Beetles.qmd

# Theme: Beetle Communities

**What:** Beetle abundance and species richness

**Where:** 47 terrestrial NEON sites that span the diverse ecosystems of the U.S.

**When:** Forecasts for 52 weeks into the future using a weekly time-step are accepted at any time. 

**Why:** Improve understanding of habitat quality, conservation potential, land-use sustainability, and biodiversity change in response to global change and ecological disturbances

```{r echo = FALSE, message = FALSE}
library("tidyverse")
```

## Overview

Biodiversity monitoring is critical for understanding environmental quality, evaluating the sustainability of land-use practices, and forecasting future impacts of global change on ecosystems. Sentinel species give forewarning of environmental risk to humans, so are particularly useful for such monitoring and forecasting efforts because they can provide surrogates for other co-located components of biodiversity (Sauberer et al. 2004).

Ground beetles (Family: Carabidae) are appropriate candidates for biodiversity monitoring and ecological forecasting as they are well-studied sentinel species that are geographically widespread, and their community dynamics are particularly congruent with the diversity of other invertebrates (Holland 2002; Lundgren & McCravy 2011; Bousquet 2012; Hoekman et al. 2017). Therefore, monitoring carabid communities and forecasting changes in their species richness and abundance can be useful in studying edge effects and habitat quality (Magura 2002), conservation potential (Butterfield 1995), land-use sustainability (Pearce & Venier 2006) and biodiversity change in response to global change and ecological disturbances (Koivula 2011). Most ecological forecasting models are limited in the geographic scale and also suffer from scarcity of temporally extensive data. Further, most existing forecasting efforts focus on a single species (Humphries et al. 2018) with limited community-wide forecasts at the continental scale. Developing forecasts for community-scale metrics (i.e., species richness, abundance) and evaluating such models for accuracy and generalizability can help test our scientific knowledge of spatial (geographical turnover) and temporal (seasonal, inter-annual) carabid community dynamics (Dietze et al. 2018). Such forecasting models can inform regional or local habitat management, identify where biodiversity monitoring efforts should be prioritized, and shed light on what data or modelling techniques are needed to build the best forecasts of ecological dynamics (e.g., can we predict richness or abundance better and why?) (Johansson et al. 2019).

With the long-term, community-wide, continental-scale data collection through the National Ecological Observatory Network (NEON), 181 data products are available for 81 sites in the US (47 terrestrial, at which carabids are sampled, and 34 aquatic). Fully initiated in 2019, this sampling will continue for 30 years (Schimel et al. 2007; 2011). NEON has effectively removed the previous barriers to community-scale forecasting across a broader geographical realm.

## Challenge

This is an open ecological forecasting challenge to forecast carabid species richness, defined as the total number of species, and abundance, defined as the total number of carabid individuals. The forecasts should be done weekly per site for all NEON terrestrial sites with richness being absolute and abundance scaled by the sampling effort. NEON releases carabid sampling data weekly and no sooner than 60 days after collection, so a model submitted on June 30 can include a forecast for the first week of May, and so forth. Teams may use any open data products as drivers of richness and abundance so long as they are not from the month being forecast, and are made publicly available (minimum of URL, but ideally a script). Potential driver data sources include: NEON site data ([Soil and sediment data](https://www.neonscience.org/data-collection/soils-sediments){target="_blank"}, [Terrestrial Plant data](https://www.neonscience.org/data-collection/terrestrial-plants){target="_blank"}, [weather data](https://www.neonscience.org/data-collection/meteorology){target="_blank"}), NOAA forecasts, and beyond.

## Data: Targets

The challenge uses the following NEON data product:\
[DP1.10022.001](https://data.neonscience.org/data-products/DP1.10022.001){target="_blank"}: Ground beetles sampled from pitfall traps

Forecasts will be made on a weekly basis for the abundance of beetles at a given NEON site at a given month ('abundance') and the observed species richness (n, number of species) of carabid beetles at each NEON site, each month.

