Results: Testing the ELIA model against biodiversity
empirical data
Our study provides evidence that depending on the interplay between
energy storage and its distribution pattern (E ·I ), the
societal metabolic flows driven by farming imprint different landscapes
(L ) where agroecosystems may either enhance or decrease
populations and species richness of butterflies and birds in the
Barcelona Metropolitan Region. Significant results of Structural
Equation Models (SEM) are summarized in Fig. 2 and 3 ,
while complete data are shown in Tables S1 to S8 inAnnex A of the Supplementary Information .
[Insert Table 1 here]
The butterfly’s Principal Component Analysis (Table 1 ) shows
that the first factor is the land cover composition of the landscape
(Cm1; 43.9% of variance) that obtains higher loading for forest, while
the second (Cm2; 35.5%) presents greater loading for cropland. The
first component of land metrics which assesses landscape configuration
through land metrics (Cn1; 72.4%) shows negative loadings for diversity
and fragmentation and positive loadings for grain size and connectivity,
while the second component is more heterogeneous and contributes much
less to explain the variance (Cn2; 12.2%) despite being mostly
associated to connectivity metrics (including effective mesh size).
[Insert Figure 2 here]
The SEM results (Fig. 2 ) show that total butterfly observations
(TBOB) are positively associated to ELIA values while species
richness (TBSR) does not show any significant correlation with this
model. ELIA is negatively associated to Cm1 in the TBSR model,
but not in the TBOB one. In both models, Cm1 and Cn1 were negatively
associated. The R2 for the endogenous variables of
TBSR and TBOB models were 0.316 and 0.266 respectively. When
disentangling the effects of ELIA components (E , Iand L ), I shows a positive association with both TBSR and
TBOB, while E and L are only significant and positively
associated to TBOB. Landscape composition (Cm) and configuration (Cn)
are not significantly associated to any biodiversity component, yet Cm1
is negatively associated to ELIA values in both models and Cm2 is
negatively associated to I only in the TBOB model. The
R2 for the endogenous variables of TBSR and TBOB are
0.420 and 0.334 respectively.
In the bird’s Principal Component Analysis, the first factor of
landscape composition (Cm1; 39.3% of variance) shows higher loading for
forest, while the second factor (Cm2; 31.8%) has greater loadings for
scrubland and cropland (Table 1 ). The first factor of landscape
configuration (Cn1; 53.6%) records higher (negative) loadings for
landscape diversity and fragmentation, while the second (Cn2; 27.3%)
shows greater (positive) association for ecological connectivity and
effective mesh size.
[Insert Figure 3 here]
The SEM results (Fig. 3 ) show that breeding bird species
richness (BBSR) and wintering bird species richness (WBSR) are
positively related to ELIA values and Cm2, and negatively related
to Cn2. ELIA is negatively related to Cn1 in both models, and
with Cm1 only in the BBSR model. Cn2 and Cm1 are also negatively
associated in this model. The R2 for the endogenous
variables of BBSR and WBSR are 0.244 and 0.210 respectively. If we
disentangle the effects of ELIA components (E , Iand L ), I and E are positively and negatively
correlated, respectively, with both BBSR and WBSR. L is only
associated negatively to WBSR. E is negatively associated to Cm2
in both models, and to Cm1 only in the BBSR model. L and Cm1 are
negatively associated in this last model. The R2 for
the endogenous variables of BBSR and WBSR are 0.321 and 0.329
respectively.