Birds and Berries: Projecting the Responses of Seed Dispersal Networks to Climate Change Manette E

Home , Amelanchier alnifolia, Arctostaphylos glauca, Arctostaphylos mewukka, Arctostaphylos nissenana, Cornus sessilis, Gaultheria humifusa

University of Connecticut OpenCommons@UConn

Doctoral Dissertations University of Connecticut Graduate School

5-31-2018 Birds and Berries: Projecting the Responses of Seed Dispersal Networks to Climate Change Manette E. Sandor University of Connecticut, [email protected]

Follow this and additional works at: https://opencommons.uconn.edu/dissertations

Recommended Citation Sandor, Manette E., "Birds and Berries: Projecting the Responses of Seed Dispersal Networks to Climate Change" (2018). Doctoral Dissertations. 1803. https://opencommons.uconn.edu/dissertations/1803 Birds and Berries: Projecting the Responses of Seed Dispersal Networks to Climate Change

Manette Eleasa Sandor, PhD

University of Connecticut, 2018

Climate change has caused species range shifts and altered timing of life events, with more predicted in coming decades. Range shifts could result in secondary threats such as spatial mismatch with mutualist partners and movement out of protected areas. We found that species richness was lost at higher elevations with range shifts, and species turnover peaked at middle elevations. Areas of bird species turnover only partially overlap with areas of shrub species turnover, which could result in broken interactions between partners. No shrub species were extirpated, but a third spanned less than 15% of protected areas. Our findings add to growing evidence that currently protected species lose protection as they shift their ranges with changing climate.

The species-area relationship is a foundational idea in ecology and conservation biology which has been used to predict the number of species lost with habitat destruction and climate change, and we extend it to biotic interactions. We present theory for how interactions scale with space and provide mathematical relationships with the species-area curve. Our interactions-area curve accounts for connectance, a measure of interactions per species within a network. We find that the interactions-area curve from an empirical seed dispersal network fits our theoretical equation.

Habitat loss can result in species loss from a mutualist network, causing additional species to become secondarily disconnected from the network. The number of resulting disconnected species from habitat loss is poorly known. We simulated a null model network with random species loss according to the species-area relationship, and enumerated the disconnected species, varying both the number of species within the network and connectance. Our network

Manette Eleasa Sandor – University of Connecticut, 2018

simulations show more species detached at lower connectivity and with greater disparity in the species richness of the two mutualist groups. Our empirical example also displayed a wide range of outcomes: 0-5 species/10 km2 of simulated habitat loss. As available habitat is lost to land use conversion and climate change, the community level repercussions are greater than predicted by simple species loss, but there is considerable uncertainty in how severe these repercussions will be, from minimal to catastrophic.

Birds and Berries: Projecting the Responses of Seed Dispersal Networks to Climate Change

Manette Eleasa Sandor

A.B., Vassar College, 2004

M.S., University of Connecticut, 2013

A Dissertation

Submitted in Partial Fulfillment of the

Requirements for the Degree of

Doctor of Philosophy

at the

University of Connecticut

2018

Manette Eleasa Sandor

2018 ii

APPROVAL PAGE

Doctor of Philosophy Dissertation

Birds and Berries: Projecting the Responses of Seed Dispersal Networks to Climate Change

Presented by Manette Eleasa Sandor, A.B., M.S.

Co-Major Advisor ______Chris S. Elphick

Co-Major Advisor ______Morgan W. Tingley

Associate Advisor ______John A. Silander, Jr.

Associate Advisor ______Cynthia S. Jones

Associate Advisor ______Elizabeth L. Jockusch

University of Connecticut 2018 iii

Acknowledgements

I thank the funding sources that made this research possible: Lewis and Clark Fund for

Exploration and Research, Ronald Bamford Fund in the Department of Ecology and Evolutionary

Biology, Center for Conservation and Biodiversity at the University of Connecticut, University of

California Eastern Sierra Nevada Student Research Award, Audubon Connecticut Important Bird Area

Small Matching Grants Program, and Ecology and Evolutionary Biology Zoology Award. I thank the following people and organizations for collecting fecal samples for this research: Rodney Siegel, Lauren

Helton, and others at the Institute for Bird Populations; Ryan Burnett, Lauren Morgan-Outhisack, Brent

Campos, and others at Point Blue Conservation Science and the Gurnsey Creek MAPS station; Denise

LaBerteaux and colleagues at the Cranesbrake Ecological Reserve John Brokaw at the Perazzo Meadow

MAPS station; Faerthen Felix and Walter Clevenger at the Sagehen Creek Field Station (University of

California Natural Reserve System); and Michael Magnuson at Drakesbad National Park. I would also like to thank Yosemite National Park, Sierra National Forest, Stanislaus National Forest, Inyo National

Forest, El Dorado National Forest, Toiyabe National Forest, and the University of California Valentine

Eastern Sierra Reserve for allowing us to survey plants on their lands. I thank Steven W Cardiff and

James V Remsen for their assistance with Louisiana State University Museum of Natural Science’s extensive collections. I thank Diana Stralberg, Dennis Jongsomjit, and Point Blue Conservation Science for providing rasters of bird species turnover and suitability for California. I thank the Computational

Biology Core, Institute for Systems Genomics, University of Connecticut for use of their High

Performance Computing facilities and support, particularly J Wegrzyn, V Singh, M Wilson, and S King. I thank G Wang for advice regarding global climate models and C Merow and R Bagchi for their helpful discussions with me about the statistics of species distribution models. Finally, I thank A Edwards, J

Riley, I Gibson, A Mueller, K Wrensford, B Nahorney, D Zandbergen, and E Zhao, for assistance in the field and lab.

Table of Contents Acknowledgements……………………………………………………………………………...…………iv Ch. 1: Projected range shifts due to climate change likely to alter species interactions and conservation protection in California’s Sierra Nevada mountains…………………………………..…………………….1 Ch. 2: Spatial scaling of network diversity………………………………………………………………....29 Ch. 3: Species-area relationships can be used to predict knock-on species losses………………………….48 Appendix A: Supporting Information for Ch. 1…………………………………………………….…...…70 Appendix B: Supporting Information for Ch. 2……………………………………………………….…...86 Appendix C: Supporting Information for Ch. 3…………………………………………………….……...93 References………………………………………………………………………………………………..136

Projected range shifts due to climate change likely to alter species interactions and conservation protection in California’s Sierra Nevada mountains

Introduction

Climate change has already caused many species to shift ranges and the timing of key life events (Parmesan and Yohe 2003, Kelly and Goulden 2008, Forister et al. 2010, Tingley et al.

