Research
OIKOS
Seed-dispersal networks respond differently to resource effects in open and forest habitats Maximilian G. R. Vollstädt, Stefan W. Ferger, Andreas Hemp, Kim M. Howell, Katrin Böhning-Gaese and Matthias Schleuning
M. G. R. Vollstädt (http://orcid.org/0000-0001-6209-59)(
[email protected]), S. W. Ferger, A. Hemp, K. Böhning-Gaese and M. Schleuning, Senckenberg Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, DE-60325 Frankfurt am Main, Germany. MGRV and KBG also at: Inst. for Ecology, Evolution and Diversity, Goethe Univ., Frankfurt am Main, Germany. AH also at: Dept of Plant Systematics, Univ. of Bayreuth, Bayreuth, Germany. – K. M. Howell, Dept of Zoology and Wildlife Conservation, Univ. of Dar es Salaam, Dar es Salaam, Tanzania.
Oikos 127: 847–854, 2017 doi: 10.1111/oik.04703
Subject Editor: Paulo Guimarães Jr. Editor-in-Chief: Dries Bonte. Accepted 14 November 2017
While patterns in species diversity have been well studied across large-scale environmental gradients, little is known about how species’ interaction networks change in response to abiotic and biotic factors across such gradients. Here we studied seeddispersal networks on 50 study plots distributed over ten different habitat types on the southern slopes of Mt Kilimanjaro, Tanzania, to disentangle the effects of climate, habitat structure, fruit diversity and fruit availability on different measures of interaction diversity. We used direct observations to record the interactions of frugivorous birds and mammals with fleshy-fruited plants and recorded climatic conditions, habitat structure, fruit diversity and availability. We found that Shannon interaction diversity (H) increased with fruit diversity and availability, whereas interaction evenness (EH) and network specialization (H2) responded differently to changes in fruit availability depending on habitat structure. The direction of the effects of fruit availability on EH and H2 differed between open habitats at the mountain base and structurally complex habitats in the forest belt. Our findings illustrate that interaction networks react differently to changes in environmental conditions in different ecosystems. Hence, our findings demonstrate that future projections of network structure and associated ecosystem functions need to account for habitat differences among ecosystems. Keywords: elevational gradient, interaction networks, land-use gradient.
Introduction Global patterns of species diversity along spatial and environmental gradients are well documented (Gaston 2000). For instance, many studies show a correlation of species richness with climatic conditions such as temperature and precipitation (Hawkins et al. 2003, Turner 2004, Peters et al. 2016). However, species do not occur alone, but interact in complex ways, such as in plant–animal interaction networks (Bascompte ––––––––––––––––––––––––––––––––––––––––
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2009, Tylianakis et al. 2010). These interaction networks are the backbone of biodiversity and contribute to the stability of ecological communities (Bascompte and Jordano 2007). The structure of these interaction networks and the underlying relationships between species from different trophic levels also influence the stability of important ecosystem functions (Dobson et al. 2006). Yet, spatial patterns in interaction networks have only rather recently been studied in more detail (Dalsgaard et al. 2011, 2013, Schleuning et al. 2012). Over the last decade, ecologists have developed a growing interest in interaction networks and their structural properties (Bascompte et al. 2006, Olesen et al. 2011, Trøjeslgaard and Olesen 2016). Different indices describe the structure of these networks and can be used to compare interaction networks between communities (Bersier et al. 2002, Blüthgen et al. 2006). The interaction diversity in ecological communities, for example, can be described by different components of diversity. First, Shannon diversity of interactions (H) is a measure of partner diversity in a community characterized by species from two trophic levels (Bersier et al. 2002). It is analogous to the Shannon diversity of species and tends to be correlated to species richness (Bersier et al. 2002). Second, interaction evenness (EH) is a measure of how evenly the interactions are distributed across the community (Tylianakis et al. 2007) and is more independent of species richness than Shannon diversity. A low degree of interaction evenness indicates the dominance of a few links between species, while most links are rare (Tylianakis et al. 2007). Interaction evenness has been shown to be positively related to the diversity and abundance of species from the lower trophic level (Tylianakis et al. 2007). Third, network specialization (H2) measures the level of niche partitioning and, consequently, the degree of resource overlap between the species of a community (Blüthgen et al. 2006). High degrees of network specialization indicate a low overlap of ecological niches between species, which corresponds to a low degree of ecological redundancy within the community (Blüthgen et al. 2006). The environmental factors that cause variation in network structure have mostly been studied in isolation (Plein et al. 2013, Saavedra et al. 2014), but no study has so far tested how different environmental drivers interact in their effects on network structure. It is widely recognized that climate is a main driver of global species diversity (Evans et al. 2005). Regions with higher and less variable temperatures usually show higher species diversity (Currie et al. 2004). Similarly, precipitation and water availability are positively associated with species diversity (Hawkins et al. 2003). More recent work has also shown relationships between climatic drivers and the structure of interaction networks. For instance, the degree of network specialization decreased with increasing temperature and plant diversity in a global analysis of seed-dispersal and pollination networks (Schleuning et al. 2012). On the other hand, the level of specialization increased with increasing precipitation in plant–hummingbird pollination networks (Dalsgaard et al. 2011). Yet, our knowledge on the relationships between climatic conditions and network structure remains sparse.
