECTZ; **44**/**45**–**58**). My plotting functions partition the data into the bioregions and colour code the figures accordingly so I can see regional patterns in $\\beta$-diversity emerging."
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "id": "3287affe-b775-4c19-a404-bca73c8a089c",
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    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>spal.prov</th><th scope=col>spal.ecoreg</th><th scope=col>lombard</th><th scope=col>bolton</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>BMP</td><td>NE</td><td>NamBR</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>BMP</td><td>NE</td><td>NamBR</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>BMP</td><td>NE</td><td>NamBR</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>BMP</td><td>NE</td><td>NamBR</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>BMP</td><td>NE</td><td>NamBR</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>BMP</td><td>NE</td><td>NamBR</td><td>BMP</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & spal.prov & spal.ecoreg & lombard & bolton\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & BMP & NE & NamBR & BMP\\\\\n",
       "\t2 & BMP & NE & NamBR & BMP\\\\\n",
       "\t3 & BMP & NE & NamBR & BMP\\\\\n",
       "\t4 & BMP & NE & NamBR & BMP\\\\\n",
       "\t5 & BMP & NE & NamBR & BMP\\\\\n",
       "\t6 & BMP & NE & NamBR & BMP\\\\\n",
       "\\end{tabular}\n"
      ],
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       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | spal.prov &lt;chr&gt; | spal.ecoreg &lt;chr&gt; | lombard &lt;chr&gt; | bolton &lt;chr&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | BMP | NE | NamBR | BMP |\n",
       "| 2 | BMP | NE | NamBR | BMP |\n",
       "| 3 | BMP | NE | NamBR | BMP |\n",
       "| 4 | BMP | NE | NamBR | BMP |\n",
       "| 5 | BMP | NE | NamBR | BMP |\n",
       "| 6 | BMP | NE | NamBR | BMP |\n",
       "\n"
      ],
      "text/plain": [
       "  spal.prov spal.ecoreg lombard bolton\n",
       "1 BMP       NE          NamBR   BMP   \n",
       "2 BMP       NE          NamBR   BMP   \n",
       "3 BMP       NE          NamBR   BMP   \n",
       "4 BMP       NE          NamBR   BMP   \n",
       "5 BMP       NE          NamBR   BMP   \n",
       "6 BMP       NE          NamBR   BMP   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bioreg <- read.csv('/Users/ajsmit/Dropbox/R/workshops/Quantitative_Ecology/exercises/diversity/bioregions.csv', header = TRUE)\n",
    "head(bioreg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5f213a-d900-4533-b132-84d1e1c3d942",
   "metadata": {},
   "source": [
    "Load the geographic coordinates for the coastal sections:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "bce31bdc-6c68-4459-9945-edbc5e3bc612",
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    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Longitude</th><th scope=col>Latitude</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>16.72429</td><td>-28.98450</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>16.94238</td><td>-29.38053</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>17.08194</td><td>-29.83253</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>17.25928</td><td>-30.26426</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>17.47638</td><td>-30.67874</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>17.72167</td><td>-31.08580</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
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      "text/latex": [
       "A data.frame: 6 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Longitude & Latitude\\\\\n",
       "  & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 16.72429 & -28.98450\\\\\n",
       "\t2 & 16.94238 & -29.38053\\\\\n",
       "\t3 & 17.08194 & -29.83253\\\\\n",
       "\t4 & 17.25928 & -30.26426\\\\\n",
       "\t5 & 17.47638 & -30.67874\\\\\n",
       "\t6 & 17.72167 & -31.08580\\\\\n",
       "\\end{tabular}\n"
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       "\n",
       "A data.frame: 6 × 2\n",
       "\n",
       "| <!--/--> | Longitude &lt;dbl&gt; | Latitude &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 16.72429 | -28.98450 |\n",
       "| 2 | 16.94238 | -29.38053 |\n",
       "| 3 | 17.08194 | -29.83253 |\n",
       "| 4 | 17.25928 | -30.26426 |\n",
       "| 5 | 17.47638 | -30.67874 |\n",
       "| 6 | 17.72167 | -31.08580 |\n",
       "\n"
      ],
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       "  Longitude Latitude \n",
       "1 16.72429  -28.98450\n",
       "2 16.94238  -29.38053\n",
       "3 17.08194  -29.83253\n",
       "4 17.25928  -30.26426\n",
       "5 17.47638  -30.67874\n",
       "6 17.72167  -31.08580"
      ]
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       "<style>\n",
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       "<ol class=list-inline><li>58</li><li>2</li></ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 58\n",
       "\\item 2\n",
       "\\end{enumerate*}\n"
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       "1. 58\n",
       "2. 2\n",
       "\n",
       "\n"
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       "[1] 58  2"
      ]
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   "source": [
    "sites <- read.csv(\"/Users/ajsmit/Dropbox/R/workshops/Quantitative_Ecology/exercises/diversity/sites.csv\")\n",
    "sites <- sites[, c(2, 1)]\n",
    "head(sites)\n",
    "dim(sites)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Again, we have 58 rows of data for both the coastal section coordinates and the bioregions. You may omit the dataset with spatial coordinates as it is not actually used further below. Can you think of ways in which to use this dataset to graphically represent the spatial distribution of some environmental or biodiversity data?"