2012). These effects are predicted to continue in coming decades (Sinervo et al. 2010, Bellard et al. 2012, Moritz and Agudo 2013). In Mediterranean climates, shrubs often dominate the vegetation, and in forested systems they can play an important role in forest regeneration by acting as nurse plants that reduce water loss and provide shade for tree seedlings (Gomez-

Aparicio et al. 2004, Gray et al. 2005). Shrubs are especially prevalent in early stages of forest succession in disturbance-prone systems, and early successional forest supports high biodiversity driven by high structural heterogeneity, large nutrient fluxes, and high productivity (Swanson et al. 2011). Despite the key role that shrubs play in both shrubland and forest ecosystems, research on how shrub species will respond to climate change has been largely skewed towards phenological shifts (Richardson and O’Keefe 2009) and woody encroachment into tundra (e.g.

Sturm 2001, Walker et al. 2006) and grasslands (e.g. Brown et al. 1997, Hibbard et al. 2003), with some attention to species loss in the face of climate change (e.g. Slingsby et al. 2017). We focus here on the effects of climate change on fleshy-fruited shrub diversity.

Range shifts allow species to continue to exist within a specific climate envelope, but these range shifts could result in secondary threats to species persistence. Fleshy-fruited shrub species participate in mutualistic relationships with animals, particularly birds. Shrubs rely on these species to disperse their seeds away from the parent plant or to better habitat (Howe and

Miriti 2004). In the face of climate change, shrubs depend on birds to move seeds to areas with a

1 more conducive climate (Clark et al. 1999, McEuen and Curran 2004, Loarie et al. 2009). In return, shrubs provide birds with food, especially during the post-breeding and fall migration periods (Thompson and Willson 1979, Howe and Smallwood 1982, Herrera 1984). If frugivorous birds and fleshy-fruited shrubs are required to shift their ranges in different directions to stay within their preferred climate envelope, those shrubs that depend on birds could be unable to move. If they do manage to shift their ranges, or simply die out in inhospitable areas, then food could become scarce within the birds’ preferred climate envelopes (Kissling et al. 2007,

Schweiger et al. 2008). We address this issue of spatial mismatch in a montane system in which fleshy-fruited shrubs are prevalent. We compare our projected shrub species distributions with projected bird distributions that have been published previously.

Another secondary threat to shrubs that undergo range shifts is that they might move out of protected areas (Araujo et al. 2011, Bagchi et al. 2012). For example, many bird species that are currently protected by national parks in the USA are projected to be extirpated from these areas by 2070 (Wu et al. 2018). Protected areas designed specifically to encompass high species diversity could lose some of that diversity to range shifts (Peters and Darling 1985, Halpin 1997,

Lemes et al. 2014). We determine how shifts in the ranges of our shrub species affects their extent within currently protected land.

Two primary types of data exist for modeling species distributions: presence-absence data (which are largely from planned surveys) and presence-only data (from opportunistic surveys). Presence-absence data provide a better estimate of a species’ true relationship with environmental parameters (Fithian et al. 2015), but they can be expensive to collect and so are typically limited geographically. Presence-only data (e.g., from herbaria) are more readily available, but models based solely on these data are limited because they do not produce an

2 absolute probability of presence, only a relative or conditional one, and because they lack a systematic survey design, creating potential for bias (Phillips et al. 2009, Chakraborty et al.

2011, Dorazio et al. 2014, Fithian et al. 2015). We used a modified version of a recently- published model that combines presence-only and presence-absence data. By combining these modeling methods, the power of the presence-only data improves the precision of the presence- absence model (Fithian et al. 2015, Merow et al. 2016), especially for rare species (Giraud et al.

2016), and it allows for greater identifiability of parameters (Dorazio et al. 2014).

We used this combined presence-absence, presence-only model to fit and project distributions of fleshy-fruited shrubs in two future climate scenarios. We use these projections to address the following questions: How will shrub alpha richness be altered by climate change?

How will future shrub communities compare to current shrub communities? We then explore two threats that shrubs could face as a result of these range shifts: 1. spatial mismatch with their dispersal agents, and 2. moving out of protected areas. We compare shrub species distributions to previously-published bird species distributions (Stralberg et al. 2009) to determine how our projected changes to shrub species richness and turnover match similar projections for birds. We also compare the range shift of the three most frugivorous bird species within our study region to changes in shrub species richness at different elevations to determine whether or not these three frugivores are shifting in the same direction as shrub species richness. Finally, we test whether land protection for our target shrub species declines as a result of their range shifts under the assumption of static protected areas.

Methods

Study site

3 The Sierra Nevada are part of the California Floristic Province, a biodiversity hot spot due to a high rate of plant diversity and endemics (Myers et al. 2000, Kraft et al. 2010).

Undeveloped land at lower elevations within the Sierra Nevada is dominated by shrubs, and the higher elevation forests have a diverse shrub community (North et al. 2005). The Sierra Nevada are well protected at mid- to high elevations with numerous national forests, and three national parks including Yosemite National Park, one of the largest parks within the continental USA.

Our study site encompasses the central Sierra Nevada, an area covering approximately 2,337,005 ha, including all of Yosemite National Park. We defined this region as the area within and around Yosemite National Park as well as within Stanislaus, Eldorado, and Toiyabe National

Forests.

Data collection

We chose survey points for our presence-absence data following a generalized random tessellation stratified (GRTS) design because it results in a spatially-balanced random sample

(Stevens and Olsen 2004). We laid a 2 km2 hexagonal grid over the central Sierra Nevada. We then selected 34 hexagons from this grid and drew 20 evenly distributed survey plots within each hexagon using the R package “spsurvey”, which implements GRTS sampling (Kincaid and

Olsen 2013). From the selected hexagons, we discarded any that were inaccessible because of prohibitively steep slopes or permanent glaciers as well as any that were more than 10 km from a paved or US Forest Service road. During July and August of 2014 and 2015, we collected presence-absence data at these pre-selected locations across the central Sierra Nevada region. We surveyed 2-5 survey plots per hexagon, for a total of 140 survey plots. The first survey plot per hexagon was randomly selected, and others were randomly selected from the subset of 20 that

4 were 0.5-1 km away from the original survey plot. Each plot was 30 m in diameter and circular.

Within the circle, we thoroughly surveyed all fleshy-fruited shrubs, recording the presence of any species found.

To obtain presence-only data, we downloaded species occurrences from the Global

Biodiversity Information Facility (GBIF; gbif.org) for all fleshy-fruited shrub and small tree species that occur within the Sierra Nevada for the years 2014 and 2015, the same years as our presence-absence surveys, using the “dismo” R package (Hijmans et al. 2017). We queried by scientific species names so as to download all subspecies, varieties, and synonyms of any given species. We discarded all entries with a missing latitude, longitude, or geodetic datum field. We also discarded all entries where latitude or longitude was rounded to a full degree (Yesson et al.

