DOI: 10.5061/dryad.q573n5tms
title: Meta-analysis of elevational changes in the intensity of trophic interactions: similarities and dissimilarities with latitudinal patterns

description: The premise that the intensity of biotic interactions decreases with
 increasing latitudes and elevations is broadly accepted; however, whether
 these geographical patterns can be explained within a common theoretical
 framework remains unclear. Our goal was to identify the general pattern of
 elevational changes in trophic interactions and to explore the sources of
 variation among the outcomes of individual studies. Meta-analysis of 226
 effect sizes calculated from 136 publications demonstrated a significant
 but interaction-specific decrease in the intensity of herbivory, carnivory
 and parasitism with increasing elevation. Nevertheless, this decrease was
 not significant at high latitudes and for interactions involving
 endothermic organisms, for herbivore outbreaks or for herbivores living
 within plant tissues. Herbivory similarly declined with increases in
 latitude and elevation, whereas carnivory showed a fivefold stronger
 decrease with elevation than with latitude and parasitism increased with
 latitude but decreased with elevation. Thus, although these gradients
 share a general pattern and several sources of variation in trophic
 interaction intensity, we discovered important dissimilarities, indicating
 that elevational and latitudinal changes in these interactions are partly
 driven by different factors. We conclude that the scope of the latitudinal
 biotic interaction hypothesis cannot be extended to incorporate
 elevational gradients.
description: <em>Search for and processing of
 studies</em> We focused our meta-analysis on trophic
 interactions in terrestrial ecosystems that are broadly defined as
 predation (i.e. as consumption of one organism by another organism) and
 classified into herbivory, carnivory and parasitism, as in previously
 conducted the meta-analysis of latitudinal changes in trophic interactions
 (Zvereva & Kozlov 2021). We extracted references from earlier reviews of
 elevational changes in trophic interactions (Hodkinson 2005; Andrew et al.
 2012; Péré et al. 2013; Sundqvist et al. 2013; Moreira et al. 2018;
 Carmona et al. 2011, 2020), and we then searched for additional
 publications in the ISI Web of Science using the keywords ‘elevation*’,
 ‘geographic*’, ‘biotic interactions’, ‘herbivor*’, ‘predat*’, ‘carnivor*’
 and ‘parasit*’. The search was completed on 11 January 2022. We did not
 use unpublished data or grey literature. To enable comparisons between elevational and
 latitudinal patterns, we followed the methodology used in our previous
 meta-analysis (Zvereva & Kozlov 2021). We considered studies
 containing direct quantitative estimates of the intensity of herbivory
 (the percentage of plant biomass or leaf area lost to herbivores or the
 proportion of damaged leaves, shoots, flowers, seeds or plants), carnivory
 (the mortality of prey or the predator attack rates) or parasitism
 (prevalence; i.e. the percentage of infected hosts). We did not include
 studies where the interaction intensity was deduced from the abundance of
 herbivores or predators, because abundance may show variable relationships
 with the intensity of their impact on plants and on prey (Bito et al.
 2011; Tela et al. 2021). We also excluded studies that did not contain
 data collected from individual elevational gradients but had quantified
 elevational changes in biotic interactions by combining data obtained in
 different mountain regions (e.g. Roslin et al.
 2017). We extracted information from studies that fit
 the following criteria: (i) the data were collected from natural
 ecosystems, (ii) the data were collected from at least two study sites
 with elevation differences of at least 100 m within the same mountain
 region, and (iii) the magnitude of the effect could be calculated from the
 data or statistics presented in the publication or provided by the
 authors. From multiyear
 studies, we extracted the combined result for all years if it was
 presented in the publication. If the data collected in different years
 were not combined by the authors, then we selected the year with the
 highest average value of the character under study. If the study employed
 some manipulations (e.g. enemy exclusion, insecticide treatment, water
 treatment), then we selected a control treatment.
 <em>Classificatory
 variables</em> Whenever possible, herbivores and carnivores
 were divided into ectotherms (invertebrates only; we found no data on
 carnivory by amphibians or reptiles) and endotherms (birds and mammals).