   ]
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   "source": [
    "## Start the db-RDA\n",
    "\n",
    "I test the niche difference mechanism as the primary species compositional assembly process operating along South African shores. I suggest that the thermal gradient along the coast provides a suite of abiotic (thermal) conditions from which species can select based on their physiological tolerances, and hence this will structure $\\beta$-diversity. For this mechanism to function one would assume that all species have equal access to all sections along this stretch of coast, thus following 'Beijerinck’s Law' that everything is everywhere but the environment selects (Sauer, 1988).\n",
    "\n",
    "I do a RDA involving all the thermal variables in $E1$ (the 'global analysis' resulting in the full model, `rda_full`). Analysis shown for $Y1$:"
   ]
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    {
     "data": {
      "text/plain": [
       "Call: capscale(formula = Y1 ~ febMean + febRange + febSD + augMean +\n",
       "augRange + augSD + annMean + annRange + annSD, data = E1)\n",
       "\n",
       "               Inertia Proportion Rank\n",
       "Total          7.52344    1.00000     \n",
       "Constrained    6.86398    0.91235    8\n",
       "Unconstrained  1.02840    0.13669   28\n",
       "Imaginary     -0.36895   -0.04904   24\n",
       "Inertia is squared Unknown distance \n",
       "Some constraints were aliased because they were collinear (redundant)\n",
       "\n",
       "Eigenvalues for constrained axes:\n",
       " CAP1  CAP2  CAP3  CAP4  CAP5  CAP6  CAP7  CAP8 \n",
       "5.620 1.155 0.074 0.006 0.004 0.003 0.001 0.001 \n",
       "\n",
       "Eigenvalues for unconstrained axes:\n",
       "  MDS1   MDS2   MDS3   MDS4   MDS5   MDS6   MDS7   MDS8 \n",
       "0.5768 0.1687 0.1096 0.0413 0.0322 0.0243 0.0179 0.0103 \n",
       "(Showing 8 of 28 unconstrained eigenvalues)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit the full model:\n",
    "rda_full <- capscale(Y1 ~., E1)\n",
    "rda_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f93eef0-3c72-4aca-99a7-67c1d0bc18d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# summary(rda_full)\n",
    "# notice that the species scores are missing\n",
    "# refer to PCoA for why"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260b0d2c-2e6e-4584-962a-df4c3eb27877",
   "metadata": {},
   "source": [
    "Species information is lost during the calculation of the dissimilarity matrix, but if the original matrix of species composition is available, the species scores can be added back into the ordination diagram as weighted means of site scores in which case they occur or as vectors fitted onto the ordination space."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2751463c-2548-45b1-ab17-79da303a0562",
   "metadata": {},
   "source": [
    "Is the fit significant? I run a permutation test to check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d507a905-2c92-4a97-af02-e8d6f49f913e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A anova.cca: 2 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>SumOfSqs</th><th scope=col>F</th><th scope=col>Pr(&gt;F)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Model</th><td> 8</td><td>6.863982</td><td>40.8807</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>Residual</th><td>49</td><td>1.028405</td><td>     NA</td><td>   NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova.cca: 2 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & SumOfSqs & F & Pr(>F)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tModel &  8 & 6.863982 & 40.8807 & 0.001\\\\\n",
       "\tResidual & 49 & 1.028405 &      NA &    NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova.cca: 2 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;dbl&gt; | SumOfSqs &lt;dbl&gt; | F &lt;dbl&gt; | Pr(&gt;F) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| Model |  8 | 6.863982 | 40.8807 | 0.001 |\n",
       "| Residual | 49 | 1.028405 |      NA |    NA |\n",
       "\n"
      ],
      "text/plain": [
       "         Df SumOfSqs F       Pr(>F)\n",
       "Model     8 6.863982 40.8807 0.001 \n",
       "Residual 49 1.028405      NA    NA "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anova(rda_full, parallel = 4) # ... yes!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d3d806-cedd-4223-b4d0-0b5653d7928b",
   "metadata": {},
   "source": [
    "Since the fit is significant (the environmental variables capture the variation seen in the species data), I compute the adjusted $R^{2}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c47c102f-06cd-4f6d-886b-74c59cce844f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.9"
      ],
      "text/latex": [
       "0.9"
      ],
      "text/markdown": [
       "0.9"
      ],
      "text/plain": [
       "[1] 0.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rda_full_R2 <- RsquareAdj(rda_full)$adj.r.squared\n",
    "round(rda_full_R2, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c886b1-b456-49ba-b88b-855349bf0d27",
   "metadata": {},
   "source": [
    "Inertia accounted for by constraints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f42edb1-fcf1-4734-b44f-c691b63b445f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6.86"
      ],
      "text/latex": [
       "6.86"
      ],
      "text/markdown": [
       "6.86"
      ],
      "text/plain": [
       "[1] 6.86"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(sum(rda_full$CCA$eig), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6273f79-083f-44f0-9345-cb58114e30c7",
   "metadata": {},
   "source": [