2007). We further reduced our species list to only those species found in our presence-absence surveys (Table 1) and restricted all occurrences to a box bounded by 37.3° – 38.8° N and 117.5°

– 121.5° W.

Species distribution model

To derive species-specific distribution models of shrub species in the central Sierra

Nevada, we modified a combined presence-absence and presence-only model presented by

Fithian et al. (2015; see also Dorazio et al. 2014) to a Bayesian framework. This model uses an inhomogenous Poisson process for the presence-only portion, with background, or “pseudo- absence,” points as the centroids (quadrature points) of a regular grid (quadratures) (Renner et al.

2015). We modeled multiple species together, but to reduce computational load we split our 30 species into four groups of seven or eight species each (rationale for species groupings described below; Table 1). The model explicitly describes any spatial aggregation in the sampling of the

5 presence-only data by fitting bias covariates alongside the environmental covariates (Dorazio et al. 2014, Fithian et al. 2015, Giraud et al. 2016). Sampling bias covariates, unlike environmental covariates which are fit separately for each species, are assumed to have the same effects on all species in the model. Because they only explain variation in sampling and not in the actual distribution of the species, these bias covariates are not used in the species projections.

Model Structure

In this combined presence-absence and presence-only model, the presence-absence data are modeled by a Bernoulli distribution with a logit link where,

logit&'())+ = . + 01 ∗ 31()) + 04 ∗ 34()) + ⋯

for all b’s, p(s) describes the species intensity (similar to ‘suitability’) and is the mean of the

Bernoulli distribution, a is the intercept term for the species intensity, and bi*xi(s) describes all environmental covariates (xi(s)) and associated slopes (bi) at observation location s.

Because we used presence-only data collected without a unified and systematic sampling design, we explicitly modelled the potential bias inherent in this type of dataset. To do this, we used a thinned inhomogeneous Poisson process model with log link where,

( ) ( ) ( ) ( ) ( ) log 6()) = a + b1 ∗ 31 ) + b4 ∗ 34 ) + … + g + d1 ∗ 81 ) + d4 ∗ 84 ) + ⋯

for each b and d, l(s) is the product of p(s) and b(s) (the sampling bias function), g is the intercept term for the bias function, and the di*zi(s) terms represent the bias covariates (zi(s)) and

6 associated slopes (di) at location s. The d terms are chosen to model aggregations of presence- only data caused by ease of access instead of the species distribution. These terms are fit within the model alongside the environmental covariates, but when the species distributions are projected into current or future environmental space, the bias covariates are not used. We implemented our full model in a Bayesian framework, where our data were described by a weighted Poisson distribution with the mean and variance as l(s).

The presence-only data were described by a Poisson distribution that is the product of l(s) and a weight at each s, hence a weighted Poisson distribution. Most presence-only models require background points, also sometimes referred to as “pseudo-absence” points, against which the presence points can be compared. One approach to the selection of background points is through the use of quadrature points, the centroids of a regular grid (quadratures) over which the intensity function is evaluated (cf. Renner et al. 2015). It is this quadrature approach to background points that requires weights for all quadrature points and all presence points. We used 10,700 quadrature points over an area of 32,661 km2 and weighted each of them as the total area (divided by 3.5) divided by the number of quadrature points (0.87) (cf. Renner et al. 2015).

All presence points were weighted using a value of 0.000001 as per Renner et al. (2015).

We implemented our model in a partial multispecies framework, where the terms a and b1, b2, … bi were determined separately for each species, whereas d1, d2, … di were determined for all species together (Fithian et al. 2015). The intercept terms a and g are not identifiable separately (Fithian et al. 2015) so we collapsed these terms to a single intercept for each species, arising from a normal distribution. The terms d1, d2, …di are not differentiated among species because the model assumes that the bias covariates 1) affect each species with the same relationship and 2) reflect the behavior of people rather than the locations of the organisms being

7 studied (Fithian et al. 2015). The terms d1, d2, …di also arise from a normal distribution. All terms described by normal distributions were given vague priors with a mean of 0 and standard deviation of 0.001. The terms a, b1, b2, … bi were estimated for both the presence-absence and presence-only portions of the model simultaneously. All four multispecies models were fit in

JAGS (Plummer 2003) via the R package ‘R2jags’ (Su and Yajima 2015). Full model code in the JAGS language (Plummer 2003), including prior specification, is detailed in Appendix A.

We ran each multispecies model with three chains for 30,000 iterations. We checked for convergence by ensuring the scale reduction factors approached one for all parameters (Gelman and Rubin 1992).

Environmental and bias covariates

We fit distribution models using a variety of relevant climate variables. We used climate data from the Geophysical Fluid Dynamics Laboratory’s (GFDL) Earth System Model (ESM)

2M because it had close agreements to observations in the historical period and low error (Figs.

9.8 and 9.7 in Flato et al. 2013), while also incorporating many of the Atmosphere-Ocean

General Circulation Model and ESM variables (Flato et al. 2013, Table 9.1).

We downloaded daily downscaled climate data (precipitation, minimum air temperature, and maximum air temperature) using model GFDL-ESM2M for historical (1950-2005) and future (2006-2099) time scales under two future scenarios: Representative Concentration

Pathway 4.5 (RCP 4.5), a moderate projection of future CO2 emissions, and Representative

Concentration Pathway 8.5 (RCP 8.5), the highest projection of future CO2 emissions. All data were taken from MACAv2-METDATA (Abatzoglou 2013), downloaded from the Multivariate

Adaptive Constructed Analogs (MACA) data portal (see Table S1). We aggregated daily climate

8 data over each time period by season, with the exception of total annual precipitation. For each temperature variable, we took the mean, maximum, minimum, and standard deviation for each growing season of each year of climate data (April to mid-September). We took the mean total annual precipitation, the standard deviation of annual precipitation, the mean total growing season precipitation, and also the mean total winter (mid-September through March) precipitation as it is known to drive water availability in this area, especially during the start of the growing season (Hunsaker et al. 2012). This resulted in 12 precipitation and temperature covariates (Table S1).

Proximity to a water source is a known factor in the distribution of some of our shrub species (Baldwin et al. 2012). We determined the shortest distance to a lake, river, or stream using the USGS National Hydrography Dataset Best Resolution for California (U.S. Geological

Survey, 2017) and the “v.distance” function in QGIS 2.18 (GRASS GIS 7) for each presence- absence, presence-only, and quadrature point.