 All parasites were ectotherms; therefore, they were excluded from this
 comparison. We classified host plants and prey into natural, permanently
 inhabiting the study areas, and standardized, i.e. introduced to all study
 sites by the researchers (e.g. sunflower seeds or artificial
 prey). The herbivory level was considered to be
 background unless the authors explicitly mentioned an outbreak. Herbivory
 was divided into folivory (consumption of leaves, sometimes with their
 supporting branches) and granivory (i.e. seed predation). Folivory was
 divided into mammalian grazing and invertebrate folivory, whereas
 granivory was divided into pre- and post-dispersal seed predation.
 Invertebrate herbivores were divided into exophagous (defoliators and
 sap-feeders) and endophagous (miners, gallers, borers) feeders; the latter
 group also included pre-dispersal seed feeders.
 We divided studies of herbivory into those
 reporting elevational changes in herbivory on all plant species in a site
 or on several dominant species (community-wide herbivory hereafter) and
 those reporting herbivory in a certain plant species or genus
 (species-specific herbivory hereafter). Host plants were classified as
 woody or herbaceous, and woody plants were classified as evergreen (with
 foliage that remains green and functional through more than one growing
 season) or deciduous. Elevational gradients were classified as those
 located entirely below or above the tree line and those crossing the tree
 line. We calculated the elevational span of a gradient as the difference
 in elevation between the highest and lowest sites. The geographical
 coordinates of study areas (to the nearest degree of latitude and
 longitude) were extracted from publications or searched on the internet
 based on information provided in the publication. The attribution of elevational gradients to
 climate zones (tropical,
 including subtropics; temperate; and polar, including boreal forests) was
 based on climate and vegetation at the foot of the mountain and was
 performed in the same way as in the meta-analysis of latitudinal patterns
 (Zvereva & Kozlov 2021). <em>Meta-analysis</em>
 We quantified the strength of the elevational
 gradients by the <em>z</em>-transformed correlation between
 elevation and the intensity of the interactions
 (<em>z<sub>r</sub></em>). We estimated the
 variation in effect sizes (ES) within groups by calculating the
 heterogeneity index (<em>Q</em><sub>t</sub>). To
 compare ES among different groups of studies, we calculated the
 between-group heterogeneity
 (<em>Q</em><sub>B</sub>) using a random effects
 model, and we tested <em>Q</em><sub>B</sub>
 against the χ<sup>2</sup> distribution with the number of
 groups minus one degree of freedom (Koricheva et al.
 2013). We used three approaches for the ES calculation.
 When a study reported the data from two or three sites, we calculated
 Hedge's <em>d</em> based on data from the lowest and
 highest sites. We then converted Hedge's <em>d </em>into
 a Pearson linear correlation coefficient (<em>r</em>)
 following an equation given by Lajeunesse (2013) and calculated the
 variance of <em>z<sub>r</sub></em><sub> </sub>based on sample size (Rosenberg et al. 2013). When the number of sites was four or more, we extracted or calculated the Pearson linear correlation coefficient between the intensity of the trophic interaction and elevation and converted <em>r</em> into a <em>z</em><em><sub>r</sub></em><em> </em>value. If the authors provided the <em>F</em> statistic, we transformed it into <em>z</em><sub>r</sub> using a Metawin calculator (Rosenberg et al. 2000). The ES calculated in different ways were of similar magnitudes (<em>Q</em><sub>B</sub> = 1.09, df = 2, <em>P</em> = 0.58), thereby justifying the combination of ES calculated by these methods in our analyses. We explored the effects of latitude and gradient span on elevational changes in the intensity of trophic interactions by means of a meta-regression. We searched for publication bias by calculating the Kendall τ correlation between the standardised ES and sample size (Rosenberg et al. 2000); the significant correlation was interpreted as the presence of a small study effect hinting at publication bias (Jennions et al. 2013). Finally, we calculated Rosenthal’s fail-safe number, which shows the number of insignificant studies that are required to turn the significant mean ES into an insignificant one. The fail-safe numbers exceeding 5<em>n </em>(where <em>n </em>is the number of studies included in the meta-analysis) are considered as proof of robustness of the analysis against the insignificant results (Møller & Jennions 2001).