    "Remaining (unconstrained) inertia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6200f5e5-a7bb-4f1f-865d-a1626e66167d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1.03"
      ],
      "text/latex": [
       "1.03"
      ],
      "text/markdown": [
       "1.03"
      ],
      "text/plain": [
       "[1] 1.03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(sum(rda_full$CA$eig), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7f628e-f7d7-4fd2-aacb-db9cb548b8e3",
   "metadata": {},
   "source": [
    "Total inertia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b1c23c1-d719-4101-8b08-bd4f840994f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "7.52"
      ],
      "text/latex": [
       "7.52"
      ],
      "text/markdown": [
       "7.52"
      ],
      "text/plain": [
       "[1] 7.52"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(rda_full$tot.chi, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c392c816-139c-4b66-9492-40c511c51969",
   "metadata": {},
   "source": [
    "What is the variation explained by the full set environmental variables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dcf37c40-156d-493b-8b72-8018c474f557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "91.23"
      ],
      "text/latex": [
       "91.23"
      ],
      "text/markdown": [
       "91.23"
      ],
      "text/plain": [
       "[1] 91.23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(sum(rda_full$CCA$eig) / rda_full$tot.chi * 100, 2) # in %"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35782d3c-a53c-4ec6-8897-570c688f7c24",
   "metadata": {},
   "source": [
    "I check for collinearity using variance inflation factors (VIF), and retain a subset of non-collinear variables to include in the 'reduced' or 'final' model. A common rule is that values over 10 indicate redundant constraints. I run the VIF procedure iteratively, each time removing the highest VIF and examining the remaining ones until these are mostly below 10.\n",
    "\n",
    "First on the full model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad33b8d0-d771-450a-99d2-430897024e62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>febMean</dt><dd>91.1297004989349</dd><dt>febRange</dt><dd>6.77595855436087</dd><dt>febSD</dt><dd>7.73443622940163</dd><dt>augMean</dt><dd>73.0903818299856</dd><dt>augRange</dt><dd>8.48663062262088</dd><dt>augSD</dt><dd>12.1189137150005</dd><dt>annMean</dt><dd>233.400745999033</dd><dt>annRange</dt><dd>&lt;NA&gt;</dd><dt>annSD</dt><dd>5.39634338853676</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[febMean] 91.1297004989349\n",
       "\\item[febRange] 6.77595855436087\n",
       "\\item[febSD] 7.73443622940163\n",
       "\\item[augMean] 73.0903818299856\n",
       "\\item[augRange] 8.48663062262088\n",
       "\\item[augSD] 12.1189137150005\n",
       "\\item[annMean] 233.400745999033\n",
       "\\item[annRange] <NA>\n",
       "\\item[annSD] 5.39634338853676\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "febMean\n",
       ":   91.1297004989349febRange\n",
       ":   6.77595855436087febSD\n",
       ":   7.73443622940163augMean\n",
       ":   73.0903818299856augRange\n",
       ":   8.48663062262088augSD\n",
       ":   12.1189137150005annMean\n",
       ":   233.400745999033annRange\n",
       ":   &lt;NA&gt;annSD\n",
       ":   5.39634338853676\n",
       "\n"
      ],
      "text/plain": [
       "   febMean   febRange      febSD    augMean   augRange      augSD    annMean \n",
       " 91.129700   6.775959   7.734436  73.090382   8.486631  12.118914 233.400746 \n",
       "  annRange      annSD \n",
       "        NA   5.396343 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vif.cca(rda_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105ca6cf-eb35-472a-8b40-aad4e70df0e1",
   "metadata": {},
   "source": [
    "Drop annMean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74ff211d-96e0-4aaf-b4b1-baaa76a2eb9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>febMean</dt><dd>24.99615213153</dd><dt>febRange</dt><dd>6.14924546371304</dd><dt>febSD</dt><dd>7.16063735671608</dd><dt>augMean</dt><dd>17.7179357614808</dd><dt>augRange</dt><dd>8.06633959857528</dd><dt>augSD</dt><dd>10.7261165223377</dd><dt>annRange</dt><dd>&lt;NA&gt;</dd><dt>annSD</dt><dd>5.39627542564953</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[febMean] 24.99615213153\n",
       "\\item[febRange] 6.14924546371304\n",
       "\\item[febSD] 7.16063735671608\n",
       "\\item[augMean] 17.7179357614808\n",
       "\\item[augRange] 8.06633959857528\n",
       "\\item[augSD] 10.7261165223377\n",
       "\\item[annRange] <NA>\n",
       "\\item[annSD] 5.39627542564953\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "febMean\n",
       ":   24.99615213153febRange\n",
       ":   6.14924546371304febSD\n",
       ":   7.16063735671608augMean\n",
       ":   17.7179357614808augRange\n",
       ":   8.06633959857528augSD\n",
       ":   10.7261165223377annRange\n",
       ":   &lt;NA&gt;annSD\n",
       ":   5.39627542564953\n",
       "\n"
      ],
      "text/plain": [
       "  febMean  febRange     febSD   augMean  augRange     augSD  annRange     annSD \n",
       "24.996152  6.149245  7.160637 17.717936  8.066340 10.726117        NA  5.396275 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "E2 <- dplyr::select(E1, -annMean)\n",
    "rda_sel1 <- capscale(Y1 ~., E2)\n",
    "vif.cca(rda_sel1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c004106f-44c6-494f-9b25-1f3abe364214",
   "metadata": {},
   "source": [
