1. Introduction
As the living basis of biosphere, ecosystem productivity (EP) is easily
influenced by climate changes (Ciais et al., 2003; Brookshire &
Weaver, 2014; Seddon et al., 2016; Madrigal-González et
al., 2017). This kind of influences has proved to become more serious
along with the increases of climatic extremes (Grimm et al.,2013), particularly for the lower-EP ecosystems such as those
temperature-sensitive Arctic ecosystems (Kim et al. , 2017) and
the higher-EP ecosystems such as those drought-susceptible tropical
rainforests (Bonal et al. , 2016). Probing the underlying rules of
such ecological effects (eco-effects), especially for the lower-EP and
higher-EP ecosystems with distinct responses (Grimm et al.,2013), facilitates better modeling terrestrial processes (Ruddellet al., 2016) and predicting the future of our Earth (Chen &
Zhang, 2013). However, this task is quite challenging, because the
ecological causes, processes, and performance of this kind of influences
are so complicated – involving so diverse climatic factors and so
complex interaction mechanisms (McGuire et al., 1992). This
reasoning is exemplified by the case of snow phenology (SP), which
involves periodic snow phenomena such as snow onset, snow end, and snow
cover duration (Peng et al., 2013). People found SP may
positively or negatively affect ecosystems’ performance (Lin & Hyyppä,
2019), through playing its ecological roles of adapting the chilling
effect (He et al., 2018), balancing subsurface water supply
(Schlaepeer et al., 2012), and keeping under-snow temperature
(Zeeman et al., 2017) for organisms living. Recent SP-relevant
ecological progresses reported that while in spring the timing of
snowmelt partly depends on temperature, snowmelt and temperature
sometimes work independently from one another to change species’
flowering timing (Steltzer et al., 2009); moreover, some species
seem to be approaching their extremes of phenological shifts in response
to snowmelt date rather than temperature (Iler et al., 2013).
Such knowledge, however, is in shortage for ecosystems at large scales,
as evidenced by the fact that the contemporarily popular dynamic global
vegetation models mostly just contain simple snow functional modules
(McCarthy et al., 2012; Murray, 2014; Yang et al., 2015).
This has restricted the models from fully simulating climate-biosphere
interactions and, eventually, from accurately projecting EP.
Consequently, the related kernel macrosystem ecological
(macroecological) (Heffernan et al., 2014) questions such as
“how may SP influence EP, particularly for those lower- and higher-EP
ecosystems?” have been extensively asked, and seeking their answers has
attracted a lot of attention in the fields of ecology, global carbon
cycles, and Earth interactions (Steltzer et al., 2009; Schlaepeeret al., 2012; Peng et al., 2013).
For the question, we further burst with a curiosity on its
commonly-assumed premise – “must this eco-effect be caused by the SP
in the same site exclusively?” or “may EP be more influenced by the SP
around?”. So far, no answers have been found, let alone for the special
cases of lower- and higher-EP ecosystems. The raising of such an odd
question stemmed from our re-examination of the first question by
analogy with the principle-approximate problems in the temporal field.
For example, for understanding how temperature controls leaf onsets (Wuet al., 2015) or snow cover accumulations (Lin & Jiang, 2017),
scientists were often devoted to deriving the temporal lengths of
“preseasons”, whose definition is the phase with the highest
correlation between the two feature series of a periodic event of
interest and its driver at the different phases preceding the
occurrences of that event (Lin & Jiang, 2017). The “preseason” here,
in effect, is analogous to the “distance” of the neighboring site that
acts with the greatest eco-effect. In such a sense, it is reasonable to
pose the second question. Actually, this analogy had its theoretical
foundation, i.e., the spatial gradients (this term already involving the
adjacent sites) of temperature tend to affect the dynamics of snow
accumulations (Yang et al., 2011), mechanically through
atmospheric circulations (Li et al., 2013; Ohara et al.,2014; Johnson & Ohara, 2018). Thereby, the scheme and strategy of
characterizing the time-lag eco-effect (Wu et al., 2015; Lin &
Jiang, 2017) seemingly can be transferred into the spatial field for
detecting the possible eco-effects from the neighboring sites. This
analogy is why the second question was posed. Altogether, the two
questions can be schematically shown in Fig. 1, and their solving can be
methodologically specified as a task of exploring the strongest
correlation between the interannual series of EP values at each site and
the related interannual series of its corresponding/surrounding
site-related snow-onset/end SP values. Completing this task, no doubt,
can enrich the ecological knowledge of SP-EP interactions, enhance snow
functional modules in dynamic global vegetation models (McCarthyet al., 2012; Murray, 2014; Yang et al., 2015), and,
further, improve the accuracies of climate-based EP predictions (Chenet al., 2013).
-Insert Fig. 1 here-
To answer the two questions, we innovatively proposed a new concept of
“periconnection”, by referring to the way of defining
“teleconnection” (Mikolajewicz et al., 1997) but also
expandingly exploring the “connections” to neighborhoods, for
reflecting the potential links between EP and its corresponding and
neighboring site-related SP. Teleconnection refers to climate anomalies
being related to each other at large distances (typically for thousands
of kilometers) (Allen & Luptowitz, 2017). The general initiative of
teleconnection studies is like first “hunting” (exploratory analysis)
statistically significant correlations, and then, varying methods of
correlation analysis are used to determine the existence and degrees of
teleconnections (Mamalakis et al., 2018). In fact, such a
conceptual reference to “teleconnection” has emerged in the field of
urban ecology (Seto et al., 2012), but no defined concepts in
this direction have been reported. We attempted to fill this gap, thru a
case study of exploring the macroecological links between the Arctic
circumpolar EP and the corresponding/surrounding site-related SP in the
Northern Hemisphere (>45°N) (NH).