Aldo Compagnoni

### Introduction

The availability of large quantities of freely available data is
revolutionizing the world of ecological research. Open data maximizes
the opportunities to perform comparative analyses and meta-analyses.
Such synthesis efforts will increasingly exploit "population data",
which we define here as time series of population abundance. Such
population data plays a central role in testing ecological theory and
guiding management decisions. One of the richest sources of open access
population data is the [USA Long Term Ecological Research (LTER)
Network](https://lternet.edu/). However, LTER data presents the drawback
common to all ecological time-series: extreme heterogeneity derived from
differences in sampling designs.We experienced this heterogeneity first
hand, upon embarking on our own comparative analysis of population data.
Specifically, we noticed that heterogeneities in sampling design made
datasets hard to compare, and therefore hard to search and analyze.

To alleviate these issues, we created popler (POPulation time series
from Long-term Ecological Research): an online PostgreSQL database
(henceforth "popler online database"), and associated R client
(henceforth "popler R package"). popler accommodates raw population
time-series data using the same structure for all datasets. Using raw
data, without aggregation, promotes flexibility in data analysis.
Moreover, using a common data structure implies that all datasets share
the same variables, or the same types of variables. As a result, popler
facilitates comparing, manipulating, and retrieving population data.

#### The popler R package

We strove to make user-friendliness the hallmark of the popler R
package. To facilitate use, we created as few functions as possible:
popler relies on essentially two functions: `pplr_browse()`, which you
can use to see what types of data are available in popler, and
`pplr_get_data()`, to download datasets from the popler online
database.`pplr_browse()` allows selection of which one of the 305
datasets satisfies user-specified criteria. These criteria will depend
on the metadata variables used to describe the dataset stored in popler.
These metadata variables, and their content, is described by function
`pplr_dictionary()`.Once a user understands the meaning and content of
metadata variables, she/he can use `pplr_browse()` to identify datasets
of interest. For example, one could be interested in experimental
datasets that quantify population abundance using cover measurements,
and that are at least 20 years long. To see which of these data are
available, the user should run:

::: {.highlight}
``` {.chroma tabindex="0"}
library(popler)data_search <- pplr_browse( studytype == 'exp' & datatype == 'cover' & duration_years > 20 )data_search
```
:::

The resulting tibble object shows that there are only four datasets with
these characteristics:

    ## # A tibble: 4 x 20## title proj_metadata_k~ lterid datatype structured_data studytype duration_years community## * <chr>            <int> <chr>  <chr>    <chr>           <chr>              <int> <chr>    ## 1 SGS-~               71 SGS    cover    no              exp                   42 yes      ## 2 Post~              140 AND    cover    no              exp                   22 yes      ## 3 Vege~              196 BNZ    cover    no              exp                   27 yes      ## 4 e133~              235 CDR    cover    no              exp                   26 yes   ## # ... with 12 more variables: studystartyr <chr>, studyendyr <chr>, structured_type_1 <chr>,## #   structured_type_2 <chr>, structured_type_3 <chr>, structured_type_4 <chr>, treatment_type_1 <chr>,## #   treatment_type_2 <chr>, treatment_type_3 <chr>, lat_lter <dbl>, lng_lter <dbl>, taxas <list>

If interested we can download these directly:

::: {.highlight}
``` {.chroma tabindex="0"}
df_obj <- pplr_get_data( datatype == 'cover' & studytype == 'exp' & duration_years > 20 )
```
:::

#### Managing the popler database and its API

One of the biggest hurdles in creating the popler R package was
integrating it with an Application Programming Interface (API) that
allows secure access to the popler online database. In principle, the
popler R package could access the popler online database directly from R
using functionalities of R packages such as RPostgreSQL. However, this
poses substantial security threats, and we therefore welcomed the
suggestion by [Noam Ross](/authors/noam-ross/), from rOpenSci, to use an
API. Subsequently, [Scott Chamberlain](/authors/scott-chamberlain/),
also from rOpenSci, was kind enough to develop the API using the Ruby
language, the code for which is [currently stored on
GitHub](https://github.com/AldoCompagnoni/popler_API).

Querying an SQL database using an API improves security, but poses a few
limitations on the features of the popler R package, and imposes the
additional challenge of managing the API. The API limits the ways in
which the R package can query the popler online database. In particular,
our API allows querying popler in two ways: downloading the metadata of
all 305 datasets, and downloading entire datasets. Fortunately, these
two queries reflect the vast majority of operations that users perform
when using the popler R package. In particular, the metadata of all 305
datasets is essentially what is used by the `pplr_browse()` function,
and users almost always tend to use `pplr_get_data()` to download entire
datasets. Hence, the limitations posed by the API were of little
importance when weighed against the benefit provided by increased
security. As a side note, using an API provides a minor, yet somewhat
enticing perk: a progress bar for the downloads of `pplr_get_data()`.

The API also needs to be managed in order to avoid crashes which make
downloads impossible. The API crashes whenever the online database is
stopped and restarted, and whenever the API web service crashes. The
former case is predictable, but the API web service can crash at any
time. To monitor whether the web service of our API is running
correctly, we use [Uptime Robot](https://uptimerobot.com/), a free
application that quantifies the time during which the web server is
available to users.

### Conclusion: working with LTER population data

Our work with popler highlighted many opportunities linked to data
synthesis using population data from the USA LTER network. Specifically,
we found that population time-series data, while containing sometimes
daunting heterogeneities, also present conspicuous regularities. By
"regularities", we mean that population abundance can be quantified in
just a few ways (e.g. using densities, counts, or area covered), that
all datasets have a temporal replication of population censuses, a
spatial replication structure (which is often times nested), and so on.
Ecologists can use these regularities to format most population
time-series using the same data model. During our work on popler, we
realized that the population data associated with the USA LTER data, and
with other long-term population sampling efforts, provide untapped
potential to foster synthesis work in population, community, and macro
ecology.

#### Acknowledgements

We thank reviewers [Corinna Gries](https://github.com/cgries), and
[Benjamin Bond-Lamberty](https://github.com/bpbond) for [helpful
comments](https://github.com/ropensci/software-review/issues/254) that
improved the package functionality.


Introduction  The availability of large quantities of freely available data is revolutionizing the world of ecological research. Open data maximizes the opportunities to perform comparative analyses and meta-analyses. Such synthesis efforts will increasingly exploit “population data”, which we define here as time series of population abundance.

Publicaciones de Rogue Scholar

Synthesizing population time-series data from the USA Long Term Ecological Research Network