### Abundance of beetles (abundance)

**Definition**

Total number of carabid individuals per trap-night, estimated each week of the year at each NEON site

**Motivation**

A forecast prediction can be compared against only measured data (i.e. counts) and not latent variables (i.e. true carabid abundance), which are only inferred under specific model assumptions. However, raw count of beetles found in a particular trap depends on many other drivers than local abundance; in particular, the sampling effort. To avoid the need to accurately predict the sampling effort, we compute a target variable as counts per trap-night (number of nights each trap was set at the site; also called 'catch per unit effort'). We chose this to define this variable in terms of total carabid abundance, rather than resolving to particular taxonomy (in contrast to species-specific relative abundance) because it simplifies issues related to taxonomic resolution of unpinned samples, data latency, and the choice of focal species. As supported by literature (Hoekman et al. 2017 and literature cited therein), we believe that abundance of the beetle family as a whole is an ecologically relevant metric. We considered predictions aggregated to the site level (rather than predicting individual traps or individual plots) to be both the most ecologically meaningful and simplest choice. Traps are typically collected every two weeks. Submitting a forecast for every week avoids the need to predict which weeks of the year collection does or does not occur. 

### Species richness (n)

**Definition**

Total number of unique 'species' in a sampling bout for each NEON site each week. For this challenge, we define 'species' as the taxonomic unit closest to species (e.g., species, genus, morphospecies) for each individual since not all identifications in the raw data are strictly at species-level.

**Motivation**

A forecast prediction can be compared against only measured data (i.e. observation count of taxonomic units) and not latent variables (i.e. count of species), which are only inferred under specific model and taxonomic assumptions. The number of unique taxonomic identities of beetles in a trap depends on many drivers, including sampling effort. As demonstrated by species rarefaction curves in ecology, the more time a trap is left out, the more individual beetles will fall in, and thus the more species can be expected. However, since perfect species-level data are not available to us and to keep the forecasted variables from being overly derived, we define the target variable as the total number of unique 'species' per week per NEON site. For this challenge, we chose to define 'species' as the taxonomic unit closest to species (e.g., species, genus, morphospecies) since not all identifications in the raw data are strictly at species-level. Species identifications will be used for individuals identified to sub-species level, as these are uncommon in the raw data. NEON taxonomists identify individuals as morphologically distinct units. Thus, it is reasonable to assert that the count of finest morphologically distinct identification (e.g., species, genus, morphospecies) is biologically meaningful, and thus the count of these is an important focal forecast variable. We focus on the NEON site as the spatial resolution and weekly intervals as the temporal resolution for the same reasons stated in the abundance metric. 

### Focal sites

All 47 NEON terrestrial sites are included

Information on the sites can be found here:

```{r message = FALSE}
site_data <- readr::read_csv("https://raw.githubusercontent.com/eco4cast/neon4cast-targets/main/NEON_Field_Site_Metadata_20220412.csv") |> 
  dplyr::filter(beetles == 1)
```

```{r echo = FALSE}
site_data %>% 
  select(field_site_id, field_site_name, field_dominant_nlcd_classes, field_latitude, field_longitude, neon_url) %>% 
  rename(siteID = field_site_id,
         `site name` = field_site_name,
         `vegetation type` = field_dominant_nlcd_classes,
         `latitude` = field_latitude,
         `longtitude` =  field_longitude,
         `NEON site URL` = neon_url) %>% 
  arrange(siteID) %>% 
  knitr::kable()
```

### Target data calculation

Ground beetle data are collected at each NEON site every two weeks throughout the sampling season. The sampling season is defined based on measures of growing season, including vegetation indices, phenology, and degree days, for a maximum of 13 bouts per site during which the 10-day average low temperature at the site is \>4°C.

Samples are collected from pitfall traps placed at each of the cardinal directions within the 10 plots per site representative of up to three dominant vegetation types. Four traps were placed from 2014-2017 and from 2018 onward the northward plot was eliminated leaving three traps for each plot. Ground beetles from the pitfall traps are removed, sorted, and identified to the lowest possible taxonomic rank or morphospecies. A subset of individuals (up to 467 per site and year) are sent to taxonomic experts for subsequent identification with priority on individuals for which species-level identification was not able to be assigned. Further detail can be found in the NEON Ground Beetle User Guide.

Raw (NEON L1 ground beetle data product [DP1.10022.001](https://data.neonscience.org/data-products/DP1.10022.001){target="_blank"}) are accessible via the NEON data portal, via the NEON API, via R using the neonUtilties package, and via R using neonStore::neon_download. The raw data is also available through the NEON data portal with archived copies at https://data.ecoforecast.org/minio/neonstore.

The raw data is processed to generate total abundance and richness per week per NEON site. All data in the supplied file is available to build and evaluate models before submitting a forecast to challenge. Once new data becomes available, the data are appended to the existing file. Within the challenge scoring, only the new data are used to evaluate previously submitted forecasts.

