# Presentation: 452400

## Biophysical and Land-use Controls on Biodiversity: Regional to Continental Scales

**Montana State University**
**Curt Flather**
**Colorado State University**

**Biophysical and Land-use Controls on Biodiversity: Regional to Continental Scales**

**Joint Workshop on NASA Biodiversity, Terrestrial Ecology, and Related Applied Sciences**
**May 1-2, 2008 **

## Research Questions:

**Human Land Use**
**(Land use,**
**Home  density)**

**Current Biodiversity Value**

**Biophysical Potential**
**(i.e. Energy,**
**Habitat structure)**

**Conservation Priority/Strategies**

**Research Questions:**
**1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use?**
**2: How are these patterns of biodiversity modified due to land use?**
**3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change?**

## Ecosystem Energy as a Framework for Conservation?

**Hawkins et al. 2003**

**Key Hypothesis**
**Primary productivity, and the factors that drive it (climate, soils, topography), ultimately influence: **
** ****disturbance and succession**
** ****resources for organisms**
** ****species distributions and demographies **
** ****community diversity**
** ****responses to habitat fragmentation, land use, exotics**
** ****effectiveness of conservation**

## Framework for Classifying Ecosystems for Conservation

| Conservation Category | Low Energy | Medium Energy | High Energy |
| --- | --- | --- | --- |
| Conservation Zones | Protect high energy places | Protect more natural areas | Protect low energy places |
| Disturbance | Use fire, flooding, logging judiciously in hotspots | Similar to “Descending” |  Use disturbance to break competitive dominance

 Use shifting mosaic harvest pattern

 Maintain structural complexity |
|  |  |  |  |
|  |  |  |  |
| Landscape Pattern | Maintain connectivity due to migrations |  | Manage for patch size and edge |
| Sensitive Species | Many species with large home ranges and low population sizes due to energy limitations |  | Forest interior species |
| Exotics |  | High exotics likely due to productivity and high land use |  |
| Protected Area Size | Large  | Smaller | Smaller |
| Land Use | Low overall | High overall | Moderate overall |
|  | Focused on hot spots | Emphasize “backyard” conservation | More random across landscape |
|  | Plan development outside of hotspots | Apply restoration |  |

**Framework for Classifying Ecosystems for Conservation**

## Focus of This Talk

**Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use?**
** ****Which MODIS energy products best explain patterns of bird diversity across North America?**
** ****Does the relationship between birds and energy (slope and sign) differ between places of low, medium, and high energy?**

## History of Predictor Variables Used to Explain Species Energy Patterns

**History of Predictor Variables Used to Explain Species Energy Patterns**

**NDVI = (NIR - red) / (NIR + red)**

- Latitude (MacArthur 1972)

- Evapotranspiration (Currie  1987, Hawkins et al 2003)

- Ambient temperature (Acevedo and Currie, 2003)

- Water/Energy Balance (Hawkins et al 2003)

- 1960’s
- 1970’s
- 1980’s
- Remote Sensing advances
- 1990’s
- 1999
- present

-   MODIS Land Surface Product Development

- NDVI
- EVI
- GPP (simulated from fpar, climate, cover type)
- NPP

-  AVHRR

-  Thematic Mapper

- Precipitation (Chown et al., 2003)

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## Does NDVI have limitations that higher order products address?

- Phillips, L.B., Hansen, A.J. & Flather, C.H. (in press), Remote Sensing of Environment

- Not complete vegetation cover
- (backscatter)

- Dense vegetation
- (saturation)

**Does NDVI have limitations that higher order products address?**

- GPP NPP

**Notes:**
Since NDVI has been the historic surrogate for biomass, productivity, energy in general in species energy studies, we began wondering if the higher order products would better inform us of SER, or if the products and NDVI would be so highly correlated that little additional information would be provide.

## What is the shape of the species energy relationship?

## What is the shape of the relationship?

**What is the shape of the relationship?**
**Why?**

- energy

- energy

- richness

- richness

- _Hypothesis:_
- More individuals hypothesis
**(Wright, 1983, Preston, 1962; MacArthur & Wilson,**
**1963, 1967)**
** **

- _Hypothesis:_
- Competitive exclusion
**(MacArthur and Levins, 1964, 1967; Grime, 1973 1979, Rosenzweig 1992)**

## Energy as a framework for conservation

**Energy as a framework for conservation**

**Identify and manage hotspots judiciously**

**Protect harsh places**
**But most of landscape is high in diversity, so more options for multiple use such as shifting mosaic approach to forest management; **

**If slope and sign vary among energy levels, conservation strategies should differ among low, intermediate, and high energy places.**