    "Drop febMean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f97e2bd1-40fb-487f-985c-cae29b0fde34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>febRange</dt><dd>6.14924546371304</dd><dt>febSD</dt><dd>7.16063735671607</dd><dt>augMean</dt><dd>1.61923266783647</dd><dt>augRange</dt><dd>8.06633959857528</dd><dt>augSD</dt><dd>10.7261165223377</dd><dt>annRange</dt><dd>5.52997053562175</dd><dt>annSD</dt><dd>5.39627542564953</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[febRange] 6.14924546371304\n",
       "\\item[febSD] 7.16063735671607\n",
       "\\item[augMean] 1.61923266783647\n",
       "\\item[augRange] 8.06633959857528\n",
       "\\item[augSD] 10.7261165223377\n",
       "\\item[annRange] 5.52997053562175\n",
       "\\item[annSD] 5.39627542564953\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "febRange\n",
       ":   6.14924546371304febSD\n",
       ":   7.16063735671607augMean\n",
       ":   1.61923266783647augRange\n",
       ":   8.06633959857528augSD\n",
       ":   10.7261165223377annRange\n",
       ":   5.52997053562175annSD\n",
       ":   5.39627542564953\n",
       "\n"
      ],
      "text/plain": [
       " febRange     febSD   augMean  augRange     augSD  annRange     annSD \n",
       " 6.149245  7.160637  1.619233  8.066340 10.726117  5.529971  5.396275 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "E3 <- dplyr::select(E2, -febMean)\n",
    "rda_sel2 <- capscale(Y1 ~., E3)\n",
    "vif.cca(rda_sel2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a66535-25cf-4eb3-878c-09f5a5950d5b",
   "metadata": {},
   "source": [
    "We select E3 as the variables to construct the final model (`rda_final`) from. \n",
    "\n",
    "**Note:** you can switch to the formula interface within `capscale()` and specify the variables to use on the right-hand side of the formula (as shown but not executed). You will (obviously) no longer analyse only the turnover component of $\\beta$-diversity as you'll be using the raw `spp` data that encapsulate both nestedness-resultant and turnover processes, but the upshot of this is that you'll now have species scores. Run this bit of code by yourself and see what the outcome is (the ordiplot is affected, as well as the $R^{2}$, number of significant reduced axes, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "edde7ace-2860-4e71-b925-9b3ef2efecb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "rda_final <- rda_sel2\n",
    "# rda_final <- capscale(spp ~ febRange + febSD + augMean + augRange + augSD + annRange + annSD, data = E3, distance = \"jaccard\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff00685-c134-4bc0-8463-df21ee97fb22",
   "metadata": {},
   "source": [
    "We calculate the significance of the model, the variance explained by all the constraints (in $E3$) in the final model, as well as the $R^{2}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d7231ac-0f72-4d18-a2b1-2cae9a8e4814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A anova.cca: 2 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>SumOfSqs</th><th scope=col>F</th><th scope=col>Pr(&gt;F)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Model</th><td> 7</td><td>6.825063</td><td>45.67541</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>Residual</th><td>50</td><td>1.067324</td><td>      NA</td><td>   NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova.cca: 2 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & SumOfSqs & F & Pr(>F)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tModel &  7 & 6.825063 & 45.67541 & 0.001\\\\\n",
       "\tResidual & 50 & 1.067324 &       NA &    NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova.cca: 2 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;dbl&gt; | SumOfSqs &lt;dbl&gt; | F &lt;dbl&gt; | Pr(&gt;F) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| Model |  7 | 6.825063 | 45.67541 | 0.001 |\n",
       "| Residual | 50 | 1.067324 |       NA |    NA |\n",
       "\n"
      ],
      "text/plain": [
       "         Df SumOfSqs F        Pr(>F)\n",
       "Model     7 6.825063 45.67541 0.001 \n",
       "Residual 50 1.067324       NA    NA "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# is the fit significant?\n",
    "anova(rda_final, parallel = 4) # ... yes!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827cd91d-61e6-4d7d-bc46-ea64accb1b14",
   "metadata": {},
   "source": [
    "Which axes are significant?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "795e1907-44cb-4e8c-b7f6-c735a6b98ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A anova.cca: 8 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>SumOfSqs</th><th scope=col>F</th><th scope=col>Pr(&gt;F)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CAP1</th><td> 1</td><td>5.617936009</td><td>263.17859640</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>CAP2</th><td> 1</td><td>1.124244745</td><td> 52.66652264</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>CAP3</th><td> 1</td><td>0.072531264</td><td>  3.39780950</td><td>0.416</td></tr>\n",
       "\t<tr><th scope=row>CAP4</th><td> 1</td><td>0.004952809</td><td>  0.23201998</td><td>1.000</td></tr>\n",
       "\t<tr><th scope=row>CAP5</th><td> 1</td><td>0.002720164</td><td>  0.12742919</td><td>1.000</td></tr>\n",
       "\t<tr><th scope=row>CAP6</th><td> 1</td><td>0.001349479</td><td>  0.06321786</td><td>1.000</td></tr>\n",
       "\t<tr><th scope=row>CAP7</th><td> 1</td><td>0.001328608</td><td>  0.06224014</td><td>1.000</td></tr>\n",
       "\t<tr><th scope=row>Residual</th><td>50</td><td>1.067323879</td><td>          NA</td><td>   NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova.cca: 8 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & SumOfSqs & F & Pr(>F)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tCAP1 &  1 & 5.617936009 & 263.17859640 & 0.001\\\\\n",
