24 Sep 2025

Why this matters (now)

  • EU Biodiversity Strategy: & GBF aim for 30% protected by 2030 (10% strictly protected)
  • Nature Restoration Law: requires restoration on 20% of EU area by 2030 and all degraded areas by 2050.
  • Denmark’s gap: About 16% land protected, but only ~1.6% (land) currently contribute to 30×30
  • Implication: We must add effective protection and do targeted restoration (rewetting, afforestation) to close the gap on time.

What decisions we actually face (WHERE × WHAT)

We choose WHERE to act and WHAT nature to establish

Two ways to set priorities

A) Ecological first principles (rules of thumb)
- Prefer large, connected, high-quality areas; avoid fragmentation.
- Ensure a portfolio of habitats. Works even where species data are missing.

B) Empirical estimates (data-driven)
- Score each cell/type using biodiversity potential (Richness, PD, Rarity) and carbon (AGB + soils, avoided peat).
- Optimize under policy constraints (rewetting, afforestation, contiguity).

Afforestation

Principles

  1. connected to existing nature areas (Areas with protetion schemes).
  2. connected to high-quality forest (Descidius forest).
  3. connected to the largest contiguous nature areas.

Cell Score Equations

Parameters for Each Cell

  1. Proportion of Existing Nature around cell \(i\) on a given radius \(\text{pEN}_i\).
  2. Proportion of Existing Nature that is forested around cell \(i\) on a given radius \(\text{pEN}^f_i\).
  3. Proportion of Forest not considered existing nature around cell \(i\) on a given radius \(\text{pEN}^f_i\).
  4. Logarithm of the Sum of areas of existing nature polygons around cell \(i\) on a given radius \(\log(A_{\text{sum}})\).

Parameters plotted

Cell Score Equations

  • Equation 1:

    \[ CS_1 = \frac{\text{pEN} + \text{pF} + \log(A_{\text{sum}})}{3} \]

  • Equation 2:

    \[ CS_2 = \frac{\text{pEN}^f + \text{pF} + \log(A_{\text{sum}})}{3} \]

  • These equations ensure that each cell’s score ranges from 0 to 1, encapsulating both the proposed area and its quality.

Cell score 1

Cell score 2

Closeup 1

Prioritization

How to plan for futute landscapes

\[ \text{maximize CI} = \color{Red}{W_1 \times \text{Biodiversity}} + \color{Purple}{W_2 \times \text{Carboon}} \times \color{Blue}{W_3 \times\text{Contiguity}_n} \]

  • Biodiversity (Nature for Nature W1 = 1, W3 = 1, W2 = 0)
    • Species richness
    • Phylogenetic Diversity
    • Rarity
  • Carbon (Nature for Society W1 = 1, W3 = 1, W2 = 1)
    • Above ground carbon
    • Bellow ground carbon
  • Contiguity (Always there)
    • Contiguity to new solutions
    • Contiguity to current nature

Results

Pareto frontier

Why we need to find our elbows

  • Best deals only: points below the curve are just worse.
  • Elbow = shrinking returns: before it, cheap big gains; after it, pricey tiny gains.
  • Defensible compromise: easy to explain and justify.
  • Focus: avoids extremes and gives 2–3 options to test.

Optimization elbow

What does this mean

  • The biodiversity value is a sum of Richnes, Phylogenetic diversity and rearity Per cell
  • It does not account for the overall effect such as complementarity
  • We check here extra metrics such as:
    • proportion of species preserved (IUCN)
    • Beta diversity
  • IUCN Criterion B thresholds (range size) AOO (Area of Occupancy)
    • CR: < 10 km²
    • EN: < 500 km²

Percentage critical

We are being very conservative

  • This should be for worldwide distribution
  • This only includes what is in our solution not what already exists

Beta diversity

  • Sørensen and Jaccard weigh overlap differently but here they agree on the elbow near w≈0.4.

  • Interpretation: around that weight you still retain high between-site distinctness while boosting carbon—after that, extra carbon comes with rapidly diminishing \(\beta\) diversity.

Land-use composition across weights

  • Ir stabilizes at around 70-75 per cent forest

Area composition by weight

Area composition by weight
Structure
Moisture
band_lab
weight Forest Open Dry Wet
0.0 29.6% 70.4% 47.8% 52.2%
0.1 70.5% 29.5% 56.4% 43.6%
0.2 74.1% 25.9% 62.4% 37.6%
0.3 74.9% 25.1% 67.6% 32.4%
0.4 75.1% 24.9% 71.6% 28.4%
0.5 75.5% 24.5% 74.1% 25.9%
0.6 75.8% 24.2% 75.2% 24.8%
0.7 76.0% 24.0% 75.8% 24.2%
0.8 76.3% 23.7% 76.3% 23.7%
0.9 76.6% 23.4% 76.6% 23.4%
1.0 76.9% 23.1% 76.9% 23.1%
1.1 77.1% 22.9% 77.1% 22.9%
1.2 77.2% 22.8% 77.2% 22.8%
1.3 77.3% 22.7% 77.3% 22.7%
1.4 77.4% 22.6% 77.4% 22.6%

How it changes in space

If we zoom in

How does carbon looklike

Total Afforestation score in each weight

Something to think about

  • Optimization
    • The elbow is with weights form 0.1 to 0.4
    • The “Optimal” optimization is with those weights where forest is 70 to 75%
    • Wetness is less clear
    • More forest, also seems to be better for mental health
    • It seems than form 0.1 Weigh on the Afforestation solutions are quite similar