As part of our reproducible workflow, we provide an R script for producing derived tables of total abundance and richness from the raw NEON data. Our workflow gives preference to expert taxonomist identifications when available. Since expert taxonomy lags behind identifications from the sorting and pinning process, newer data will not be updated with expert taxonomy. The abundance table gives total abundance at each site for each week. The richness table gives an aggregate count of the number of 'species' at each site in each week.

## References

Bousquet, Y. (2012) Catalogue of Geadephaga (Coleoptera: Adephaga) of America, north of Mexico. ZooKeys 245: 1-1722. https://doi.org/10.3897/zookeys.245.3416

Butterfield, J., Luff, M., Baines, M., Eyre, M. (1995) Carabid beetle communities as indicators of conservation potential in upland forests. Forest Ecology and Management 79, 63-77. https://doi.org/10.1016/0378-1127(95)03620-2

Dietze, M.C., Fox, A., Beck-Johnson, L.M., Betancourt, J.L., Hooten, M.B., Jarnevich, C.S., Keitt, T.H., Kenney, M.A., Laney, C.M., Larsen, L.G. (2018) Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences 115, 1424-1432. https://doi.org/10.1073/pnas.1710231115

Gneiting, T., & Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102(477), 359--378. https://doi.org/10.1198/016214506000001437

Hoekman, D., LeVan, K.E., Gibson, C., Ball, G.E., Browne, R.A., Davidson, R.L., Eriwin, T.L., Knisley, C.B., LaBonte, J.R., Lundgren, J., Maddison, D.R., Moore, W., Niemela, J., Ober, K.A., Pearson, D.L. Spence, J.R., Will, K., Work, T. (2017) Design for ground beetle abundance and diversity sampling within the National Ecological Observatory Network. Ecosphere, 8(4), e01744. https://doi.org/10.1002/ecs2.1744

Holland, J.M. (2002) The agroecology of carabid beetles. Intercept Limited, Andover.

Humphries, G.R., Che-Castaldo, C., Bull, P., Lipstein, G., Ravia, A., Carrión, B., Bolton, T., Ganguly, A., Lynch, H.J. (2018) Predicting the future is hard and other lessons from a population time series data science competition. Ecological Informatics 48, 1-11. https://doi.org/10.1016/j.ecoinf.2018.07.004

Johansson, M.A., Apfeldorf, K.M., Dobson, S., Devita, J., Buczak, A.L., Baugher, B., Moniz, L.J., Bagley, T., Babin, S.M., Guven, E. (2019) An open challenge to advance probabilistic forecasting for dengue epidemics. Proceedings of the National Academy of Sciences 116, 24268-24274. https://doi.org/10.1073/pnas.1909865116

Koivula, M.J. (2011) Useful model organisms, indicators, or both? Ground beetles (Coleoptera, Carabidae) reflecting environmental conditions. ZooKeys, 287-317. https://doi.org/10.3897/zookeys.100.1533

Lundgren, J., McCravy, K. (2011) Carabid beetles (Coleoptera: Carabidae) of the Midwestern United States: A review and synthesis of recent research. Terrestrial arthropod reviews 4, 63-94. https://doi.org/10.1163/187498311X565606

Magura, T. (2002) Carabids and forest edge: spatial pattern and edge effect. Forest Ecology and Management 157, 23-37. https://doi.org/10.1016/S0378-1127(00)00654-X

Pearce, J.L., Venier, L.A. (2006) The use of ground beetles (Coleoptera: Carabidae) and spiders (Araneae) as bioindicators of sustainable forest management: A review. Ecological Indicators 6, 780-793. https://doi.org/10.1016/j.ecolind.2005.03.005

Sauberer, N., Zulka, K.P., Abensperg-Traun, M., Berg, H.-M., Bieringer, G., Milasowszky, N., Moser, D., Plutzar, C., Pollheimer, M., Storch, C. (2004) Surrogate taxa for biodiversity in agricultural landscapes of eastern Austria. Biological Conservation 117, 181-190. https://doi.org/10.1016/S0006-3207(03)00291-X

Schimel, D., Hargrove, W., Hoffman, F., MacMahon, J. (2007) NEON: a hierarchically designed national ecological network. Frontiers in Ecology and the Environment 5, 59-59. https://doi.org/10.1890/1540-9295(2007)5\[59:NAHDNE\]2.0.CO;2

Schimel, D., Keller, M., Berukoff, S., Hufft, R., Loescher, H., Powell, H., Kampe, T., Moore, D., Gram, W. (2011) NEON Science Strategy: Enabling Continental-Scale Ecological Forecasting. https://www.neonscience.org/sites/default/files/basic-page-files/NEON_Strategy_2011u2_0.pdf