## Methods

**Bird richness from BBS data for years 2000-2005, estimated richness using COMDYN**
**Subset of routes (1838) to represent terrestrial natural routes (exclude human dominated land uses, water impacted)**

**Methods**

** ****Survey unit is a roadside route**
** ****39.4 km in length**
** ****50 stops at 0.8 km intervals**
** ****Birds tallied within 0.4 km**
** ****3 minute sampling period**
** ****Water birds, hawks, owls, and nonnative species excluded in this analysis**

## Methods

**  ****Calculate both ****breeding season averages ****for NDVI, EVI and GPP and ****annual averages ****of NDVI, EVI, and GPP, NPP**

**Methods**

**Annual Average MODIS GPP**

- NDVI
- Enhanced Vegetation Index
- Gross Primary Production
- Net Primary Production

_**MODIS products used**_

## Methods

_**Statistical analysis**_
** ****Stratify BBS routes by vegetation life from and density (MODIS VCF)**
**Perform correlation analyses between predictors across vegetative strata and regression analysis between predictor and response variables across strata.**

**Methods**

**Notes:**
To identify areas that would most vunerable to the limitations of NDVI, we used the vcf product

Subpixel measure of lifeform.....percent bare, herbaceous, tree

## Methods

_**Statistical analysis**_
** ****Perform regression analysis with linear, polynomial, spline and breakpoint spline models**
**  ****Perform simple linear regression analysis of four quartiles of GPP to determine slopes and significance**
**  ****Assess and control for effects of spatial correlation on significance levels and coefficients using generalized least squares analyses.**

**Methods**

## Results: Best Predictor?

| variable | time | model | overall rank | delta aic-R from best overall | r2 | adj r2 |
| --- | --- | --- | --- | --- | --- | --- |
| GPP | annual | quadratic | 1 | 31.625 | 0.5353 | 0.5346 |
| NDVI | annual | quadratic | 2 | 72.939 | 0.5212 | 0.5205 |
| NPP | annual | quadratic | 3 | 96.321 | 0.513 | 0.5123 |
| EVI | annual | quadratic | 4 | 180.654 | 0.4824 | 0.4816 |
| NDVI | BS | linear | 5 | 288.095 | 0.3561 | 0.3556 |
| NDVI | BS | quadratic | 6 | 309.786 | 0.4406 | 0.4398 |
| NDVI | annual | linear | 7 | 331.438 | 0.4219 | 0.4215 |
| NPP | annual | linear | 8 | 374.89 | 0.4035 | 0.4031 |
| EVI | BS | linear | 9 | 395.62 | 0.4296 | 0.4292 |
| EVI | BS | quadratic | 10 | 395.62 | 0.3954 | 0.3945 |
| EVI | annual | linear | 11 | 410.694 | 0.3878 | 0.3874 |
| GPP | annual | linear | 12 | 411.244 | 0.3876 | 0.3872 |
| GPP | BS | linear | 13 | 416.29 | 0.376 | 0.3756 |
| GPP | BS | quadratic | 14 | 416.29 | 0.3863 | 0.3854 |

**Results: Best Predictor?**

## Results: Best Predictor?

- Correlation between NDVI and GPP across vegetation classes

**Results: Best Predictor?**

**Notes:**
Now see that in areas of herbaceous dominated cover, ndvi and gpp very tightly correlated....bare and forest classes are not.

## Results: Best Predictor?

**Results: Best Predictor?**

**Notes:**
Bare are strength of relationship with bird richness, lines are correlation.

Where ndvi and gpp/npp are least correlated, products are better predictors.

## Intrepertation: Best Predictor

- GPP NPP

**Intrepertation: Best Predictor**

- Annual formulation better than breeding season for all predictors
- Results suggest that GPP better represents primary productivity and bird richness than NDVI in low and high vegetation areas
- GPP should be used especially in desert areas (bare ground) and dense forests (SE and PNW)
- Results help explain differences in past studies on predictors and strength of relationships: will depend on vegetation density of samples.

**Notes:**
Since NDVI has been the historic surrogate for biomass, productivity, energy in general in species energy studies, we began wondering if the higher order products would better inform us of SER, or if the products and NDVI would be so highly correlated that little additional information would be provide.

## Vegetation Coninuous fields

- Blue gradient - bare ground
- Red gradient - forest cover
- Green gradient - herbaceous cover

**Notes:**
First paper more technically oriented, 
When in exploratory analysis, we expected to find that the MODIS higher order products were superior in statistical ability to predict bird diversity.

## Results: Slope and Shape?

**Results: Slope and Shape?**

**Notes:**
Scatterplot and predicted regression line of bird richness and GPP, note larger scatter in lower gpp values, tighter relationship in mid and upper gpp values.  Note negative relationship at upper gpp values.