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In addition to climate, habitat structure influences species diversity (Hurlbert 2004). For birds, many studies show an increase in species diversity with more complex habitat structures, for example due to the availability of more ecological niches and nesting options in forested than in open habitats (MacArthur and Mac Arthur 1961). Hence, a decline of habitat structure often leads to decreasing species diversity (Pimm and Raven 2000, Fahrig 2003). A loss of habitat structure can occur naturally, e.g. through wildfires or storms (Smit et al. 2010), but can also be the consequence of human activities. Deforestation and selective logging can cause a decline of species diversity (Brook et al. 2003) and can also modify the structure of interaction networks (Brook et al. 2003, Sebastián-González et al. 2014). Another important driver of species diversity is the diversity and availability of resources, such as fruits (Mulwa et al. 2013, Ferger et al. 2014). Fruit resources are also important for the structure of interaction networks (Menke et al. 2012, Plein et al. 2013). So far, it has not been tested how climate, habitat structure and resources interact in their effects on the structure of interaction networks. Seed dispersal by animals is ideally suited to study patterns of interaction networks between plants and animals. It is a key ecological process, especially in the tropics where up to 90% of woody plant species are dispersed by animals (Şekercioğlu 2006). Moreover, tropical mountain systems are ideally suited for studies on patterns of interaction networks under different environmental conditions, since climatic conditions, habitat structure and resources vary greatly over small spatial extents across the elevational range (Sanders and Rahbek 2012, Ferger et al. 2014). Here we present the first study to simultaneously test how climate, habitat structure and fruit resources affect seeddispersal interaction networks across large environmental gradients. Specifically, we tested the following hypotheses: 1) first, if Shannon interaction diversity is correlated to species diversity, it should increase with an increasing fruit diversity and availability. 2) If interaction evenness in seed-dispersal networks depends on the diversity and abundance of species from the lower trophic level, it should exhibit similar patterns as Shannon interaction diversity. 3) As network specialization measures niche partitioning in ecological communities, it should decrease with increasing temperature and fruit diversity, consistent with a previous large-scale study (Schleuning et al. 2012). We expect this pattern because frugivorous birds tend to generalize their fruit diet and increase the overlap with other frugivores in tropical environments with high fruit diversity (Dalsgaard et al. 2017).