       "\tCAP2 &  1 & 1.124244745 &  52.66652264 & 0.001\\\\\n",
       "\tCAP3 &  1 & 0.072531264 &   3.39780950 & 0.416\\\\\n",
       "\tCAP4 &  1 & 0.004952809 &   0.23201998 & 1.000\\\\\n",
       "\tCAP5 &  1 & 0.002720164 &   0.12742919 & 1.000\\\\\n",
       "\tCAP6 &  1 & 0.001349479 &   0.06321786 & 1.000\\\\\n",
       "\tCAP7 &  1 & 0.001328608 &   0.06224014 & 1.000\\\\\n",
       "\tResidual & 50 & 1.067323879 &           NA &    NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova.cca: 8 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;dbl&gt; | SumOfSqs &lt;dbl&gt; | F &lt;dbl&gt; | Pr(&gt;F) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| CAP1 |  1 | 5.617936009 | 263.17859640 | 0.001 |\n",
       "| CAP2 |  1 | 1.124244745 |  52.66652264 | 0.001 |\n",
       "| CAP3 |  1 | 0.072531264 |   3.39780950 | 0.416 |\n",
       "| CAP4 |  1 | 0.004952809 |   0.23201998 | 1.000 |\n",
       "| CAP5 |  1 | 0.002720164 |   0.12742919 | 1.000 |\n",
       "| CAP6 |  1 | 0.001349479 |   0.06321786 | 1.000 |\n",
       "| CAP7 |  1 | 0.001328608 |   0.06224014 | 1.000 |\n",
       "| Residual | 50 | 1.067323879 |           NA |    NA |\n",
       "\n"
      ],
      "text/plain": [
       "         Df SumOfSqs    F            Pr(>F)\n",
       "CAP1      1 5.617936009 263.17859640 0.001 \n",
       "CAP2      1 1.124244745  52.66652264 0.001 \n",
       "CAP3      1 0.072531264   3.39780950 0.416 \n",
       "CAP4      1 0.004952809   0.23201998 1.000 \n",
       "CAP5      1 0.002720164   0.12742919 1.000 \n",
       "CAP6      1 0.001349479   0.06321786 1.000 \n",
       "CAP7      1 0.001328608   0.06224014 1.000 \n",
       "Residual 50 1.067323879           NA    NA "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anova(rda_final, by = \"axis\", parallel = 4) # ... yes!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6166c92f-46e4-4169-a546-f0dfc4aef53c",
   "metadata": {},
   "source": [
    "Extract the significant variables in $E3$ that are influential in the final model as influencers of seaweed community differences amongsth coastal sections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a4cda137-e324-4644-8049-f6f478a616bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A anova.cca: 8 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>SumOfSqs</th><th scope=col>F</th><th scope=col>Pr(&gt;F)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>febRange</th><td> 1</td><td>1.09622899</td><td> 51.3540925</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>febSD</th><td> 1</td><td>0.18497387</td><td>  8.6653111</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>augMean</th><td> 1</td><td>5.38149659</td><td>252.1023232</td><td>0.001</td></tr>\n",
       "\t<tr><th scope=row>augRange</th><td> 1</td><td>0.09026584</td><td>  4.2286060</td><td>0.018</td></tr>\n",
       "\t<tr><th scope=row>augSD</th><td> 1</td><td>0.02117148</td><td>  0.9918020</td><td>0.374</td></tr>\n",
       "\t<tr><th scope=row>annRange</th><td> 1</td><td>0.01962051</td><td>  0.9191449</td><td>0.363</td></tr>\n",
       "\t<tr><th scope=row>annSD</th><td> 1</td><td>0.03130580</td><td>  1.4665559</td><td>0.225</td></tr>\n",
       "\t<tr><th scope=row>Residual</th><td>50</td><td>1.06732388</td><td>         NA</td><td>   NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova.cca: 8 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & SumOfSqs & F & Pr(>F)\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tfebRange &  1 & 1.09622899 &  51.3540925 & 0.001\\\\\n",
       "\tfebSD &  1 & 0.18497387 &   8.6653111 & 0.001\\\\\n",
       "\taugMean &  1 & 5.38149659 & 252.1023232 & 0.001\\\\\n",
       "\taugRange &  1 & 0.09026584 &   4.2286060 & 0.018\\\\\n",
       "\taugSD &  1 & 0.02117148 &   0.9918020 & 0.374\\\\\n",
       "\tannRange &  1 & 0.01962051 &   0.9191449 & 0.363\\\\\n",
       "\tannSD &  1 & 0.03130580 &   1.4665559 & 0.225\\\\\n",
       "\tResidual & 50 & 1.06732388 &          NA &    NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova.cca: 8 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;dbl&gt; | SumOfSqs &lt;dbl&gt; | F &lt;dbl&gt; | Pr(&gt;F) &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| febRange |  1 | 1.09622899 |  51.3540925 | 0.001 |\n",
       "| febSD |  1 | 0.18497387 |   8.6653111 | 0.001 |\n",
       "| augMean |  1 | 5.38149659 | 252.1023232 | 0.001 |\n",
       "| augRange |  1 | 0.09026584 |   4.2286060 | 0.018 |\n",
       "| augSD |  1 | 0.02117148 |   0.9918020 | 0.374 |\n",
       "| annRange |  1 | 0.01962051 |   0.9191449 | 0.363 |\n",
       "| annSD |  1 | 0.03130580 |   1.4665559 | 0.225 |\n",
       "| Residual | 50 | 1.06732388 |          NA |    NA |\n",
       "\n"
      ],
      "text/plain": [
       "         Df SumOfSqs   F           Pr(>F)\n",
       "febRange  1 1.09622899  51.3540925 0.001 \n",
       "febSD     1 0.18497387   8.6653111 0.001 \n",
       "augMean   1 5.38149659 252.1023232 0.001 \n",
       "augRange  1 0.09026584   4.2286060 0.018 \n",
       "augSD     1 0.02117148   0.9918020 0.374 \n",
       "annRange  1 0.01962051   0.9191449 0.363 \n",
       "annSD     1 0.03130580   1.4665559 0.225 \n",
       "Residual 50 1.06732388          NA    NA "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(rda_final_axis_test <- anova(rda_final, by = \"terms\", parallel = 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5287c671-1f00-4a65-8223-3a9b17fdbe62",
   "metadata": {},
   "source": [
    "The significant variables are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5187a553-25eb-4ed4-a771-570ed6633af9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'febRange'</li><li>'febSD'</li><li>'augMean'</li><li>'augRange'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'febRange'\n",