The bias terms within the model describe the spatial aggregation in presence-only data that results from greater survey effort near easy-to-access areas. This spatial variation in observations recorded in unplanned surveys can be described by a variety of ease-of-access variables (Mair and Ruete 2016). In our mountainous study system, road access and slope greatly limit access, so we used distance to nearest road, elevation, and the steepness of slope as potential bias covariates to describe the spatial aggregation of presence-only points due to where surveys were most likely to have occurred. We assessed distance from roads using the

“v.distance” function in QGIS 2.18 (GRASS GIS 7) for each presence-absence, presence-only, and quadrature point, using the major roads of California and Nevada datasets (2016

TIGER/Line Shapefiles). We used the USGS Digital Elevation Model (DEM) in 1/3 arc second

9 blocks (Gesch 2007) to calculate elevation at each quadrature. We also used this DEM to feed into the QGIS tool “DEM (Terrain models)” to calculate percent slope at each quadrature.

We performed pairwise correlation analyses for all possible environmental and bias covariates. Distance to water was not highly correlated with any temperature, precipitation, or bias covariate (<0.3). All precipitation covariates were highly correlated with each other (>0.9), as were all temperature covariates (>0.8). Precipitation covariates were not highly correlated with temperature covariates (0.2-0.55). To avoid multicollinearity, while still allowing us to distinguish precipitation and temperature effects, we performed separate principal component analyses on each set of variables (Dobrowski et al. 2011) (Table S2, Table S3). We predicted principal component scores for future climate scenarios, based on the results of our principal component analysis of current climate (cf. Dobrowski et al. 2011). We also ran principal component analyses for each future scenario to check loadings and proportion of variation explained against the principal component analysis for current climate. Future climate loadings and proportion of variation explained were nearly identical to those in the current climate so principal component score predictions based on current climate should be sufficient to represent future climate. For our final analysis we chose the first two precipitation principal components, which explained >99% of total variation in the current climate, and the first two temperature principal components, which explained >96% of total variation in the current climate.

Our bias covariates were chosen a priori and only elevation correlated above 0.7 with any other covariate. For the final models, we used distance to road and slope as our bias covariates and distance to water, and the first two PC’s of precipitation and temperature as our environmental covariates. We centered road, slope, and distance to water variables and bias

10 covariates by subtracting its mean and dividing by its standard deviation for each (Schielzeth

2010).

Species Groupings

To reduce computational load and speed up processing time for our species distribution model, we broke our full list of species into four groups (Table 1). To select the number of groups, we used all shrub survey plots within which at least one species was located to perform a k-means analysis in the R package “vegan” (Oksanen et al. 2018), specifying a minimum of two and a maximum of six groups. Using the three groups of species identified by this k-means analysis (Figure S1), we summed the number of presences for each species within each group.

Some species were relegated to a single group making group assignment simple, but most species were found in two or all three of the groups. When a species was found in multiple species groups, we assigned the species to the group number in which it had the highest number of presences. Because one group had twice as many species as the other two, we split it in half alphabetically, resulting in four groups of seven or eight species each.

Model prediction

To project individual species distributions, we used the inverse logit of the joint intercept as well as the environmental covariates:

&a D b ∗F (G)D b ∗F (G)D b ∗F (G)D b ∗F (G)D b ∗F (G)+ ; E E H H I I J J K K 9:;<=>?;< <=)?:=@A?=BC = &a D b ∗F (G)D b ∗F (G)D b ∗F (G)D b ∗F (G)D b ∗F (G)+ 1 + ; E E H H I I J J K K

We used the full posterior distribution of the intercepts and slope values so as to be able to put uncertainty bounds on each cell. We did not use the bias covariates in these predictions because

11 they are simply used to adjust parameter estimates for the environmental variables, and do not add to the description of the species distributions. We predicted current distribution and future distributions under the RCP4.5 and RCP8.5 scenarios for each shrub species.

We tested our models using ten-fold block cross-validation. We randomly selected quadratures to create ten equally-sized groups of quadratures across our entire region (Fithian et al. 2015). Because not all quadratures contained presence-only or presence-absence points, this selection was stratified across 1) quadratures that contained at least one type of data points, and

2) those that did not. The first randomly selected group that contained data points was matched with the first that did not. This approach was repeated, to create 10 groups of quadratures with each containing some quadratures with presence-absence or presence-only points and some without. For ten runs of each model, we left out one group, including background points, and calculated each individual species distribution with the nine remaining groups (Fithian et al.

2015). We calculated the true skill statistic (TSS, sensitivity + specificity-1) (Allouche et al.

2006, Nenzen and Araujo 2011) for each species, using the left-out group as our observed presences and absences and the prediction from the remaining nine groups as our predicted presences and absences. We evaluated model fit through the maximum of TSS and Cohen’s

Kappa for each species (Table S4) (Cohen 1960, Nenzen and Araujo 2011).

Species richness and turnover

To determine whether a species was likely to be present in a given quadrature, we used

TSS to convert our predicted probability to a binary present/absent variable (Allouche et al.

2006, Nenzen and Araujo 2011). We tested 100 threshold values for each species ranging from the minimum, non-zero, predicted value of intensity to the maximum value, and used the value

12 that maximized TSS (Nenzen and Araujo 2011). Because TSS compares predictions against observations, we could not calculate it for future scenarios and used the threshold values identified for current distributions instead. We created presence/absence rasters for each species based on their threshold values and summed these rasters to estimate the current number of species present in any given cell. We used our estimates of species richness to calculate the expected change in richness under each future scenario. Species turnover was calculated with the

Sorensen diversity index within the R package “betapart” (Baselga 2018) by pairing the current shrub community at any given cell with the future shrub community. We analyzed the change in species richness and Sorensen distance by elevation by fitting a local polynomial regression smoother (LOESS) regressions to each (quadratic (degree=2), alpha=0.7).

To compare our shrub species turnover with the bird species turnover for the central

Sierra Nevada, we used predictions for birds in Stralberg et al. (2009). Those authors projected bird species turnover for the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios A2 scenario, using two global climate models (GCM’s) – GFDL GCM

CM2.1, and National Center for Atmospheric Research Community Climate System Model

(CCSM3.0) – and using maximum entropy (MAXENT) species distribution models (see

Stralberg et al. 2009 for full modeling and dissimilarity calculation methods). Because the IPCC

A2 scenario projects 3.7°C of warming globally in the fourth assessment report (IPCC 2007), we compared it to our species turnover at RCP 8.5, which also projects 3.7°C of warming globally in the IPCC fifth assessment report (Flato et al. 2013). We cropped each dissimilarity index raster to the central Sierra Nevada and then extracted the dissimilarity for each quadrature, using the R package ‘raster’ (Hijmans 2017). We analyzed the change in Sorensen distance for both GCMs

13 as compared to elevation by fitting LOESS regressions to each (quadratic (degree=2), alpha=0.7).