Methods Study area
The study was conducted on Mt Kilimanjaro in northern Tanzania (2°45–3°25S, 37°0–37°43E). Temperature on the mountain decreases linearly with increasing elevation
from 23.4°C at the foothills (Walter et al. 1975) to –7.1°C at the highest elevations (Thompson et al. 2002). Precipitation peaks with 2700 mm at an elevation of about 2200 m a.s.l., decreasing towards lower and higher elevations, respectively (Hemp 2005). Mt Kilimanjaro has several distinct bioclimatic belts, with a dry and hot savanna zone at low elevations and different forest types ranging from about 1000 m a.s.l. to 3100 m a.s.l. (Hemp 2005). On Mt Kilimanjaro, a network of 65 standardized study plots was established by the DFG Research Unit ‘KiLi – Kilimanjaro ecosystems under global change: linking biodiversity, biotic interactions and biogeochemical ecosystem processes’. Thirteen habitat types are represented by five spatial replicates along one of five transects on the southern slopes of the mountain. The minimum distance between two transects is 4.6 km and the minimum distance between two plots is 300 m (Supplementary material Appendix 6). For this study, we worked on 50 of the 65 plots, each with an area of 30 100 m, and excluded the fifteen plots in the alpine zones because no frugivore activity could be observed at these elevations. The study plots of this study cover four near-natural and six disturbed habitat types: natural savanna and maize field (870–1150 m a.s.l.), natural lower montane forest, Chagga homegarden, coffee plantation and grassland (1300–2020 m a.s.l.), natural and disturbed Ocotea forest (2100–2700 m a.s.l.), and natural and disturbed Podocarpus forest (2700–3000 m a.s.l.). For the visualization of interacting effects between different environmental drivers, we distinguish between open habitat types (savanna, maize field, coffee plantation, grassland; n = 20) and structurally diverse forest-like habitat types (natural lower montane forest, Chagga homegarden, natural and disturbed Ocotea forest, natural and disturbed Podocarpus forest; n = 30). Climate, habitat structure and fruit availability
To describe the climatic conditions on each plot, we recorded mean annual temperature and mean annual rainfall. These data were collected over 15 years through a network of temperature loggers (maximum – minimum thermometers; 0.1°C) and rain gauges (dipping bucket and funnel gauges, 1 mm) distributed on Mt Kilimanjaro (Hemp 2006). We additionally characterized habitat structure on each plot with an index of habitat complexity consisting of three individual measures. First, we measured maximum canopy height above ground, using a laser rangefinder. Second, we measured canopy closure as the mean percentage of closed cells from four spherical canopy densitometer readings. Third, we quantified vertical vegetation heterogeneity by estimating the vegetation cover in layers at 0, 1, 2, 4, 8, 16, 32 and 64 m above ground and calculated the Shannon–Wiener diversity index across all strata (Bibby et al. 2000). Each of the measurements was taken at the center of eight subplots per study plot (Ferger et al. 2014). We calculated the means of the three measures across the eight subplots per study plot. We then calculated a habitat structure index for each study plot as the resulting mean from these three measures
after each individual measure had been scaled to zero mean and unit variance. High values of the habitat structure index indicate a high habitat complexity, while low values indicate low habitat complexity. To assess fruit diversity and availability we recorded, mapped and identified all fruiting plant individuals on each plot. For each plant individual, we estimated the total number of ripe fruits. On plants with very large crop sizes, we counted the number of fruits for representative branches and used these to estimate the crop size of the whole plant. Fruit diversity on each plot was calculated as the Shannon index of fruits per species. Fruit availability on each plot was the sum of all crop sizes across all fruiting plant individuals and was log-transformed prior to analysis. Fruit diversity and fruit availability were only weakly and negatively correlated (n = 45 plots, Pearson correlation r = –0.34, p = 0.02). Seed-dispersal networks
We studied interactions between frugivorous birds and mammals and fruiting plants on all study plots between November 2013 and October 2015. To record frugivore interactions with fruiting plants, we observed frugivores with binoculars. The entire area of the plot was observed with equal attention. On each plot, frugivores were observed for 25 h distributed over four consecutive days. Observations were conducted for seven hours (hours 1–5 after sunrise, the 2 hours before sunset) on the first three days and for four hours on the last day (hours 1–4 after sunrise). Birds were identified using Zimmerman et al. (1999) and mammals were identified using Kingdon (1997). We recorded the visits of each fruit-eating animal on each fruiting plant and documented animal behavior. We distinguished between legitimate seed dispersal (i.e. swallowing fruit, carrying fruit away from plant) and illegitimate fruit handling (i.e. pecking at pulp without ingesting seeds, dropping fruit to the ground below the plant, preying upon seeds). For the network analysis, we summed the total number of visits of each frugivore species on each fruiting plant species per study plot. Only visits constituting legitimate seed dispersal were considered in the analysis. We used the frequency of interactions between frugivorous animals and fruiting plants to build a bipartite, weighted interaction matrix for each study plot (Bascompte et al. 2003). Columns corresponded to animal species and rows to plant species. Each cell in the matrix contained the total number of observed interaction events (i.e. the number of visits) between the respective pair of species. We had to exclude five plots since we did not observe sufficient interactions to build an interaction matrix for these plots (i.e. less than two species of both trophic levels). Hence, the final sample size for the analysis of interaction networks was 45 study plots. We estimated sampling completeness of seed-dispersal interactions on our study plots by investigating the accumulation of unique interaction pairs (i.e. link richness) between frugivorous birds and fleshy-fruited plants (Chacoff et al.
