Harvard Forest

Data Archive

HF182

Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011

Related Publications

Data

• hf182-01: trees current environment (preview)
• hf182-02: trees past (preview)
• hf182-03: environment past (preview)
• hf182-04: R code - hindcast
• hf182-05: R code - niche overlap functions
• hf182-06: R code - occ prep functions
• hf182-07: R code - mkMatUtil
• hf182-08: R code - glm amcmc
• hf182-09: R code - sp Diag
• hf182-10: R code - nonspatialspGLM

Overview

• Lead: Sydne Record, Aaron Ellison
• Investigators: Matt Fitzpatrick
• Contact: Information Manager
• Start date: 2010
• End date: 2011
• Status: complete
• Location: Eastern North America
• Latitude: +27.3 to +48.3 degrees
• Longitude: -101.7 to -67.2 degrees
• Elevation: 0 to 1325 meter
• Datum: WGS84
• Taxa: Fagus grandifolia (american beech), Tsuga canadensis (eastern hemlock), Tsuga caroliniana (carolina hemlock)
• Release date: 2023
• Language: English
• EML file: knb-lter-hfr.182.13
• DOI: digital object identifier
• EDI: data package
• DataONE: data package
• Related links:
• Study type: historical, paleological, modeling
• Research topic: ecological informatics and modelling; historical and retrospective studies; regional studies
• LTER core area: population studies
• Keywords: biogeography, modeling, paleoecology, spatial variability, trees
• Abstract:

  The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.

• Methods:

  To distinguish between the importance of climatic versus spatial predictors, we fit three Bayesian models to modern occurrence data of Fagus and Tsuga, two tree genera whose distributions can be reliably inferred from fossil pollen: a pure spatial model, a non-spatial model with climatic predictors, and a model with climate predictors and spatial terms. Using high temporal resolution paleoclimate data, we hindcasted the models for 8000 years, and compared model projections with palynological data.

• Organization: Harvard Forest. 324 North Main Street, Petersham, MA 01366, USA. Phone (978) 724-3302. Fax (978) 724-3595.

• Project: The Harvard Forest Long-Term Ecological Research (LTER) program examines ecological dynamics in the New England region resulting from natural disturbances, environmental change, and human impacts. (ROR).

• Funding: National Science Foundation LTER grants: DEB-8811764, DEB-9411975, DEB-0080592, DEB-0620443, DEB-1237491, DEB-1832210.

• Use: This dataset is released to the public under Creative Commons CC0 1.0 (No Rights Reserved). Please keep the dataset creators informed of any plans to use the dataset. Consultation with the original investigators is strongly encouraged. Publications and data products that make use of the dataset should include proper acknowledgement.

• License: Creative Commons Zero v1.0 Universal (CC0-1.0)

• Citation: Record S, Ellison A. 2023. Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011. Harvard Forest Data Archive: HF182 (v.13). Environmental Data Initiative: https://doi.org/10.6073/pasta/edb14581fa6dae179e559e25a81ea5a9.

Detailed Metadata

hf182-01: trees current environment

1. lon: longitude of 30 minute grid cell (North America Datum 83) (unit: degree / missing value: NA)
2. lat: latitude of 30 minute grid cell (North America Datum 83) (unit: degree / missing value: NA)
3. count: number of FIA plots per grid cell (unit: number / missing value: NA)
4. fagus: presence or absence of Fagus grandifolia in a grid cell
   • 1: present
   • 0: absent
5. tsuga: presence or absence of hemlock in a grid cell
   • 1: present
   • 0: absent
6. bio1.0: current annual mean temperature (standard deviation units) (unit: celsius / missing value: NA)
7. bio12.0: current annual precipitation (standard deviation units) (unit: dimensionless / missing value: NA)
8. bio2.0: current mean diurnal range (standard deviation units) (unit: dimensionless / missing value: NA)
9. bio4.0: current temperature seasonality (standard deviation units) (unit: dimensionless / missing value: NA)
10. bio7.0: current temperature annual range (standard deviation units) (unit: dimensionless / missing value: NA)
11. bio15.0: current precipitation seasonality (standard deviation units) (unit: dimensionless / missing value: NA)
12. albx: Albers Equal Area Conic x value (unit: meter / missing value: NA)
13. alby: Albers Equal Area Conic y value (unit: meter / missing value: NA)

hf182-02: trees past

1. kyears.bp: thousand years before present
   • 1: 1000 years before present
   • 2: 2000 years before present
   • 3: 3000 years before present
   • 4: 4000 years before present
   • 5: 5000 years before present
   • 6: 6000 years before present
   • 7: 7000 years before present
   • 8: 8000 years before present
2. albx: Albers Equal Area Conic x value (unit: meter / missing value: NA)
3. alby: Albers Equal Area Conic y value (unit: meter / missing value: NA)
4. lat: latitude of 30 minute grid cell (North America Datum 83) (unit: degree / missing value: NA)
5. lon: longitude of 30 minute grid cell (North America Datum 83) (unit: degree / missing value: NA)
6. count: number of FIA plots per grid cell (unit: number / missing value: NA)
7. fagus.half: presence or absence of Fagus grandifolia based on low pollen threshold of 0.5%
   • 1: present
   • 0: absent
8. fagus.1: presence or absence of Fagus grandifolia based on low pollen threshold of 1%
   • 1: present
   • 0: absent
9. tsuga.1: presence or absence of Tsuga canadensis based on low pollen threshold of 1%
   • 1: present
   • 0: absent
10. tsuga.2: presence or absence of Tsuga canadensis based on low pollen threshold of 2%
    • 1: present
    • 0: absent

hf182-03: environment past

1. kyears.bp: thousand years before present (values: 1-8, equivalent to 1000 – 8000 years before present) (unit: nominalYear / missing value: NA)
2. albx: Albers Equal Area Conic x value (unit: meter / missing value: NA)
3. alby: Albers Equal Area Conic y value (unit: meter / missing value: NA)
4. lat: latitude of 30 minute grid cell (North America Datum 83) (unit: meter / missing value: NA)
5. lon: longitude of 30 minute grid cell (North America Datum 83) (unit: meter / missing value: NA)
6. bio1: annual mean temperature (standard deviation units) (unit: dimensionless / missing value: NA)
7. bio12: annual precipitation (standard deviation units) (unit: dimensionless / missing value: NA)
8. bio2: mean diurnal range (standard deviation units) (unit: dimensionless / missing value: NA)
9. bio4: temperature seasonality (standard deviation units) (unit: dimensionless / missing value: NA)
10. bio7: temperature annual range (standard deviation units) (unit: dimensionless / missing value: NA)
11. bio15: precipitation seasonality (standard deviation units) (unit: dimensionless / missing value: NA)

hf182-04: R code - hindcast

• Compression: none
• Format: R script
• Type: script

hf182-05: R code - niche overlap functions

• Compression: none
• Format: R script
• Type: script

hf182-06: R code - occ prep functions

• Compression: none
• Format: R script
• Type: script

hf182-07: R code - mkMatUtil

• Compression: none
• Format: R script
• Type: script

hf182-08: R code - glm amcmc

• Compression: none
• Format: R script
• Type: script

hf182-09: R code - sp Diag

• Compression: none
• Format: R script
• Type: script

hf182-10: R code - nonspatialspGLM

• Compression: none
• Format: R script
• Type: script