The three most frugivorous bird species, as defined by diet breadth and percent of fruit within their diet, are cedar waxwing (Bombycilla cedrorum), American robin (Turdus migratorius), and Townsend’s solitaire (Myadestes townsendii) (Ch 2, Ch 3, Cohen et al. 2009,

Carlisle et al. 2012). We compared their future ranges to changes in species richness to determine where shrub species might encounter spatial mismatch with these three important dispersers. We subtracted the future suitability from the current suitability using the unmodified

MAXENT outputs for each GCM and each of these three bird species to get difference in suitability. This difference in suitability served as a proxy for where each species might lose or gain range and abundance under future climate scenarios. We then compared the change in suitability of these frugivorous species to our calculated change in species richness of shrubs. We determined, by elevation, if changes in suitability and species richness were happening in concert or if the shrubs and these dispersers are predicted to shift ranges in different ways.

Results

Precipitation and temperature relationships

Precipitation PC1 explains over 95% of the variation in precipitation variables, and reflects higher and more variable levels of precipitation. Growing season precipitation, and to a lesser extent standard deviation of annual precipitation, were most correlated with precipitation

PC2, which explains most of the remaining variation. An increase in growing season precipitation generally equates to higher values of PC2, and an increase in standard deviation of precipitation equates to a decrease in PC2 (Table S2). The PC1 for temperature variables

14 explains 87% of the variation in temperature and provides an inverse measure of temperature.

PC2 explains 10%, most of the remaining variation in temperature, and primarily explains variation in the daily temperature extremes (Table S3).

A combination of precipitation and temperature helped explain the distribution of most species. Overall the number of species with at least one non-zero temperature variable was higher than the number of species with at least one non-zero precipitation variable (25 and 19, respectively) (Figure S2). Nine species had a positive relationship with precipitation PC1 versus

5 with a negative relationship, whereas seven species had a positive relationship with precipitation PC2 versus seven with a negative relationship (Figure S2). A positive relationship with precipitation PC1 indicates a greater species intensity with greater precipitation overall

(Table S2). If this positive relationship with precipitation PC1 is combined with a negative relationship with precipitation PC2, as in the case of three of the species, winter precipitation likely drives the species intensity (Table S2). If the species has a negative relationship with precipitation PC1 combined with a positive relationship with precipitation PC2, as in the case of five of the species, growing season precipitation likely drives the species intensity (Table S2).

Nineteen species had a positive relationship with temperature PC1, suggesting that lower growing season temperatures were associated with increased species intensity (Figure S2, Table

S3). Only four species had a significant negative relationship with temperature PC1, indicating that for these species higher temperatures were associated with increased species intensity

(Figure S2). Nine species had a positive or negative relationship with temperature PC2, indicating that standard deviation of temperature was less important for the distribution of more species than maximum and minimum temperature (PC1) (Figure S2, Table S3). Additionally, nearly all species had a non-zero relationship with distance to water, all groups had a negative

15 relationship with distance to road, and two of the four groups had a negative relationship with slope (Figure S3).

Shrub species diversity and secondary threats

Shrub species richness was generally lost at elevations above ~2500 m in predictions of future scenarios. In some locations at around 3000 m, up to one third of shrub species were lost, with an average across locations of two to five species lost. On average, species richness increased at elevations below 2000 m (Figures 1, 2, S4, S5). Species turnover was highest on average between 1000 and 3000 m, peaking at 2000 m (Figures 3, S6). Many locations at elevations of 1000 m to 3000 m had >50% dissimilarity between current (1950-2005) and future

(2006-2099) scenarios (Figures 3, S6). Although the average species turnover was lower at elevations <1000 m, some locations still experienced >50% species turnover (Figures 3, S6).

Elevations above 3000 m experienced approximately the same average percent turnover as low elevations, but few locations had >40% dissimilarity and none had >70% dissimilarity (Figures

3, S6). Future scenarios RCP 4.5 and 8.5 produced similar results for both species richness and species turnover.

The greatest bird species turnover on average is predicted to occur between 500-1500 m elevation, which is generally lower than that for shrub species turnover (1000-3000 m) (Figures

3, S6). Unlike shrub species turnover, no location had >45% dissimilarity for bird species

(Figure 4). The suitability for cedar waxwings across the landscape showed little change between current and future climate, with most locations showing no change (Figure S7). Where suitability for cedar waxwings did decline slightly, species richness of shrubs generally increased (Figure

S7). At elevations below 1000 m, shrub species richness increased, but suitability for American robins and Townsend’s solitaires decreased (Figures S8, S9). Above 3000 m, shrub species

16 richness largely declined whereas suitability for American robins increased in some locations but decreased in others (Figure S8) and suitability for Townsend’s solitaires largely increased

(Figure S9).

Half of the shrub species in our study increased their range within protected areas, and half decreased their range, with the mean change for both climate scenarios centered on 0 (RCP

4.5 = 0.03 (-0.03-0.08, 95% CI); RCP 8.5 = -0.01 (-0.07-0.05, 95% CI)) (Figures 5, S10). Five species gained from 20% up to nearly 40% of the total area of the protected area and three species lost between 20% and 25% of the total area of the protected area from their range

(Figures 5, S10, S11). No species shifted its range so much that it no longer occurred within protected areas. Ten species, one third of those in our study, were projected to have a distribution that covers 15% or less of the protected area, and 7 of these species lost over half of their range within the protected area (Figures 5, S10). Three species more than doubled their range within the protected area (Figures 5, S10). Three species that currently have a restricted distribution were nearly extirpated within our future projections, regardless of scenario, with a distribution that covers no more than 5% of the protected area, but the two least widely distributed species within the protected area actually gained protected extent (Figures 5, S10, S11).