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2012). We used a sample-based rarefaction method to calculate plot-specific accumulation curves and computed means and the corresponding standard deviations across plots of the same habitat type (Supplementary material Appendix 7). In order to account for variability in frugivore activity per plot, link richness and the number of observed interaction events were standardized by the respective plot maximum. In all habitat types, accumulation curves showed a clearly saturating trend, indicating an almost complete sampling (Supplementary material Appendix 7). Overall, more than 80% of unique links between species were recorded already after about 50% of the interaction events had been observed. Network indices, model fitting and model selection
We calculated three indices of network structure that are all derived from Shannon entropy, i.e. Shannon interaction diversity (H, Bersier et al. 2002), Shannon interaction evenness (EH, Sahli and Conner 2006) and network specialization (H2, Blüthgen et al. 2006). We calculated H2 for a subset of 37 study plots because eight communities only comprised two or less species for one or both trophic levels. Network size (i.e. species richness of plants and birds) was related to Shannon interaction diversity (n = 45 plots, Pearson correlation r = 0.90, p 0.01), but was unrelated to interaction evenness (n = 45 plots, Pearson correlation r = 0.09, p = 0.52) and network specialization (n = 37 plots, Pearson correlation r = –0.14, p = 0.39). We fitted separate linear models (LM) to test main and interaction effects of climate (temperature, precipitation), habitat structure, fruit diversity and fruit availability (logtransformed) on H, EH and H2, respectively. To this end, we first constructed a global LM containing all possible combinations of predictor variables, including the two-way interaction terms of all predictor variables, plus the main effect of fruit diversity. From all possible model combinations nested within the global model, we selected the best-fit LMs defined by a ΔAICc value smaller than 2 and performed model averaging across this subset of best models (Burnham and Anderson 1998, Supplementary material Appendix 3). We additionally calculated the relative variable importance for this subset of best models, corresponding to the sum of Akaike weights for all models including the respective predictor variable (Burnham and Anderson 1998). All statistical analyses were performed with R ver. 3.1.0 (www.rproject.org), using the packages ‘bipartite’ (Dorman et al. 2009), ‘lme4’ (Bates et al. 2015) and ‘MuMIn’ (Barton 2013). Data deposition
Metadata of all datasets is available online: www. kilimanjaro.biozentrum.uni-wuerzburg.de/Data/Data. aspx . Interaction data available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.dn7hv (Vollstädt et al. 2017).
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Results We covered a wide climatic gradient across the 50 study plots (mean annual temperature: 9–21.6°C; mean annual precipitation: 609–2653 mm; Supplementary material Appendix 1–2). Temperature declined linearly with elevation, whereas precipitation showed a unimodal distribution along the elevational gradient (Supplementary material Appendix 1–2). Habitat structure (ranging from open habitats to closed forest habitats), fruit availability and fruit diversity (Supplementary material Appendix 1–2) also varied greatly among plots (Supplementary material Appendix 1–2). Overall, we recorded 5300 visits, constituting legitimate seed-dispersal events between 91 frugivore species (86 birds, 5 mammals; Supplementary material Appendix 5) and 71 plant species (Supplementary material Appendix 4). Plant species richness varied from 1–15 species (median: 5) and animal species richness from 2–24 species (median: 9) per interaction network. Network metrics
We found an increase of Shannon interaction diversity with both fruit diversity (H fruits) and fruit availability (N fruits [log-transformed], Table 1), whereas climatic variables were unrelated to Shannon interaction diversity (Supplementary material Appendix 3). The positive effect of resources on Shannon interaction diversity was consistent for both open and closed habitats (Fig. 1a–b). In contrast, habitat structure and fruit availability interacted in their effect on interaction evenness, while climate had also no significant effect on this measure (Table 1, Supplementary material Appendix 3). In open habitats, interaction evenness decreased with increasing fruit availability, whereas on forested plots with high habitat structure this relationship was positive (Fig. 2a). We also found an interacting effect of habitat structure and fruit availability on network specialization (Table 1, Supplementary material Appendix 3). In open habitats with low habitat structure, network specialization increased with increasing fruit availability, whereas the relationship was negative in forested plots with high habitat structure (Fig. 2b). In addition, network specialization consistently increased with increasing temperature (Table 1).
