carabids_00_project.md

Spring 2020 CU Boulder EBIO Ecological Forecasting Seminar Project: NEON-Niwot forest species SDMs

This is a centralized location to organize our thoughts and resources for this project. Thoughts and data are focused on Niwot Ridge site, unless specified otherwise.

Scientific questions we could ask

1. How will carabid species distributions shift with global warming in a subalpine forest?
2. Is there a shift in community composition through time at all in the NEON data available? For multiple sites, account for habitat and environmental differences. see Brooks et al. 2012 hyp1
3. Across NEON sites, do temporal community trends differ in magnitude and direction between habitats and regions? see Brooks et al. 2012 hyp2
4. Are community trends unique to combinations of habitats and regions? That is, might we expect species's responses to environmental change to be dictated mostly by habitat? see Brooks et al. 2012 hyp3
5. What are shifts in phenology of species through time? see Hoekman et al. 2017
6. What is carabid community response to habitat and land-use change over time?
7. What are shifts in phenology of species through time and across habitat? see Hoekman et al. 2017
8. Is fine-scale change in microhabitat heterogeneity more important to carabid abundance and richness than larger-scale changes in landscape diversity? Could more stable habitats (e.g. forest) buffer wider-scale perturbations? see Brooks et al. 2012
9. "How much data is enough to forecast accurately?": Develop a prediction model and run it (say) 35 years into the future. Assume this forecast data is real. Re-run the model, giving it varying amounts of "real" data (1 yr, 3 yrs, 5 yrs, 10 yrs etc). For each amount of "real" data, assess forecast accuracy at various timesteps; i.e. how far into the future can we accurately forecast with a given amount of "real" data? Figure out how to predict standard error change through time by simulating fake data.
10. Predict beetle abundance and and composition at a different spot in the same year. Or forecast rel abundance for the next collection period
11. Brett feedback from class pres - we're describing a bootstrapped forecast (generating fake data - if this is the truth, what uncertainty is there?) Think about the generative process in data collection

Why carabids?

• identifying habitat associations in a dominant insect group would be an important step toward understanding the consequences of global climate change in high mountain areas (Hiramatsu and Usio 2018)
• Because of their known sensitivity to changes in habitat structure and requisitly altered microhabitat, carabid beetles have been suggested to serve as useful bioindicators of environmental and land use change (Hiramatsu and Usio 2018)
• To date, numerous studies have shown that carabid beetle richness and/or assemblage composition change along altitudinal gradients [5–7] and/or with vegetation type [8–11] (Hiramatsu and Usio 2018)
• Remote sensing can measure carabid habitat at the local and landscape scales. Identifying the predictive relationships between beetle assemblages to remote sensing variables allows conservation managers to measure carabids at a scale appropriate for their biology and scale up to that which management decisions function (Muller et al. 2009).

Data products we could use

• Response variable: carabid community abundance and composition
  • Source: NEON carabid data at NIWO 2015-19

Predictor variable	source	dpID	spatial distribution	temporal distribution	literature
carabid richness & abundance	NEON	DP1.10022.001	4 traps per plot (10) at each terrestrial site, arrayed 20m from the center of the plot in each cardinal direction	2015-2019, biweekly sampling during growing season	NEON carabid data user guide Hoekman et al. 2017
carabid DNA barcode	NEON	DP1.10020.001
slope/aspect	NEON	DP3.30025.001	same as elevation	same as elevation	data page
elevation	NEON	DP3.30024.001	full extent of site, well beyond pitfalls, 1mx1m resolution	2017-2019 (Sept, Aug, Aug)	data page
precipitation	NEON	DP1.00006.001	looks like a couple of sensors per NEON site - CHECK	(almost) every month 2017-current	neon data page
relative humidity	NEON	DP1.00098.001	sensors on flux tower and in soil array - CHECK	1min and 30min time series, 2017-current	data page
IR biological temp	NEON	DP1.00005.001	a couple sensors near tower	1min and 30min series, every month 2017-current	data page
Shortwave radiation (direct and diffuse pyranometer)	NEON	DP1.00014.001
Shortwave and longwave radiation (net radiometer)	NEON	DP1.00023.001
LAI - spectrometer - flightline	NEON	DP2.30012.001
soil temp	NEON	DP1.00041.001	a couple sensors near tower	1min, 30min series, mid2017-current	data page
Soil water content and water salinity	NEON	DP1.00094.001	many base plots, including pitfall locations
Woody plant vegetation structure	NEON	DP1.10098.001	38 base plots, including pitfall locations (24 base plots in 2019)	annual samples, 2015-16, 2019. Not all plots were sampled each year.	TOS Science Design for Plant Biomass and Productivity
Litterfall and fine woody debris sampling	NEON	DP1.10033.001	12 base plots, including pitfall locations	monthly samples (some gaps by location), June or July through November, 2016-2019
Plant presence and percent cover	NEON	DP1.10058.001			data page
Permanent forest plot data	NiwotLTER	NA		1982-2016	Chai et al. 2019
canopy gaps
hydrology
% canopy cover
litterfall/woody debris
surface temp
microtopography
time to snowmelt
max. snowpack

Miscellaneous data	source	spatial distribution	temporal distribution
carabids	carabids.org	Europe, Africa, Asia

Carabid traits

These are the 7 most abundant and accurately identified species at Niwot Ridge.