       "\\item 'febSD'\n",
       "\\item 'augMean'\n",
       "\\item 'augRange'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'febRange'\n",
       "2. 'febSD'\n",
       "3. 'augMean'\n",
       "4. 'augRange'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"febRange\" \"febSD\"    \"augMean\"  \"augRange\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rda_final_ax <- which(rda_final_axis_test[, 4] < 0.05)\n",
    "rda_final_sign_ax <- colnames(E3[,rda_final_ax])\n",
    "rda_final_sign_ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4aa16a-4aab-4ed3-ae14-81284cc8cd77",
   "metadata": {},
   "source": [
    "The adjusted $R^{2}$ for the constraints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "57f7665e-5786-4183-acab-35f2472efcaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.89"
      ],
      "text/latex": [
       "0.89"
      ],
      "text/markdown": [
       "0.89"
      ],
      "text/plain": [
       "[1] 0.89"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(rda_final_R2 <- RsquareAdj(rda_final)$adj.r.squared, 2) # %"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf9dca03-d3c4-47c2-bf7a-09bc0c89cb66",
   "metadata": {},
   "source": [
    "Variance explained by reduced (final) model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5ff98134-32c4-4cfb-8c6a-f8d8cebf79d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "90.72"
      ],
      "text/latex": [
       "90.72"
      ],
      "text/markdown": [
       "90.72"
      ],
      "text/plain": [
       "[1] 90.72"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(sum(rda_final$CCA$eig) / rda_final$tot.chi * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51b54f6-0a16-4755-bca5-7799dcb0dd04",
   "metadata": {},
   "source": [
    "The biplot scores for constraining variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c438da8f-bc19-48c0-b79e-d4aa3bb2937f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 7 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>CAP1</th><th scope=col>CAP2</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>febRange</th><td>-0.17951109</td><td>-0.90202271</td></tr>\n",
       "\t<tr><th scope=row>febSD</th><td>-0.08259676</td><td>-0.50969451</td></tr>\n",
       "\t<tr><th scope=row>augMean</th><td> 0.98516406</td><td> 0.15657181</td></tr>\n",
       "\t<tr><th scope=row>augRange</th><td> 0.03499429</td><td>-0.14728687</td></tr>\n",
       "\t<tr><th scope=row>augSD</th><td>-0.01944793</td><td>-0.07441746</td></tr>\n",
       "\t<tr><th scope=row>annRange</th><td> 0.41313533</td><td>-0.18050396</td></tr>\n",
       "\t<tr><th scope=row>annSD</th><td> 0.20412535</td><td>-0.56833391</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 7 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & CAP1 & CAP2\\\\\n",
       "\\hline\n",
       "\tfebRange & -0.17951109 & -0.90202271\\\\\n",
       "\tfebSD & -0.08259676 & -0.50969451\\\\\n",
       "\taugMean &  0.98516406 &  0.15657181\\\\\n",
       "\taugRange &  0.03499429 & -0.14728687\\\\\n",
       "\taugSD & -0.01944793 & -0.07441746\\\\\n",
       "\tannRange &  0.41313533 & -0.18050396\\\\\n",
       "\tannSD &  0.20412535 & -0.56833391\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 7 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | CAP1 | CAP2 |\n",
       "|---|---|---|\n",
       "| febRange | -0.17951109 | -0.90202271 |\n",
       "| febSD | -0.08259676 | -0.50969451 |\n",
       "| augMean |  0.98516406 |  0.15657181 |\n",
       "| augRange |  0.03499429 | -0.14728687 |\n",
       "| augSD | -0.01944793 | -0.07441746 |\n",
       "| annRange |  0.41313533 | -0.18050396 |\n",
       "| annSD |  0.20412535 | -0.56833391 |\n",
       "\n"
      ],
      "text/plain": [
       "         CAP1        CAP2       \n",
       "febRange -0.17951109 -0.90202271\n",
       "febSD    -0.08259676 -0.50969451\n",
       "augMean   0.98516406  0.15657181\n",
       "augRange  0.03499429 -0.14728687\n",
       "augSD    -0.01944793 -0.07441746\n",
       "annRange  0.41313533 -0.18050396\n",
       "annSD     0.20412535 -0.56833391"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores(rda_final, display = \"bp\", choices = c(1:2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995a8f8e-fbd5-40c3-8c6a-b9a12db4dde6",
   "metadata": {},
   "source": [
    "These biplot scores will mark the position of the termini of the arrows that indicate the direction and strength of the constraining variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9a83c3-7015-4b11-a330-3df3585f9de2",
   "metadata": {},
   "source": [
    "## Ordination diagrams"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a55bdf4-b458-404a-8d50-f766c8e82470",
   "metadata": {},
   "source": [
    "This code recreates Figure 2a in Smit et al. (2017):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7de2e6ca-6446-4060-88b1-d567153bac92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q4AAABcW8Yxuc0giGxHAhERUVR7YhjSJSi5rosDCHYAAADgwuxGQcYJuWf0w09T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\nAAAAHPF/wI0ukbK2IfoAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use scaling = 1 or scaling = 2 for site and species scaling, respectively\n",
    "rda_final_scrs <- scores(rda_final, display = c(\"sp\", \"wa\", \"lc\", \"bp\"))\n",
    "# see ?plot.cca for insight into the use of lc vs wa scores\n",
    "# below I splot the wa (site) scores rather than lc (constraints) scores\n",
    "site_scores <- data.frame(rda_final_scrs$site) # the wa scores\n",
    "site_scores$bioreg <- bioreg$bolton\n",
    "site_scores$section <- seq(1:58)\n",