Discussion This is the first study to simultaneously test effects of climate, habitat structure, fruit diversity and availability on the structure of mutualistic plant–animal interaction networks across a large environmental gradient. Overall, habitat structure, fruit diversity and availability influence the structure of seeddispersal networks more strongly than climatic factors. Interestingly, we found interacting effects of fruit availability and habitat structure on interaction evenness and network specialization. Hence, the specific effects of resource availability on network structure depended on the habitat context.
Table 1. Summary of the effects of climate (temperature and precipitation), habitat structure, fruit diversity (H fruits) and fruit availability (N fruits, log-transformed) on the properties of seed dispersal networks on Mt Kilimanjaro. Given are the model estimates, their significance and relative variable importance. Shown are full model averages for (a) interaction diversity (n = 45), (b) interaction evenness (n = 45) and (c) network specialization (n = 37) for all models with ΔAICc 2; in the case of (c), estimates are shown for the single best model. All predictor variables were scaled to zero mean and unit variance. ß (a) Intercept H fruits N fruits (log) Temperature Precipitation N fruits (log) temperature (b) Intercept Habitat N fruits (log) H fruits N fruits (log) habitat (c) Intercept Temperature Habitat N fruits (log) N fruits (log) habitat
SE
z/t
p-value Importance
2.060 0.649 0.469 0.076 –0.015 –0.023
0.088 22.655 0.001 0.097 6.508 0.001 0.096 4.755 0.001 0.096 0.780 0.435 0.052 0.282 0.778 0.064 0.354 0.723
1.00 1.00 0.54 0.15 0.20
0.553 –0.051 –0.006 0.017 0.074
0.018 29.759 0.001 0.019 2.542 0.011 0.020 0.271 0.786 0.021 0.817 0.414 0.027 2.677 0.007
1.00 1.00 0.55 1.00
0.489 0.109 0.108 –0.027 –0.119
0.031 15.726 0.001 0.039 2.773 0.009 0.039 2.740 0.009 0.032 –0.849 0.402 0.047 –2.533 0.016
1.00 1.00 1.00 1.00
Overall, the degree of Shannon interaction diversity (H) was highest on plots with high fruit diversity as well as on plots with high fruit availability. It has been shown that species richness can be more closely related to resource availability than to climatic drivers or habitat structure (Loiselle and Blake 1991) which is particularly true for frugivorous birds (Kissling et al. 2012). In fact, a previous study on Mt Kilimanjaro showed that effects of precipitation and habitat structure on avian frugivore richness were mediated by fruit availability (Ferger et al. 2014). Since Shannon interaction diversity is a measure of the diversity of realized interactions within a network of interdependent species, the metric is expected to correlate to species diversity (Bersier et al. 2002, Plein et al. 2013). Here we show a consistent, positive effect of both fruit diversity and fruit availability on Shannon interaction diversity in seed-dispersal networks. Interestingly, fruit diversity and fruit availability had additive and independent effects on Shannon interaction diversity. These findings suggest that bottom–up effects of resources may indeed drive the diversity of seed-dispersal interactions, since our results also indicate that, even when the diversity of resources is low, a high abundance of resources still attracts and supports a large number of frugivores resulting in high interaction diversity. For instance, disturbed habitats at the mountain
Figure 1. Relationship between Shannon interaction diversity and (a) fruit diversity and (b) fruit availability for open habitats (habitat complexity = 0 to –1 SD, circles) and forest habitats (habitat complexity = 0 to 1 SD, triangles). Fruit diversity is calculated as the Shannon index of fruits per plant species on each study plot, while fruit availability is calculated as the total number of fruits (logtransformed) on each study plot. The relationship did not differ between habitat types and is thus only described with one, solid trend line. Data points correspond to the fitted values plus model residuals from the model averages presented in Table 1a. Sample size: n = 45 study plots.
