Presented by Yves Laumonier (CIFOR-ICRAF) at 2023 FLARE Annual Meeting, 12-16 Oct 2023, Nairobi

1. Integrating bottom up and top-
down research pathways for
biodiversity assessments in
Integrated Landscape
Approaches
Yves Laumonier, Gemasakti Adzan,
Iin Simamora, Sari Narulita
2023 FLARE Annual Meeting, October 12-16 , Nairobi

2. How can landscape structure and scale
assessments be incorporated into biodiversity
management?
What is the impact of spatial scale on the
identification of critical habitat patch thresholds
for maintaining landscape connectivity?
What is the most influential scale at which the
environmental variables affect the taxonomic and
functional diversity of species?
How and where do we best match bottom-up and top-down
approaches in ILA research for biodiversity?

3. Multiple scales of pattern in landscapes, perception
varies according to scale
Landsat data vs. drone data interpretation
How to extrapolated from plot level?
The scaling up/down problem
regional
local
global

4. Detecting forest fires with satellites
(MODIS and VIIRS)
Assess and monitor the spatial
and temporal dynamics of shifting
cultivation
Landsat time-series 1990 to 2000 of
Normalized Difference Moisture Index
(NDMI)
Detecting breakpoints as potential fire
land clearing events
Result 1 Remote sensing
modeling, habitat degradation,
forest fires

5. Medium to fine ~
1:50.000-1:100.000
Regional scale ~
1:250.000-1:500.000
Global scale >
1:1000.000
Landsat-based land
cover (30 m) types with
~ 50 classes
Soil data (250 m) Climate data (1 km)
Biophysical
reference/coefficient
Data availability
Challenges:
● Unmatched spatial resolution between the data.
● Unmatched granularity between land cover class and
biophysical references → reclassifying land cover
means a lot of detail is lost.
● Resampled finer resolution data into coarser resolution
→ the model cannot describe “small change of land
cover” in ecosystem services valuation.
Original land cover
(50 classes)
Reclassified land
cover (14 classes)
Result 2 Ecosystem services modelling
How the predictive power of landscape structure is influenced by the
spatial scale at which predictor variables have been sampled

6. Result 3: Fragmentation and connectivity

7. • We found that the influence of landscape
characteristics on bird species richness and functional
guilds was scale-dependent
• Presence-absence data for modelling at regional,
global scale vs. abundance data from detailed field
survey.
• Coarser-scale data are more readily available, that can
explain the high number of studies at regional/global
scales
• Using fine scale survey with abundance data give us
capability to perform Multispecies Occupation Model
Result 4: Species Distribution Model
eBird- Little Spiderhunter (Arachnothera longirostra)
Understanding both the scale of threats and the scale over which
species live are essential for successful management and conservation

8. Lessons Learned
• Challenge remains to understand how data collected at finer scales relates to
larger areas.
• Spatial and temporal scales important to organisms or processes are not
necessarily the scales relevant to decision-makers.
• Biological interactions most likely occur at multiple scales, but challenges remain,
mostly because of presence absence data in SDM, and the lack of fine-grained
data.
• High value of citizen science and community-based monitoring, but same
challenge of presence absence data and often the generated information must be
interpreted with caution to avoid misleading conclusions.

9. Identifying the “right” scale, matching bottom-up and top-down
Scale is a prominent topic for ILA biodiversity studies (species distribution,
conservation/land use zoning, adaptive management) and should always be addressed.
Considering district as focal level is the best option for matching Bottom-up and Top-
down pathways to biodiversity assessment.
We advocate more investigations on scale effects in future modelling efforts, less
emphasize on global and more investigations on local to feed the focal level.
We advocate efforts on co-knowledge development, citizen science and community-
based monitoring, keeping in mind how to deal with potential data quality limitations.

10. Thank you
THANK YOU
©Yves Laumonier