    "\n",
    "biplot_scores <- data.frame(rda_final_scrs$biplot)\n",
    "biplot_scores$labels <- rownames(biplot_scores)\n",
    "biplot_scores_sign <- biplot_scores[biplot_scores$labels %in% rda_final_sign_ax,]\n",
    "\n",
    "ggplot(data = site_scores, aes(x = CAP1, y = CAP2, colour = bioreg)) +\n",
    "  geom_point(size = 5.0, shape = 24, fill = \"white\") +\n",
    "  geom_text(aes(label = section), size = 3.0, col = \"black\") +\n",
    "  geom_label(data = biplot_scores_sign,\n",
    "             aes(CAP1, CAP2, label = rownames(biplot_scores_sign)),\n",
    "             color = \"black\") +\n",
    "  geom_segment(data = biplot_scores_sign,\n",
    "               aes(x = 0, y = 0, xend = CAP1, yend = CAP2),\n",
    "               arrow = arrow(length = unit(0.2, \"cm\"), type = \"closed\"),\n",
    "               color = \"lightseagreen\", alpha = 1, size = 0.7) +\n",
    "  xlab(\"CAP1\") + ylab(\"CAP2\") +\n",
    "  ggtitle(expression(paste(\"Significant thermal variables and \", beta[sim]))) +\n",
    "  theme_grey() +\n",
    "  theme(panel.grid.minor = element_blank(),\n",
    "        legend.position = \"none\",\n",
    "        aspect.ratio = 0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e43964-08ce-465f-b276-b5157cfe2988",
   "metadata": {},
   "source": [
    "Note that in Smit et al. (2017, Fig. 2a) I plot the linear constraints (lc scores) rather than the site scores (wa scores). The fact that the positioning of the site scores in ordination space in the figure, above, represents a crude map of South Africa corresponding with geographical coordinates (N-E-S-W) is coincidental (yet it can be logically explained). The coenoclines and gradients are clearly discernible, and the west to east numbering of sites and transitioning of one bioregon into the next are obvious. This map-like arrangement of sites disappears when lc scores are used, but the interpretation of how the thermal drivers structure seaweed biodiversity remains the same."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0f45fe-1d69-44a0-87e8-83ae42ccc843",
   "metadata": {},
   "source": [
    "## Dealing with factor variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e490c28-1426-4cc6-a8c4-30c0a1a088c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>febRange</th><th scope=col>febSD</th><th scope=col>augMean</th><th scope=col>augRange</th><th scope=col>augSD</th><th scope=col>annRange</th><th scope=col>annSD</th><th scope=col>bioreg</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>-0.04433865</td><td>-0.2713395</td><td>-1.376511</td><td>-0.47349787</td><td>-0.409770805</td><td>-1.0460150</td><td>-1.636461</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>-0.14318268</td><td>-0.1083868</td><td>-1.433925</td><td>-0.06998551</td><td>-0.096490845</td><td>-0.7657214</td><td>-1.307622</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>-0.39321619</td><td>-0.1719978</td><td>-1.526950</td><td> 0.02484832</td><td> 0.009053095</td><td>-0.6311568</td><td>-1.143445</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>-0.60199306</td><td>-0.3120605</td><td>-1.579735</td><td>-0.05076148</td><td> 0.005487982</td><td>-0.5861267</td><td>-1.074994</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>-0.64081940</td><td>-0.4095900</td><td>-1.546420</td><td>-0.09833845</td><td> 0.041336545</td><td>-0.8799034</td><td>-1.200028</td><td>BMP</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>-0.55083241</td><td>-0.4294142</td><td>-1.458642</td><td>-0.11132528</td><td> 0.095589389</td><td>-1.2225182</td><td>-1.343088</td><td>BMP</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 8\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & febRange & febSD & augMean & augRange & augSD & annRange & annSD & bioreg\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & -0.04433865 & -0.2713395 & -1.376511 & -0.47349787 & -0.409770805 & -1.0460150 & -1.636461 & BMP\\\\\n",
       "\t2 & -0.14318268 & -0.1083868 & -1.433925 & -0.06998551 & -0.096490845 & -0.7657214 & -1.307622 & BMP\\\\\n",
       "\t3 & -0.39321619 & -0.1719978 & -1.526950 &  0.02484832 &  0.009053095 & -0.6311568 & -1.143445 & BMP\\\\\n",
       "\t4 & -0.60199306 & -0.3120605 & -1.579735 & -0.05076148 &  0.005487982 & -0.5861267 & -1.074994 & BMP\\\\\n",
       "\t5 & -0.64081940 & -0.4095900 & -1.546420 & -0.09833845 &  0.041336545 & -0.8799034 & -1.200028 & BMP\\\\\n",
       "\t6 & -0.55083241 & -0.4294142 & -1.458642 & -0.11132528 &  0.095589389 & -1.2225182 & -1.343088 & BMP\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 8\n",
       "\n",
       "| <!--/--> | febRange &lt;dbl&gt; | febSD &lt;dbl&gt; | augMean &lt;dbl&gt; | augRange &lt;dbl&gt; | augSD &lt;dbl&gt; | annRange &lt;dbl&gt; | annSD &lt;dbl&gt; | bioreg &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | -0.04433865 | -0.2713395 | -1.376511 | -0.47349787 | -0.409770805 | -1.0460150 | -1.636461 | BMP |\n",
       "| 2 | -0.14318268 | -0.1083868 | -1.433925 | -0.06998551 | -0.096490845 | -0.7657214 | -1.307622 | BMP |\n",
       "| 3 | -0.39321619 | -0.1719978 | -1.526950 |  0.02484832 |  0.009053095 | -0.6311568 | -1.143445 | BMP |\n",
       "| 4 | -0.60199306 | -0.3120605 | -1.579735 | -0.05076148 |  0.005487982 | -0.5861267 | -1.074994 | BMP |\n",
       "| 5 | -0.64081940 | -0.4095900 | -1.546420 | -0.09833845 |  0.041336545 | -0.8799034 | -1.200028 | BMP |\n",
       "| 6 | -0.55083241 | -0.4294142 | -1.458642 | -0.11132528 |  0.095589389 | -1.2225182 | -1.343088 | BMP |\n",
       "\n"
      ],
      "text/plain": [
       "  febRange    febSD      augMean   augRange    augSD        annRange  \n",