base of Mt Kilimanjaro are characterized by a rather low diversity of fleshy-fruited plants, but specific plants can provide abundant fruit resources (Supplementary material Appendix 2). We found interesting differences between the major environmental drivers of species diversity and interaction diversity on Mt Kilimanjaro. Peters et al. (2016) found a strong and unambiguous link between temperature and species richness, especially for different animal taxa on Mt Kilimanjaro, whereas we found no effect of climate on Shannon interaction diversity. The patterns of interaction diversity in seed-dispersal networks, thus, indicate that the mechanisms shaping the structure of mutualistic interaction networks may be different from those shaping animal diversity, due to bottom–up effects of resource diversity and availability on interaction diversity. As expected, the evenness of interactions (EH) in seeddispersal networks was related to habitat structure and fruit availability, but was independent of climatic conditions. Both biotic drivers interacted in their effects, i.e. fruit availability led either to a decline or an increase of interaction evenness, depending on the habitat context. A low degree of interaction
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Figure 2. Habitat–contingent relationships between fruit availability and (a) interaction evenness and (b) network specialization in seed dispersal networks on Mt Kilimanjaro. Trend lines show the predicted effects of fruit availability on interaction metrics for open habitats (habitat complexity = 0 to -1 SD, circles) and forest habitats (habitat complexity = 0 to 1 SD, triangles). Data points correspond to the fitted values plus residuals from the model averages presented in Table 1b and 1c, respectively. Sample size for interaction evenness was n = 45 study plots, and for network specialization n = 37 study plots.
evenness suggests a dominance of some links in a community of interacting species, whereas most other links are rare (Tylianakis et al. 2007). Earlier studies on mutualistic interaction networks showed little or no difference in the degree of interaction evenness in seed-dispersal networks among different communities (Schleuning et al. 2014), which suggests that interaction evenness may be robust against variation in plant and animal communities (Plein et al. 2013). However, in a study on host–parasitoid-networks, the level of interaction evenness was lower in communities in modified habitats, which was explained by a high abundance of a few species from the low trophic level and a change in species’ behavior at the high trophic level (Tylianakis et al. 2007). Here, we find that the degree of interaction evenness in seed-dispersal networks changed along a gradient of fruit availability, contingent on habitat structure. For plots with a low habitat complexity, we found a negative association of interaction evenness with fruit availability. On Mt Kilimanjaro, plots with low habitat structure are usually modified by human activities. For instance, human impact fosters specific fruiting plants, such as Ficus spp., which have large crop sizes and are keystone fruit resources for many frugivores (Kirika et al. 2008). Consequently, seed-dispersal interactions were not distributed evenly across resources on these plots, but links
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with abundant crops dominated the interaction networks. In contrast, on forest plots with a complex habitat structure and high fruit availability, the resources were distributed more evenly across species, which resulted in a more even distribution of interactions in these communities. In forest plots at high elevations, fruit availability is reduced, which resulted in a decrease in interaction evenness. Our results, thus, show how changes in resource availability, in response to human disturbance and along natural gradients, trigger bottom–up effects on the structure of mutualistic interaction networks. The level of network specialization (H2) was related in similar ways to habitat structure and fruit availability as interaction evenness, given that a high degree of specialization corresponds to a low degree of interaction evenness. Depending on the surrounding habitat structure, network specialization either increased or decreased with fruit availability. The metric of network specialization depends on both the diversity and the distribution of interactions across species and measures the level of niche partitioning in a community, which reflects the degree of resource overlap between species (Blüthgen et al. 2006). The interacting effects of habitat structure and fruit availability underpin how open habitats and forest ecosystems differ in their ecological properties and in their interacting species communities. A previous global analysis of mutualistic interaction networks showed that an increasing diversity of plants resulted in