2.4 Eco-effect analysis
The study operated partial-correlation analyses to characterize the
eco-effects about EP of LEP, AEP, and HEP ecosystems responding to
snow-onset and -end SP. The used means can exclude the confounding
effects caused by other climatic variables (e.g., temperature and solar
radiation) and the covariate effect (Piao et al., 2016) possibly
existing between those interannual snow-onset and -end SP series of the
same sites. The specific operation was deployed onto EP case by case
(LEP, AEP, and HEP), with the performance of SP in the two different
cases (snow-onset and -end SP) compared first and then the optimal SP
case identified. Note that in subject to the relatively short terms of
the examined EP and SP datasets, all of the partial correlation analyses
were operated at the significance level of p<0.1 (indicated by
*) for extracting more potential sites (i.e., 0.5°×0.5° geo-grids) with
distinguishable responses.
With the basic method of analyzing the EP-SP ecological links
determined, the answers to the two goal questions were sought then.
First, statistics of the three kinds of EP cases that performed with the
highest correlations with the corresponding- or
surrounding-geo-grid-related snow-onset or -end SP were individually
operated all over the study area. Then, we made their comparisons, with
particular interest on the scenario showing more cases that were
sensitive to the SP around – we termed this scenario as “neighborhood
eco-effect”, by referring to the mechanism-approximate concept in
social and economic sciences usually positing that neighborhoods have
either direct or indirect effects on individual human behaviors
(MacAllister et al. , 2001). Note that theoretically the
correlation-based eco-effect exploring and comparison as above were
based on two interannual EP and SP series at the individual geo-grid
level, different from those traditional spatial correlation analyses
based on two spatial maps of different variables. Thus, the issue of
autocorrelation often met in the latter scenario (Macias-Fauria et
al. , 2012) and its related solutions such as the Monte Carlo-based
techniques (Livezey & Chen, 1983) could be omitted in this study.
Further, after the existence of the neighborhood eco-effect was
detected, a statistical comparison of its EP-SP cases in terms of the
common eight directions – as “directional eco-effect” – was operated
to reveal the primary orientations of SP driving EP, for different
regions such as North America (>45°N) (NA) and North
Eurasia (>45°N) (NE). Simultaneously identifying these two
eco-effects was considered as the kernel theoretical basis of validating
the existence of periconnection.
To quantify the traits of the neighborhood eco-effect in details, a 3×3
window for each geo-grid (0.5°×0.5°) was set to restrict the conditions
in operating its partial-correlation analyses (termed as correlation
window, whose eight surrounding geo-grids from the right upper one
relate to north, northeast, east, southeast, south, southwest, west, and
northwest in a clockwise way). That is, for each targeted geo-grid the
correlations between its serial LEP, AEP, and HEP and the synchronous SP
series of both the same geo-grid and all the eight geo-grids around were
analyzed. Then, in order to characterize the features of the directional
eco-effect, the sum of the occurrences for any a targeted surrounding
geo-grid and its two neighboring geo-grids all at the edges of the
correlation window in the derived neighborhood-accounted responses was
used as an index. If the index is large, the directional eco-effect from
the targeted geo-grid to the centered geo-grid is strong. After the two
steps accomplished as above, the primary traits of periconnection could
be characterized.