Discussion

Species richness increased at lower elevations and decreased at higher elevations on average (Figures 1, 2, S4, S5), indicating that species were projected to generally move downslope. Species turnover was projected to be highest at middle elevations (Figures 3, S6), indicating that this downslope movement is likely to result in the highest reshuffling of shrub communities where shrubs at middle elevations are predicted to move out of the area to lower

17 elevations, while higher elevation shrubs are predicted to move into the area. We suggest that these projected downslope movements would likely be driven largely by precipitation. If species were instead tracking lower temperatures, they would be expected to move upslope. Nearly two- thirds of our species were associated with lower temperatures (Figure S2, Table S3), but we did not see general upslope movement. Therefore, shrubs were generally projected to move downslope to track better precipitation regimes. Other studies in the mountainous regions of

California and Europe have found that plant species have shifted downhill from the 20th century to the present day, but to track moisture instead of temperature (Crimmins et al. 2011, Scherrer et al. 2017). More generally, a variety of species in several regions around the world have been found to track a combination of moisture and temperature, leading to non-poleward or non- upslope range shifts (Tingley et al. 2012, VanDerWal et al. 2013, Lenoir and Svenning 2015).

By contrast, other reviews and studies have found overwhelming evidence for species tracking lower temperatures poleward or upslope (e.g. Walther et al. 2002, Steinbauer et al. 2018).

Individual species identities and physiological niches may play a large part in determining whether species are moving in a direction contrary to the average poleward or upslope movements.

The inhomogenous Poisson process used in our model is mathematically similar to the popular MAXENT method for presence-only data (Phillips and Dudik 2008; Aarts et al. 2011,

Renner et al. 2015). The approach we took, however, allows one to incorporate parameters to estimate the sampling bias of the presence-only dataset (e.g. Wolf et al. 2011), which results in more precise and accurate estimates of the environmental predictors of species ranges as well as greater predictive accuracy (Fithian et al. 2015, Fletcher et al. 2016). Five of our species were found at less than 20 locations within the presence-absence dataset, and this combined presence-

18 absence, presence-only framework allowed us to be able to model those species despite this data limitation. The trade-off for this increased precision and accuracy even with few presence points is that this multi-species model is computationally intensive (Fithian et al. 2015).

Bioclimatic distribution models have been shown to be a good approximation of species niches in the current climate and for broad-scale responses by species to future climate change

(Pearson and Dawson 2003). However, many studies have shown that the most accurate species distribution models incorporate many types of data: dispersal, biotic interactions, population processes, land use, economics, and others (e.g. Guisan and Thuiller 2005, Latimer et al. 2006,

Araujo and Luoto 2007, Franklin 2010, Barve et al. 2011, Merow et al. 2011, Ay et al. 2016).

Often this type of detailed information is costly to collect, both monetarily and time-wise, especially in the context of a multi-species study or a large geographic area. Our projections for future species’ ranges assume perfect dispersal because of the difficulty of collecting data for dispersal parameters. For the most accurate projections of fleshy-fruited shrub species in the future, biotic interactions with frugivores as a proxy for dispersal, or direct measures of dispersal distance, would need to be incorporated into models.

One predicted result of climate change is the appearance of no-analog communities in which species assemblages are distinct from those that have previously existed (Williams and

Jackson 2007). Stralberg et al. (2009) found that bird species are predicted to form no-analog communities across California. We found that areas of high turnover in shrub species do not match predicted areas of high turnover in bird communities, meaning that large-scale reshuffling of Sierra Nevada bird-shrub interaction networks is possible. Because bird species turnover is based on all bird species, not just frugivorous ones, the high turnover may not be due to frugivorous species. To address this concern, we compared shrub species richness change to

19 suitability change for the three most frugivorous species: American robin, cedar waxwing, and

Townsend’s solitaire. Suitability for cedar waxwings exhibited very little change across the range, but a predicted slight decrease in suitability for cedar waxwings generally occurred at the same elevations as a predicted increase in shrub species richness. However, at low elevations, the predicted suitability for both American robin and Townsend’s solitaire largely decreased while shrub species richness was predicted to largely increase, and at high elevations, the opposite was predicted to occur.

Spatial mismatch between bird and shrub species could occur at high and low elevations, where shrubs are predicted to lose richness and suitability for frugivores declines, respectively, due to climate change. From the perspective of the shrubs, spatial mismatch could lead to a reduction in interactions and thus a loss of seed dispersal services, including the ability to move downslope to more suitable climatic conditions. At worst case, it could lead to broken interactions between partners (Schweiger et al. 2008), which could threaten the survival of shrub species (Traveset et al. 2012, Aslan et al. 2013, Rogers et al. 2017) or lead to secondary extinctions (Dunne et al. 2002, Tylianakis et al. 2010, Brodie et al. 2014). However, the distribution of one generalist frugivore, cedar waxwings, is not predicted to change much across the landscape, which could mitigate declines in other frugivores. While functional loss is still possible if cedar waxwings cannot make up for the frequency of seed dispersal interactions lost

(Cronk 2016), their continued presence across the landscape reduces the risk of the secondary threat of spatial mismatch.

We found that because species do not simply move in concert across the landscape, climate change results in winners and losers, with the secondary threat of losing ground within protected areas only acting on half of our species. No species were predicted to be completely

20 extirpated from Yosemite National Park, but three species with restricted ranges were predicted to be nearly extirpated (Figures 5, S10). The predicted near-loss of these species from this large national park illustrates the vulnerability of even those species currently under protection.

Species that were predicted to lose areal distribution within the park could be at risk due to climate change, especially if the landscape matrix outside out of conservation reserves is composed of land use types that are inhospitable to these species (Newmark et al. 1994, Thomas et al. 1992, McKinney 2006). Our findings add to the growing evidence that currently protected species could cease to be so as they shift their ranges with changing climatic conditions (Hannah et al. 2005, Klausmeyer and Shaw 2009, Loarie et al. 2009, Lemes et al. 2014). However, our results also illustrate the potential for some species to gain protection through climate change.

These results echo results of both gain and loss of mammals and birds from national parks in the

United States (Burns et al. 2003, Wu et al. 2018) and illustrate the need to integrate species- specific predictions into long-term conservation planning.

To keep pace with climate change, newly proposed protected areas should incorporate climate change projections with the aim to protect species currently and in the future (e.g.

Alagador et al. 2014). Further, because climate change results in greater protection for some species and less protection for other species, we need to know the identity of species that are likely to make gains vs those that will suffer losses and design prospective protection accordingly. Because climate change introduces additional challenges in protecting species, static species protection like national parks may need to be supplemented with additional conservation strategies like corridors to allow for migration of species from climatically less- suitable to more-suitable protected areas (Williams et al. 2005, Krosby 2010), partnering with

21 surrounding areas for coordinated conservation efforts (Monahan et al. 2018), or targeting projected climate refugia for conservation priority (Morelli et al. 2016).