more resource overlap among animals and, thus, a decrease of specialization within the networks (Schleuning et al. 2012). In a local study from Kenya, plant–frugivore networks were more specialized in the forest interior, where fruit availability was lower than at the forest edge (Menke et al. 2012). The authors explained this pattern by a higher flexibility in avian fruit choices at forest edges with high resource availability, which resulted in more niche overlap among frugivores (Menke et al. 2012). In difference to this study, we here show that on plots with high fruit availability and little habitat structure network specialization was high. This pattern was probably driven by a few abundant crops in the open habitat types. This finding is consistent with the global meta-analysis that found that network specialization increased with decreasing resource availability at high latitudes (Schleuning et al. 2012). Patterns of increasing specialization with decreasing resource availability can be explained by changes in frugivore behavior and frugivore specialization on specific, predictable resources (Ramos-Robles et al. 2016, Dalsgaard et al. 2017). The relationship was reversed on plots with high habitat structure. On forest plots, network specialization slightly decreased with increasing fruit availability, likely because few crop species dominated the forests at high elevations. In addition to the interacting effects of fruit availability and habitat structure, we found a consistently positive relationship between temperature and network specialization. This relationship contrasts to earlier findings at the global scale, where network specialization was highest in areas with low temperatures (Schleuning et al. 2012). An explanation for the distinct pattern at Mt Kilimanjaro could be that many of the plots with
the highest temperatures are located at low elevations where human disturbance is most pronounced. On these plots, it is likely that human disturbance leads to a decreased diversity of plants which reduces the potential for resource sharing and niche overlap among frugivores. An additional explanation could be that higher frugivore diversity with increasing temperature (Peters et al. 2016) requires the consumer species to partition the limited fruit resources at the hot mountain base. Niche partitioning could be facilitated by long-distance movements of large frugivores, such as hornbills, to specific resource plants in such landscapes (Lenz et al. 2011). Conclusions
We show that different measures of interaction diversity in seed-dispersal networks reacted differently to gradients in abiotic and biotic conditions. Bottom–up effects of resources, especially of resource availability, seem to be the strongest driver of network structure. Interestingly, interaction evenness and network specialization responded differently to changes in resource availability, contingent on the habitat context. These findings illustrate that interaction networks react differently to changes in environmental conditions in different ecosystems. Hence, impacts of global change on network structure and associated ecosystem functions are likely to depend on habitat differences between ecosystems. Acknowledgements – Samuel Augustino, Jeremia Issack, Nelson Massam, Ramson Mmary, Alexander Neu, George Philipo and Raymond Zaria helped with field work. COSTECH, TAWIRI, TANAPA and the Immigration Office of Moshi permitted MGRV and SWF to conduct field work in Tanzania and inside Kilimanjaro National Park. Funding – This work was funded by the Deutsche Forschungsgemeinschaft (FOR 1246) and by LOEWE – LandesOffensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz of Hesse’s Ministry of Higher Education, Research, and the Arts. Author contributions – MGRV, KBG and MS designed the study. MGRV collected all interaction data on Mt Kilimanjaro. SWF and AH contributed data on vegetation structure and climate. MGRV analyzed the data with input from MS. MGRV wrote the first full draft of the manuscript and SWF, AH, KMH, KBG and MS commented on the manuscript.
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Supplementary material (available online as Appendix oik-04703 at www.oikosjournal.org/appendix/oik-04703 ). Appendix 1: overview of the variables included in the analyses (mean, standard deviation, minimum and maximum). Appendix 2: list of study plots considered in the analysis and the respective data for abiotic and biotic factors plus network metrics. Appendix 3: list of models with ΔAICc 2 for all network metrics considered in the study. Appendix 4: list of plant species, their number of interaction events and their elevational ranges. Appendix 5: list of bird species, their number of interaction events and their elevational ranges. Appendix 6: map of the study plots. Appendix 7: accumulation curves of link richness for all habitat types.
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