Species	Traits	Reference
Amara alpina (Paykull 1790)	Diet Omnivores. Diet consists (likely in equal parts in terms of biomass) of animal prey and plant biomass, including bryophyte mosses and seeds Phenology Peak activity beginning of July in Norwegian mountains. Likely follows a univoltine life-cycle and can take up to 2 years to complete its life-cycle. Prolonged life-cycle is an adaptation to life in the alpine-tundra environment with generally shorter seasons. Emerges right after snow melt, with males appearing earlier than females. Reproduction At peak activity, they copulate, lay their eggs, and many of the adults perish shortly after. Larvae probably hatch in the late summer/early fall. Dispersal Low rates of dispersal capability. Flying is sporadic, despite being winged (can be either macropterous or brachypterous). Running on the ground is common. Crucially, undergoes phases of immobility during development (eggs, larval pupation). Low mobility, at least during development, makes it more susceptible to elevational effects. Distances covered are likely to be less than 20 m per 24 h Habitat Low-middle alpine. Common inhabitant of open, alpine-tundra environment. Does not seem to favor drier soils, as other species of the genus seem to. Occurrence is stenotopic at ridges & widespread on slopes Overwintering Adults have been shown to hibernate during winter. However, larval overwintering (in the last instar) and consequent (quick) maturation in the following spring is also possible. The life-cycle may thus be very flexible. Hibernation of both developmental stages means that these species are well-equipped to act as colonizers and pioneers in the alpine. Vegetation lichen heath/ lichen-rich dwarf-shrub veg also: sparse veg., snow-bed & grass dominated veg.	Naujok & Finch 2004 Beckers et al. 2020
Amara quenseli	Anna
Calathus advena	Anna
Carabus taedatus	Anna
Cymindis unicolor	Tribe: Lebiini Habitat: An arctic-alpine species occurring in tundra and tundra-forest transition zones; inhabits treeless, well-drained environments	Garry 1990 , Nelson 1988
Harpalus nigritarsis	Tribe Harpalini Diet (for genus): seed-eaters as adults and typically also as larvae. Phenology annual life cycle	Kent Wildlife Trust
Pterostichus restrictus	Tribe Pterostichini

Data thoughts & questions

• How to choose which species to model? Dittrich et al. 2020 chose species to reduce interannual variability by selecting ones that are (1) not directly depending on ephemeral resources and (2) nonspecific predators, feeding on various soil arthropods.
• Model occupancy or abundance? Maybe occupancy since we don't have abundant records
• Canopy structural variability seems like an important predictor (Davies & Asner 2014) - how to quantify this?
• Incorporate structural variables at trap-, plot-, and site-scales. Muller et al 2014 found higher arthropod diversity with increased canopy structural variability and density at the tree scale, but lower diversity at the stand scale
• Proxy for understory veg could be elevation or dist to stream
• NEON veg diversity data (2m scale cover)
• Climatic/meteorological variables: When Henry Nix started creating SDMs, bioclim was important> WorldClim should be at a fine enough scale for across-site NEON comparisons. Kiarney 2018 & 2019 (Methods in Ecology) used worldclim or bioclim to get 30m scale meteorological data for small-scale microclimate modeling

Literature

• Plant diversity sampling design at NEON, Barnett et al. 2019
• NEON design for ground beetle sampling Hoekman et al. 2017
• Carabids in a Japanese Alpine-Subalpine Zone, Hiramatsu and Usio 2018
• Forest beetle assemblages and LiDAR, Muller & Brandl 2009
  • LiDAR-derived variables: canopy height SD and max tree height at traps from DSM, mean altitude at trap from DTM, microclimaatic conditions proexy from laser penetration rate - see Table 2
  • decrease in body size with laser penetration ratio and larger species in closed forests
  • increase in activity of xylophagous species with an increase of the laser penetration ratio.
  • decrease in the activity of mycetophagous and phytophagous species with altitude because of a reduced availability of hosts.
• The UK's ECN, kinda like the US's NEON Brooks et al. 2012
  • largest population declines in montane habitat
• Forty years of carabid beetle research in Europe, Kotze et al. 2011
• Use satellite imagery to build mountainous rove beetle SDM. Machine learning models performed better than GLM and GAM, Dittrich et al. 2020
• studies that have used satellite-derived parameters for SDMs in mountainous regions
  • Dudov et al. 2017
  • Oke & Thompson 2015
• Review of advances in animal ecology from LiDAR - helpful table for invertebrates, definitions of structural terms Davies & Asner 2014
  • canopy variability - Beetle body size decreased with increasing variability, Muller & Brandl 2009; Arthropod diversity increased at the tree scale, Muller et al. 2013
  • canopy density - Arthropod diversity increased with increasing canopy density at tree scale, but decreased at stand scale, Muller et al. 2013
  • canopy height - Beetle body size increased, abundance decreased with increasing height, Muller & Brandl 2009
  • elevation - Beetle species richness and spider diversity increased, Muller & Brandl 2009 with higher elevation
  • hydrology - Female carabid beetles preferred steep slopes with high flow accumulation, Work et al. 2011
  • aspect - Female carabid beetles preferred cooler (north-facing) slopes, Work et al. 2011
  • From the eight studies available for review, variability in the canopy profile (both vertical and horizontal), or ‘structural variability’ (Box 3), rather than a single structural metric, was most important for invertebrate assemblages
  • make species distribution models phylogenetically-informed? Smith et al. 2018

Resources

• taxize package for taxon nomenclature
• Could use McCain Lab carabids from Niwot Ridge: collected along elevational gradient, have veg data associated