## Results: Slope and Shape?

**Results: Slope and Shape?**

## Results: Slope and Shape?

- a=0.083
- p<.001

- a=0.013
- p<.001

- a=0.005
- p<.036

- a= - 0.018
- p<.001

**Results: Slope and Shape?**

## Results: Slope and Shape?

| Variable (annual) | model | overall rank | delta aic-R from best overall | r2 | adj r2 |
| --- | --- | --- | --- | --- | --- |
| GPP | Spline (cubic) | 1 | 0 | 0.5484 | 0.5464 |
| GPP | Breakpoint (linear) | 2 | 26.304 | 0.5384 | 0.5371 |
| GPP | Quadratic | 3 | 31.625 | 0.5353 | 0.5346 |
| NDVI | Quadratic | 4 | 72.939 | 0.5212 | 0.5205 |
| NDVI | Spline (cubic) | 5 | 74.416 | 0.5234 | 0.5214 |
| NDVI | Breakpoint (linear) | 6 | 83.322 | 0.519 | 0.5176 |
| NPP | Spline (cubic) | 7 | 91.33 | 0.5176 | 0.5155 |
| NPP | Quadratic | 8 | 96.321 | 0.513 | 0.5123 |
| NPP | Breakpoint (linear) | 9 | 101.476 | 0.5126 | 0.5112 |
| EVI | Spline (cubic) | 10 | 180.609 | 0.4854 | 0.4832 |
| EVI | Quadratic | 11 | 180.654 | 0.4824 | 0.4816 |
| EVI | Breakpoint (linear) | 12 | 186.134 | 0.4818 | 0.4803 |

**Results: Slope and Shape?**

## Results: Slope and Shape?

**Results: Slope and Shape?**

## Results: Slope and Shape?

**Results: Slope and Shape?**

## Interpretation: Slope and Shape

- energy

- energy

- richness

- richness

**More Individuals Hypothesis**
**Predicts higher habitat heterogeneity in areas of high richness**

**Competitive Exclusion Hypothesis**
**Predicts high canopy cover in overstory and lower habitat heterogeneity**

**Interpretation: Slope and Shape**

## Slide 27

## Disturbance Effects and Ecosystem Energy

- Diversity increases with disturbance under high energy and decreases under low energy.

- High

- Low

- Low

- High

- Diversity

- Landscape Productivity

- Intensity of Disturbance

- Springfield

- Cle Elum

- Huston 1994.

- McWethy et al. in review.

## Next Steps:

**Human Land Use**
**(Land use,**
**Home  density)**

**Current Biodiversity Value**

**Biophysical Potential**
**(i.e. Energy,**
**Habitat structure)**

**Conservation Priority/Strategies**

**Next Steps:**
**1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use?**
**2: How are these patterns of biodiversity modified due to land use?**
**3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change?**

## Next Steps:

**Human Land Use**
**(Land use,**
**Home  density)**

**Current Biodiversity Value**

**Biophysical Potential**
**(i.e. Energy,**
**Habitat structure)**

**Conservation Priority/Strategies**

**Next Steps:**
**1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use?**
**2: How are these patterns of biodiversity modified due to land use?**
**3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change?**

- Vegetation structure from ELVS/GLAS

## Next Steps

- Balmford et al. 2001

- Vertebrates and NPP

- This study.

- Humans and NPP

**Next Steps**

## Slide 32

## Does the shape of the relationship vary with energy levels (geographically)?

**Does the shape of the relationship vary with energy levels (geographically)?**

## Is the negative portion of the unimodal relationship real?

- Nugget .002
- Sill .006
- So using GLS, enter (800000, .25)

## Slide 35

## NDVI = (NIR - red) / (NIR + red) 

- NDVI = (NIR - red) / (NIR + red)

**Do higher order MODIS products help us answer these questions?  **

**Notes:**
First paper more technically oriented, 
When in exploratory analysis, we expected to find that the MODIS higher order products were superior in statistical ability to predict bird diversity.

## Results: Best Predictor?

- Phillips, L.B., Hansen, A.J. & Flather, C.H. (in press), Remote Sensing of Environment

- Not complete vegetation cover
- (backscatter)

- Dense vegetation
- (saturation)

- Does NDVI have limitations that higher order products address?

- GPP NPP

**Results: Best Predictor?**

**Notes:**
Since NDVI has been the historic surrogate for biomass, productivity, energy in general in species energy studies, we began wondering if the higher order products would better inform us of SER, or if the products and NDVI would be so highly correlated that little additional information would be provide.

## This slide corresponds to green cells in previous slide