Climate Change Impact Assessment for Pinus palustris Mill.

Project Overview

Challenge: Longleaf pine ecosystems have declined from 37 million hectares to less than 5% of their original range (Jose et al., 2006; Frost, 2006). With climate change posing additional threats, conservation planning requires understanding how suitable habitat may shift across time periods.

Solution: I developed an innovative habitat suitability model that integrates bioclimatic, topographic, and fire ecology variables with climate projections to assess habitat suitability across three time periods: current (1991-2020), mid-century (2041-2070), and late-century (2071-2100).

Innovation: Unlike bioclimatic predictor-exclusive models, this approach incorporates fire regime suitability and canopy openness variables that capture longleaf pine's fire-dependent habitat requirements, providing projections within the context of longleaf pine's unique ecology (Hirzel & Le Lay, 2008).

Key Findings

Model Performance

0.706

Test AUC (Decent discrimination)

Current Suitable Habitat

106,598

km² in Alabama (81.3% of state)

Climate Refugia

88.3%

Stable habitat across all periods

Late-Century Change

+2.9%

Modest habitat increase projected

Interesting Insights: In my model, longleaf pine demonstrates remarkable climate resilience in Alabama. While other fire-dependent species face 16-30% habitat losses (Parks et al., 2019; Stevens-Rumann et al., 2018), longleaf pine habitat actually increases slightly under future climate scenarios, suggesting strong adaptive capacity. Additionally, regional trends within the study area reveal themselves, including habitat resilience in southeast Alabama. Compared to current U.S. Forest Service projections in their Climate Change Atlas that have longleaf pine habitat expanding significantly under a number of scenarios, habitat expansion is far more constrained in my study (however, it should be noted that other factors, like study extent and methodology, may account for this and other discrepancies; U.S. Forest Service, 2020).

Methodological Highlights

The integration of ecological predictors improved model performance, pushing Test AUC above the critical 0.7 threshold (Fielding & Bell, 1997; Hosmer et al., 2013; Swets, 1988). Canopy openness emerged as the most important predictor (35.7% contribution), validating the importance of fire-maintained savanna structure in habitat suitability modeling (Mitchell et al., 2006).

Variable Importance Dynamics

Analysis revealed how environmental relationships evolve across time periods, providing insights for adaptive conservation planning:

35.7% → 28.8% → 39.0%

Canopy Openness

20.3% → 8.6% → 4.4%

Spring Precipitation

18.7% → 29.7% → 27.1%

Sand Content (0-5cm)

1.5% → 3.0% → 4.0%

Climatic Moisture Deficit

2.8% → 6.1% → 1.6%

Fire Regime Suitability

11.6% → 4.2% → 4.9%

Mean Annual Temperature

3.5% → 13% → 9.8%

Mean Annual Precipitation

6.0% → 6.4% → 9.1%

Topographic Wetness Index

Key Temporal Patterns

Fire-dependent structure remains critical: Canopy openness maintains dominance across all periods (Boyer, 1990)
Moisture relationships shift: Spring precipitation declines while annual drought stress increases
Soil drainage amplification: Well-drained sandy soils become increasingly important mid-century
Mid-century vulnerability: Multiple factors converge during 2041-2070 transition period

Technical Approach

Data Integration & Processing

Species Data: 246 high-quality GBIF occurrence records with 2km spatial filtering (GBIF.org, 2025)
Climate Variables: ClimateNA v7.30 with 13-GCM ensemble under SSP2-4.5 scenario (Wang et al., 2016)
Ecological Enhancement: Custom fire regime suitability and canopy openness variables
Bias Correction: Sampling bias surface derived from 209,496 plant observations (GBIF.org, 2025)

Modeling Framework

Algorithm: MaxEnt with optimized parameters (LQHP features, 1.5 regularization) (Phillips et al., 2006)
Variable Selection: Multi-criteria optimization balancing VIF, correlation, and ecological domains (Dormann et al., 2013; Feng et al., 2019)
Validation: 10-fold cross-validation with independent test sets
Climate Projections: Three time periods with refugia analysis

Study Design Spotlight

Fire Regime Integration: First longleaf pine model to incorporate LANDFIRE fire return interval data (LANDFIRE, 2016)
Habitat Structure Variables: NLCD-derived canopy openness capturing fire-maintained savanna conditions (Homer et al., 2015)
Temporal Variable Analysis: Dynamic assessment of how species-environment relationships evolve
Multi-scale Processing: Systematic integration of variables from 30m to 1km resolution

🗺️ Explore Interactive Results

View detailed maps and county-level impact analysis through my interactive web mapping application.

Launch Interactive Map

Conservation Implications

Strategic Recommendations

Prioritize Climate Refugia: Focus resources on 94,126 km² of stable habitat areas
Adaptive Fire Management: Intensify prescribed burning during mid-century vulnerability window
Target Well-Drained Soils: Sandy soil sites provide crucial buffering during climate transition
Regional Focus: Southeastern Alabama shows highest resilience; central regions need enhanced management

Broader Impact

This research demonstrates that incorporating species-specific ecological requirements improves model realism for fire-dependent species (Dubuis et al., 2022). The temporal variable importance analysis reveals that conservation strategies must adapt over time, with different factors becoming critical during transition periods.

The methodology is transferable to other fire-dependent ecosystems worldwide and provides a framework for understanding how species-environment relationships may evolve under climate change beyond simple habitat area projections (Franklin, 2010).

Future Research Directions

This study opens several avenues for future research. The overall project could be improved through expanded study area extent and higher resolution (beyond the 1 km used here), factors which were limited due to computational constraints of using a personal workstation. Future work could integrate more detailed data, such as soils from SSURGO and more up-to-date LANDFIRE data. The input DEM could be LiDAR-derived, which could produce higher-quality derivatives, like TWI, slope, and aspect. Additional research directions include:

Effects of spatial extent and model design: Habitat suitability (and other ecological measures, such as species richness) could be modeled and compared at various extents and resolutions to assess model performance (Nizamani et al., 2023)
Ecosystem-level modeling: Expansion to include associated species and community dynamics (Zurrell et al., 2020)
Scenario expansion: Analysis under additional emission scenarios and time horizons
Methodological enhancement: Modern habitat suitability modeling software, such as the sdmverse R metapackage, could be used to enhance model design and user experience (Kass et al., 2024)

Technical Skills Demonstrated

Geospatial Analysis: R/QGIS integration, end-to-end data processing, coordinate system management
Habitat Suitability Modeling: MaxEnt optimization, variable selection, bias correction
Climate Data Processing: ClimateNA processing, multi-temporal analysis, raster grid handling
Ecological Variable Development: Fire regime classification, habitat structure quantification
Quantitative Analysis: Cross-validation, performance metrics, multi-criteria assessment
Data Visualization: Interactive web mapping, summary graphics, scientific communication
Conservation Planning: Refugia analysis, adaptive management frameworks, decision support

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