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_posts/2021-03-25-point-count-data-analysis/index.Rmd

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 knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
 ```
 
-![](https://github.com/psolymos/qpad-workshop/raw/main/images/thumb.jpg)
+```{r echo=FALSE}
+knitr::include_graphics("thumb.jpg")
+```
+
 
 This course is aimed towards researchers analyzing field observations, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another, or through time over the lifespan of projects, or trying to combine 'legacy' data sets with new data collected by recording units.
 

_posts/2021-03-25-point-count-data-analysis/index.html

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@@ -1498,7 +1498,9 @@ <h3>Contents</h3>
 </ul>
 </nav>
 </div>
-<p><img src="https://github.com/psolymos/qpad-workshop/raw/main/images/thumb.jpg" /></p>
+<div class="layout-chunk" data-layout="l-body">
+<p><img src="thumb.jpg" width="640" /></p>
+</div>
 <p>This course is aimed towards researchers analyzing field observations, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another, or through time over the lifespan of projects, or trying to combine ‘legacy’ data sets with new data collected by recording units.</p>
 <p>Such heterogeneities can bias analyses when data sets are integrated inadequately, or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities.</p>
 <p>Analysts of such ‘messy’ data sets need to feel comfortable with manipulating the data, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations), and should be able to make informed choices when faced with methodological challenges.</p>

_posts/2021-03-25-point-count-data-analysis/index_cache/html5/__packages

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+base
+methods
+datasets
+utils
+grDevices
+graphics
+stats

docs/index.html

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 </div>
 </div>
 <div class="thumbnail">
-<img/>
+<img src="posts/2021-03-25-point-count-data-analysis/thumb.jpg"/>
 </div>
 <div class="description">
 <h2>Point-count Data Analysis</h2>

docs/index.xml

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       <guid>https://bios2.github.io/bios2_trainings/posts/2021-03-25-point-count-data-analysis</guid>
       <pubDate>Thu, 25 Mar 2021 00:00:00 +0000</pubDate>
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     <item>
       <title>Spatial Statistics in Ecology</title>

docs/posts/2021-03-25-point-count-data-analysis/index.html

@@ -103,14 +103,16 @@
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@@ -2129,7 +2131,9 @@ <h3>Contents</h3>
 </ul>
 </nav>
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-<p><img src="https://github.com/psolymos/qpad-workshop/raw/main/images/thumb.jpg" /></p>
+<div class="layout-chunk" data-layout="l-body">
+<p><img src="thumb.jpg" width="640" /></p>
+</div>
 <p>This course is aimed towards researchers analyzing field observations, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another, or through time over the lifespan of projects, or trying to combine ‘legacy’ data sets with new data collected by recording units.</p>
 <p>Such heterogeneities can bias analyses when data sets are integrated inadequately, or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities.</p>
 <p>Analysts of such ‘messy’ data sets need to feel comfortable with manipulating the data, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations), and should be able to make informed choices when faced with methodological challenges.</p>

docs/sitemap.xml

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