22 Table 1. List of 30 shrub and small tree species found in the Sierra Nevada Mountains, CA. Group number, as identified by a k-means analysis, for species groupings for species distribution modeling is included. Group 1 is generally composed of species that are found largely or exclusively on the western side of the Sierra Crest. Group 1 was much larger than the other two groups and was split in two, denoted by 1 (a) and 1 (b). Group 2 is composed mostly of species that are found on both sides and across the Sierra Crest. Group 3 is composed of species that are found largely or exclusively on the eastern side of the Sierra Crest.

Group Species Family Number Amelanchier alnifolia Rosaceae 3 Arctostaphylos manzanita* Ericaceae 1 (a) Arctostaphylos nevadensis Ericaceae 1 (a) Arctostaphylos patula Ericaceae 1 (a) Arctostaphylos viscida Ericaceae 1 (a) Cornus nuttallii Cornaceae 2 Cornus sericea Cornaceae 3 Frangula rubra Rhamnaceae 3 Lonicera conjugialis Caprifoliaceae 2 Lonicera involucrata Caprifoliaceae 2 Prunus andersonii Rosaceae 3 Prunus emarginata Rosaceae 3 Prunus virginiana Rosaceae 1 (a) Ribes cereum Grossulariaceae 3 Ribes inerme Grossulariaceae 2 Ribes montigenum Grossulariaceae 2 Ribes nevadense Grossulariaceae 1 (a) Ribes roezlii Grossulariaceae 1 (a) Ribes viscosissimum Grossulariaceae 1 (a) Rosa gymnocarpa Rosaceae 1 (b) Rosa woodsii Rosaceae 3 Rubus leucodermis Rosaceae 1 (b) Rubus parviflorus Rosaceae 2 Sambucus nigra Adoxaceae 1 (b) Symphoricarpos mollis Caprifoliaceae 1 (b) Symphoricarpos rotundifolius Caprifoliaceae 3 Toxicodendron diversilobum Anacardiaceae 1 (b) Umbellularia californica Lauraceae 1 (b) Vaccinium cespitosum Ericaceae 2 Vaccinium uliginosum Ericaceae 1 (b)

*Arctostaphylos manzanita is endemic to California

Figure 1. Current (left) and projected future (RCP 8.5; right) species richness of fleshy-fruited shrubs within the Sierra Nevada Mountains, California, USA. Yosemite National Park is outlined on the map.

Figure 2. Left: Future (RCP 8.5) species richness minus current species richness of fleshy-fruited shrubs within the Sierra Nevada Mountains, California, USA. Yosemite National Park is outlined on the map. Right: Elevation versus the change in species richness from current to future. Each point is one quadrature. Line is LOESS smoothing average.

Figure 3. Left: Species turnover from current to future (RCP 8.5) in the Sierra Nevada Mountains, California, USA, as calculated by Sorensen dissimilarity index. Yosemite National Park is outlined on the map. Right: Elevation versus species turnover from current to future. Each point is one quadrature. Line is LOESS smoothing average.

26 0.45 0.45 0.40 0.40 0.35 0.35 0.30 0.30 0.25 0.25 Sorensen Distance Sorensen Distance Sorensen 0.20 0.20 0.15 0.15

0 1000 2000 3000 4000 0 1000 2000 3000 4000

Elevation (m) Elevation (m)

Figure 4. Elevation versus species turnover from current to future for bird species (GFDL scenario A2 and RCP 8.5, left, and CCM scenario A2 and RCP 8.5, right) in the Sierra Nevada Mountains, California, USA. Each point is one quadrature. Line is LOESS smoothing average. Data are from Stralberg et al. (2009).

Figure 5. Each fleshy-fruited shrub species in the Sierra Nevada, California, USA, and their distribution within Yosemite National Park. Purple indicates the proportion of the park occupied a species under both current (red) and future (blue) scenarios. Species bars where red is visible are projected to lose part of their distribution within Yosemite. Species where blue is visible are projected to gain distribution within Yosemite. RCP 8.5 was used to project the future climate.

28 Spatial scaling of network diversity

Introduction

Species richness varies both across space (Whittaker 1960, Field et al. 2008) and with spatial scale (Preston 1962, Rahbek 2004). The species-area relationship is a foundational idea in ecology and conservation biology, with support from multiple ecosystems worldwide (Drakare et al. 2006, Field et al. 2008). As successively larger areas are surveyed, the number of species found in each area increases following a power function that can be represented linearly on a log- log scale (Connor and McCoy 1979). In conservation biology, the species-area relationship has principally been used to predict the number of species that will be lost with habitat destruction

(Simberloff 1991, Pimm and Askins 1995, Pimm and Raven 2000). These predictions have been extended to the loss of species with contemporary anthropogenic climate change (Kinzig and

Harte 2000, Thomas et al. 2004, Urban 2015). One limitation of these predictions of species loss given area reductions, however, is that the loss of biological interactions – and the secondary extinctions arising from these losses – has not been accounted for (Lewis 2006, Aizen et al.

2012, Urban et al. 2013, Brodie et al. 2014).

Interaction networks are comprised of species that interact with at least one other species within the same network. Interaction networks can be multi-trophic, such as food webs, uni- trophic, such as competition networks, or bi-trophic, often referred to as bipartite networks

(Memmott 1999, Jordano et al. 2003, Thebault and Fontaine 2010). Bipartite networks are often used to illustrate mutualist networks, such as pollination (e.g. Olesen et al. 2007) or seed dispersal (e.g. Carlo and Yang 2011), as well as parasitism networks (Poulin 2010). Because species richness and composition vary across space and spatial scale (Wiens 1989), and since

29 this applies to both sets of interacting species within a network, the structures of bipartite networks must also vary across both space (e.g. Burkle and Alarcon 2011, Trøjelsgaard et al.

2015) and spatial scale (Sabatino et al. 2010, Sugiura 2010, Burkle and Knight 2012).

Interactions such as mutualisms are essential to community structure (Stachowicz 2001), but our understanding of them in a spatial context is still lacking. How might the structure of networks change across space and scale? Here we extend the species-area relationship to include interactions within bipartite networks.

In order to extend the concept of the species-area relationship, we first present theory governing how scaling is expected to work as one expands from a species-area scenario to an interactions-area one. Thus, we begin by exploring how species interactions scale with space, specifically the shape and limits on an interactions-area curve. We then provide mathematical relationships for the interactions-area curve combined with the species richness-area curve.

Third, we model how the accumulation of species interactions with area relates to network connectance, a commonly-used measure of the number of links per species within a network

(Jordano 1987). As some evidence exists that connectance of bipartite webs changes with size of a habitat patch (e.g., Aizen et al. 2012), our mathematical formulation of the interactions-area curve specifically accounts for connectance and allows it to either remain constant or vary with area. Finally, we use a case study from an empirical seed dispersal network from the Sierra

Nevada Mountains, California to illustrate how our proposed interactions-area curve fits real- world data and how the interactions within a network scale with space.