       "1 -0.04433865 -0.2713395 -1.376511 -0.47349787 -0.409770805 -1.0460150\n",
       "2 -0.14318268 -0.1083868 -1.433925 -0.06998551 -0.096490845 -0.7657214\n",
       "3 -0.39321619 -0.1719978 -1.526950  0.02484832  0.009053095 -0.6311568\n",
       "4 -0.60199306 -0.3120605 -1.579735 -0.05076148  0.005487982 -0.5861267\n",
       "5 -0.64081940 -0.4095900 -1.546420 -0.09833845  0.041336545 -0.8799034\n",
       "6 -0.55083241 -0.4294142 -1.458642 -0.11132528  0.095589389 -1.2225182\n",
       "  annSD     bioreg\n",
       "1 -1.636461 BMP   \n",
       "2 -1.307622 BMP   \n",
       "3 -1.143445 BMP   \n",
       "4 -1.074994 BMP   \n",
       "5 -1.200028 BMP   \n",
       "6 -1.343088 BMP   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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U0afHqlzfyw8yd/H150Y9fmQ7bH2bhU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\ntzfwkDp6iXZ6qzoHwNvPFCr/p1CoA00MoOz4gy4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EkjpOLDtOeJKoItgzhWo2GBozZBxApdgndqJeb8sAACAASURBVP89IxqKlsIgySnF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\nvXERqWYOtVoN23twUnMDTZdFVGIUr2AHGNab5Hup8V+//LLM+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R7wDbgDICYk+UUV0bt3HNNv07Od+YMHGWqKwro9OR5mgCduZLc849PY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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "E4 <- E3\n",
    "# append the bioregs after the thermal vars\n",
    "E4$bioreg <- bioreg$bolton\n",
    "head(E4)\n",
    "rda_cat <- capscale(Y1 ~., E4)\n",
    "plot(rda_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2116472-5b45-408b-a793-108dcd9d6cfc",
   "metadata": {},
   "source": [
    "The default plot works okay and shows all necessary info, but the various pieces (site, species, and centroid scores) are not clearly discernable. Plot the class (factor) centroids in `ggplot()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f0a311d-735d-422a-95bd-957f83bb4c74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79z7xEp/OOl29+roEl6SoozslXEZS0LAAB4gzWh2JpQfJaNVY8c/C6kOFMfNzDT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\nb4O/F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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# also extractthe factor centroids for the bioregions\n",
    "rda_cat_scrs <- scores(rda_cat, display = c(\"sp\", \"wa\", \"lc\", \"bp\", \"cn\"))\n",
    "site_scores <- data.frame(rda_cat_scrs$site) # the wa scores\n",
    "site_scores$bioreg <- bioreg$bolton\n",
    "site_scores$section <- seq(1:58)\n",
    "\n",
    "biplot_scores <- data.frame(rda_cat_scrs$biplot)\n",
    "biplot_scores$labels <- rownames(biplot_scores)\n",
    "biplot_scores_sign <- biplot_scores[biplot_scores$labels %in% rda_final_sign_ax,]\n",
    "\n",
    "bioreg_centroids <- data.frame(rda_cat_scrs$centroids)\n",
    "bioreg_centroids$labels <- rownames(bioreg_centroids)\n",
    "  \n",
    "ggplot(data = site_scores, aes(CAP1, CAP2, colour = bioreg)) +\n",
    "  geom_point(size = 5.0, shape = 24, fill = \"white\") +\n",
    "  geom_text(aes(label = section), size = 3.0, col = \"black\") +\n",
    "  geom_label(data = biplot_scores_sign,\n",
    "             aes(CAP1, CAP2, label = rownames(biplot_scores_sign)),\n",
    "             color = \"black\") +\n",
    "  geom_segment(data = biplot_scores_sign,\n",
    "               aes(x = 0, y = 0, xend = CAP1, yend = CAP2),\n",
    "               arrow = arrow(length = unit(0.2, \"cm\"), type = \"closed\"),\n",
    "               color = \"lightseagreen\", alpha = 1, size = 0.7) +\n",
    "  geom_label(data = bioreg_centroids,\n",
    "             aes(x = CAP1, y = CAP2,\n",
    "                 label = labels), size = 4.0,\n",
    "             col = \"black\", fill = \"yellow\") +\n",
    "  xlab(\"CAP1\") + ylab(\"CAP2\") +\n",
    "  ggtitle(expression(paste(\"Significant thermal variables and \", beta[sim]))) +\n",
    "  theme_grey() +\n",
    "  theme(panel.grid.minor = element_blank(),\n",
    "        legend.position = \"none\",\n",
    "        aspect.ratio = 0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf9c80cd-fd84-409e-b991-9f0ceaa4f7bb",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Baselga, A., Orme, D., Villeger, S., De Bortoli, J., and Leprieur, F. (2017). betapart: Partitioning Beta Diversity into Turnover and Nestedness Components. R package version 1.4-1. Available online at: https://CRAN.R-project.org/package=betapart\n",
    "\n",
    "Bolton, J. J. (1986). Marine phytogeography of the Benguela upwelling region on the west coast of southern Africa: A temperature dependent approach. Botanica Marina 29, 251–256.\n",
    "\n",
    "Bolton, J. J., and Anderson, R. J. (2004). “Marine vegetation,” in Vegetation of Southern Africa, eds R. M. Cowling, D. M. Richardson, and S. M. Pierce, (Cambridge, UK: Cambridge University Press), 348–370.\n",
    "\n",
    "Bolton, J. J., and Stegenga, H. (2002). Seaweed species diversity in South Africa. South African Journal of Marine Science 24, 9–18.\n",
    "\n",
    "De Clerck, O., Bolton, J. J., Anderson, R. J., and Coppejans, E. (2005). Guide to the seaweeds of KwaZulu- Natal. Scripta Botanica Belgica 33, 294 pp.\n",
    "\n",
    "Sauer, J. D. (1988). Plant migration: The dynamics of geographic patterning in seed plant species. University of California Press.\n",
    "\n",
    "Smit, A. J., Bolton, J. J., and Anderson, R. J. (2017). Seaweeds in two oceans: beta-diversity. Frontiers in Marine Science, 4, 404.\n",
    "\n",
    "Stegenga, H., Bolton, J. J., and Anderson, R. J. (1997). Seaweeds of the South African west coast. Contributions of the Bolus Herbarium 18, 3–637."
   ]
  }
 ],
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