Assessment Protocol for Everglades Restoration. Amanda I. Banet Joel C. Trexler

Transcription

1 Using CERP MAP Data to Develop an Assessment Protocol for Everglades Restoration ti Amanda I. Banet Joel C. Trexler Florida International University i

2 Ecological Assessment: Measures the performance of restoration projects. Interprets performance based on research, monitoring, modeling.

3 Monitoring: Documents the effect of management after controlling for environmental variation.

4 Trophic Hypothesis Regional-scale environment Vegetation structure Landscape configuration Local-scale environment Frequency of drought Time since end of last drought Duration and severity of last drought event Local-scale environment Microtopography Water depth Water recession rate Periphyton standing stock and composition Prey population size Prey availability (dry season prey concentrations ) Large fish population size Wading bird characteristics (morphology, foraging behavior) Wading bird responses Habitat selection, food intake, foraging aggregations, g physiological condition Nesting effort and productivity Mortality Population size

5 Trophic Hypothesis Regional-scale environment Vegetation structure Landscape configuration Local-scale environment Frequency of drought Time since end of last drought Duration and severity of last drought event Local-scale environment Microtopography Water depth Water recession rate Periphyton standing stock and composition Prey population size Prey availability (dry season prey concentrations ) Large fish population size Wading bird characteristics (morphology, foraging behavior) Wading bird responses Habitat selection, food intake, foraging aggregations, g physiological condition Nesting effort and productivity Mortality Population size

6 Performance Measures Recover slowly (years), effected by local drying - bluefin killifish

7 Performance Measures Recover slowly (years), effected by local drying - bluefin killifish Recover quickly (months) decline as Recover quickly (months), decline as site remains flooded flagfish

8 Performance Measures Recover slowly (years), effected by local drying - bluefin killifish Recover quickly (months), decline as site remains flooded flagfish Recover quickly (months), effected by local and regional drying eastern mosquitofish

9 Performance Measures Recover slowly (years), effected by local drying - bluefin killifish Recover quickly (months), decline as site remains flooded flagfish Recover quickly (months), effected by local and regional drying eastern mosquitofish Not effected by short drying events, y y g average depth past 6 months, regional drying Everglades crayfish

10 Modwaters Assessment Biological Assessment Aquatic Fauna All Fish 2006 Assessment Meets target Daily Hydrology Data Caution Monitoring i site Does not meet target Aquatic Fauna Sampling 12 years of data 3 regions 20 sites (total) 3-5 plots 5-7 throws

11 Modwaters Assessment data collection Hydrology Data compiled from data collected by the USGS and NPS Fauna data collected by throw trapping

12 DATASET Modwaters Assessment modeling

13 Modwaters Assessment modeling DATASET Subset Subset 1 2

14 Modwaters Assessment modeling DATASET Subset Subset 1 2 Aggregated by site Model: relationship between hydrology and fish count

15 Modwaters Assessment modeling DATASET Subset Subset 1 2 Aggregated by site Model: relationship between hydrology and fish count Predict fish count numbers in subset 2 based on target hydrology

16 Modwaters Assessment modeling DATASET Subset Subset 1 2 Aggregated by site Model: relationship between hydrology and fish count Predict fish count numbers in subset 2 based on target hydrology Interpretation Compare model predictions to observed fish count to assess how well each site is meeting restoration targets

17 Modwaters Assessment Interpretation

18 Problem: Not all systems have long term time series data, so this method of modeling cannot be directly applied.

19 Question: Can spatial data be substituted for temporal data when creating predictive e models?

20 Complications Some factors may vary spatially, but not temporally (or vice versa): Community structure -Predation -Competition Nutrients/Food availability Mechanism of recovery -dispersal and recruitment biology of species -affected by the landscape.

21 Beyond Modwaters - CERP CERP Covers larger spatial area than Modwaters Fewer years of data 4 compared to 12 Sampled once a year ~ October

22 Beyond Modwaters - CERP CERP Covers larger spatial area than Modwaters Fewer years of data 4 compared to 12 Sampled once a year ~ October

23 Space vs. Time in Modwaters

24 Space vs. Time in Modwaters

25 Space vs. Time in Modwaters

26 Space vs. Time in Modwaters

27 Space vs. Time in Modwaters This creates a dataset t where spatial variation is small relative to temporal variation

28 Space vs. Time in Modwaters Year

29 Space vs. Time in Modwaters Sampling Period Year

30 Space vs. Time in Modwaters Sampling Period Year

31 Space vs. Time in Modwaters Sampling Period Year

32 Space vs. Time in Modwaters Sampling Period Year Creates a dataset with greater spatial variation, and reduces the temporal variation.

33 Space vs. Time in Modwaters logistic population growth model Temporal Data (aggregated by site) p < 0.05 for all models Mean r 2 = Spatial Data (aggregated by season) p < 005f 0.05 for all models Mean r 2 = DSLDD

34 CERP 3 Log Bluefin Killifish DSLDD Logistic poulation growth model p < r 2 = 0.54

35 CERP predicts Modwaters CERP data is primarily collected in October, which corresponds with period 4.

36 CERP predicts Modwaters CERP data is primarily collected in October, which corresponds with period 4.

37 Modwaters predicts CERP illifish Bluefin K Log B Using Periods 3, 4, 5: r 2 = p < DSLDD

38 Modwaters predicts CERP illifish Bluefin K Log B Using Periods 3, 4, 5: r 2 = p < BIAS DSLDD

39 Fit by region Currently too few observations in CERP to include both region and sampling period in the model

40 Fit by region Currently too few observations in CERP to include both region and sampling period in the model In modwaters, including both region and, g g period increases the fit of the model

41 Conclusions Both location and season affect population response after a drying event

42 Conclusions Both location and season affect population response after a drying event CERP currently does not have enough data to construct a CERP currently does not have enough data to construct a model with both location and season included

43 Conclusions Both location and season affect population response after a drying event CERP currently does not have enough data to construct a model with both location and season included Current model has relatively good fit, and as more data is collected, it can be refined even more.

44 Conclusions Within the Everglades, space can reasonably be substituted for time when creating gpredictive models

45 Conclusions Within the Everglades, space can reasonably be substituted for time when creating gpredictive models Caution must be used when using this approach. -The Everglades is a relatively small spatial scale compared to what some ecological and climate change studies use. -As spatial scale increases, so does potential for p, p complicating factors

46 Future Exploration Inclusion I l i of nutrient t variability Inclusion of other species with different life histories i

47 Acknowledgements Funding Sources SFWMD Everglades National Park FIU Modwaters Monitoring Jeff Kline Recover Trophic Group Jana Newman Andy Gottlieb Aaron Parker and Chuck Goss