THE USE OF BENTHIC MACROINVERTEBRATE TRAITS TO ASSESS CLIMATE CHANGE RESPONSES AND VULNERABILITIES Anna Hamilton (Tetra Tech), Britta Bierwagen (US EPA), Jen Stamp (Tetra Tech), Jonathan Witt (Fairfax County Stormwater Planning Division), Lei Zheng (Tetra Tech) Monday May 23, 2:00 pm Society for Freshwater Sciences Meeting Sacramento, CA

Traits & Responses to Climate Change ■Traits-based approaches can inform the mechanistic link between pattern and process ■Can be compared across geographic differences in taxonomic composition ■Climate change influences temperature & flow regimes, and….. ■Our initial traits development focused on temperature & flow

Basis for Temperature & Flow Trait Development Northeastern US - 7 states

Basis for Temperature & Flow Trait Development Biological data 10,913 macroinvertebrate samples from 6,342 stream stations 1,416 samples from 953 stream stations are from reference stations Only reference sites GIS/other data NHDPlus catchments Temperature Flow – gage data Precipitation Moisture surplus Land use Species Distribution Models GAM/GLM used to model response in taxa capture probabilities along multiple climate gradients A stepwise variable selection process adapted to select models (initial model: GAM with 3 degrees of freedom)

Temperature preferences & tolerances Estimated ‘tolerance’ values – 95 th percentile (or 5 th ) of distribution, as well as optima 95 th percentile

Hydrologic preferences & tolerances Example taxa with lowest 3-day minimum flow tolerances

Responses to different parameters Relationships with temperature, precipitation and moisture surplus may differ – consider parameters in combination if possible

Climate change – not just temperature…. ■Use indicators to understand – ‘project’ – how stream communities are likely to change in the future with climate change ■More than looking at temperature (or flow) preference traits in isolation ■At a regional scale ■How will this influence biomonitoring

Community Indicators NRSA/WSA data Community classification - site groups/stream types across east coast Determine environmental variables (including climate variables) that are the best predictors of community composition Compare predicted community groups under baseline and projected future climate Described how membership of sites in each stream group will shift in the future due to climate change

Three Major Stream Groups (Site Classes) G1: Cold water &erosional preference taxa streams G2: Low diversity streams, intermediate in percentage of taxa in each group G3: Warm water &depositional preference taxa streams

The cold & erosional metrics follow similar patterns vs. summer temperature (same vs. summer velocity) Thermal & Rheophilic Taxa Responses Similar % Cold Pref G1 G2 G3 Erosional Pref Richness G1 G2 G3

The warm & deposition metrics follow similar patterns vs. summer temperature (same vs. summer velocity) Thermal & Rheophilic Taxa Responses Similar % Warm Pref G1 G2 G3 Depositional Pref Richness G1 G2 G3

Future community shifts due to combined temperature/flow changes Dominant class - baseline ( ) Dominant class - future ( ) G1 probability of membership future ( ) - baseline ( ) Negative value = probability of G1 membership is reduced by mid-century Positive value = probability of G1 membership increases by mid-century

Limitations? ■Single factor traits –Instantaneous temperatures – continuous better –Flow data associated with biological data highly limited –Bigger region  better results (cover fuller range of temp or flow ■Distributional differences related to other factors (e.g., stream size) that co-vary with temperature and/or flow ■Species differences within some genera – limits to transferring generic information

Where are we going from here? Regional Montioring Networks (RMNs) – one piece of the puzzle

Regional Monitoring Networks (RMNs) – improve data, refine climate change indicators Working with EPA Regional offices, states, tribes and other biomonitoring programs in the Northeast, Mid-Atlantic, Southeast & Midwest Goal – collect: o Annual biological data o Continuous thermal and hydrologic data

RMNs – Contributions to Traits Approach ■Support better assessment of trait groups & responses –Consistent monitoring across larger regions – wider ranges of temperature, flow, and other environmental gradients –Continuous temperature data –More flow data –Regional experts – review trait assignments, variable traits ■Spatial coverage to test projections of trait group (cold-preference, erosional) range contractions ■RMNs in increasing number of regions – classification and trait-group distribution results ■Contribute to other sources of information, e.g., mesocosm experiments, etc.

Traits Related to (Unpredictable) Extreme Events Magnitude of the flooding on the White River from Hurricane Irene. Black line - 90 th percentile of mean daily discharge based on the full period of record for this site (1915-current). Slower, mean changes in temp & flow  adaptation to predictable conditions Extreme conditions  survival Hurricane Irene (2011), Vermont

Thank you! Questions? Anna Hamilton Britta Bierwagen Jen Stamp Jonathan Witt Lei Zheng