Interactions-Area Relationship

Ecologists currently have little empirical evidence with which to describe the

30 interactions-area curve, and no clear expectations for the shape and bounds of this curve. Here, we generate a general mathematical expectation for the number of interactions per area by combining the species-area relationship (Connor and McCoy 1979) with variation in connectance (Jordano 1987). Our proposed interactions-area curve rests on the following assumptions. First, we assume that the total number of interactions in the network must always increase as area increases because the total number of species increase as area increases. In the case of continuous, nested areas, if two species interact at one spatial scale, they always interact at any spatial scale where both co-occur. In the case of independent, isolated patches, larger patches must have more interactions than smaller ones, even if species turnover between isolated patches is high, because larger patches have more species based on the species-area curve.

Second, in the case of continuous, nested areas we assume that each new species added with increasing area interacts with at least one other species within the network.

In the power function specification of the species-area relationship, where S = cAz, the common logarithm of species richness (S) increases linearly with the common logarithm of increasing area (A), where c and z are constants (Connor and McCoy 1979). Using this equation, we can first define the minimum and maximum number of interactions for any given area

(Martinez 1992). If we assume that a mutualist network within area A is made up of S1 species from group 1 (e.g., fleshy-fruited shrubs) and S2 species from group 2 (e.g., frugivores), then the minimum number of interactions is the case in which each species in the more speciose group interacts with only one species in the other group. Thus, if S1 ³ S2, then the minimum number of interactions is S1. More generally, for a given area, the minimum number of interactions is max{S1, S2}. The maximum number of interactions for a given area is the situation in which every species in each group is connected to every species in the other group, which is given by

31 S1 * S2.

Determining the minimum and maximum number of interactions given the size of two species pools (Martinez 1992) sets the outer limits for a potential interactions-area curve.

Because the minimum number of interactions (I) is max{S1, S2}, the lower bound of the interactions-area curve is simply the species-area curve of the larger of the two species pools:

,- ,0 ! = max{'( ∗ *+ , '/ ∗ *+ }, Equation 1

where c1, c2, z1, and z2 are the constants described by the power function model of the species- area relationship (Connor and McCoy 1979) for species groups 1 and 2, Ai is area at all A1, A2, …

Ai, and I is the total number of interactions (interaction richness). This can be common log- transformed to give:

log(!) = max {log('() + 9( ∗ log(*+) , log('/) + 9/ ∗ log(*+)}. Equation 2

For every possible interactions-area curve above the minimum, the number of interactions is set by the number of species within both groups. Using this principle, we can generalize to a situation where the size of the species pool – and thus the number of interactions

z1 z2 – changes with area. To this end, if we assume that S1 = c1*A and S2 = c2*A , then the upper bound curve to the number of interactions is:

,- ,0 ! = '( ∗ *+ ∗ '/ ∗ *+ Equation 3

32 By taking the log of this equation, we get the linear relationship:

log(!) = log('() + log('/) + (9( + 9/) ∗ log (*+) Equation 4

Because the upper bound for the interactions-area curve is set by the product of the number of species in group 1 and group 2, then increasing either z increases the slope of the curve with respect to area.

The interactions-area curve of any given network can occupy the space anywhere between the upper and lower bound curves. We note, however, that no natural network has ever been described with the minimum interaction richness (Bluthgen et al. 2007). Similarly, while the maximum potential interaction richness has been observed in ant-myrmecophyte networks, it has not been observed in other types of networks (Bluthgen et al. 2007); therefore, this maximum may be unrealistic in most situations. Thus interactions-area curves are likely to be unequally distributed within potential curve space.

Using Equation 3 as a starting point, we can determine the equation for any curve between the lower and upper bound as a function of a network’s connectance, defined as C =

I/(S1S2) (Jordano 1987), such that:

! = : ∗ ;( ∗ ;/ Equation 5

Connectance is a measure of the average number of links per species within a network

(Bascompte 2009) and is measured on a scale of 0 to 1, where zero implies no connections and 1 implies that every species in one group interacts with every species in the other. At a

33 connectance of 0.5, each species in one group interacts with approximately half of the species in the other group. Connectance depends on the number of species within a network, but the opposite is not true. Equation 1, which defines the lower bound of the interactions-area curve, is

-1 -1 the special case where connectance is equal to 1/(max{S1 , S2 }). Equation 3, which defines the upper bound, is the special case where C = 1. Equation 5 can be generalized as:

,- ,0 ! = : ∗ '( ∗ *+ ∗ '/ ∗ *+ Equation 6

and,

log(!) = log(:) + log('() + log('/) + (9( + 9/) ∗ log (*+) Equation 7

Equations 6 and 7 both assume that connectance is constant across all areas. When this is the case, the intercept of Equation 5 is defined by the constant log(C) + log(c1) + log(c2).

Similarly, the slope of the interactions-area curve in log-log space is the sum of z1 and z2, which we will call b. Because empirical estimates of z generally lie between 0.15 and 0.35 (Lomolino

1989), we can expect empirical estimates of b to be between 0.3 and 0.7 when C is constant across area.

Connectance may (Martinez 1992) or may not (Sugiura 2010, Vazquez et al. 2009) change as a function of area. If connectance increases with area, the slope of the interactions-area curve will be greater than b. If connectance decreases with area, the slope of the interactions-area curve will be less than b. If we assume two area sizes (Aa and Ab with Ab > Aa), two values of connectance (Ca and Cb, corresponding with Aa and Ab), and b, the change in number of

34 interactions (Ia to Ib) over the change in area is (see Appendix B for full derivation):

( ) ( ) <=> ?@ A<=> ?B + b <=>(C@)A<=> (CB)

Or, the change in connectance scaled to the change in area, added to the slope of the interactions- area curve as defined by the z’s for both groups.

If we assume that connectance changes linearly with area such that

log(Ci) = a + d*log(Ai),

and

( ) ( ) <=> ?@ A<=> ?B = d, <=>(C@)A<=> (CB)

then the full equation for the interactions-area curve, given changing connectance, is:

log(!) = D + log('() + log('/) + (E + F) ∗ log(*+). Equation 8

If species that have more interactions than the average for the network are added, connectance increases as does the slope of the interactions-area curve. As a simple example, if

C0 = 0.5 and, when moving from Aa to Ab, every species added to group 1 and group 2 interacts with more than half of the opposite group, C1 will be >0.5. The most extreme case of this and thus greatest interactions-area slope possible is moving from Ca = 1/S2 to Cb = 1, and can be

35 calculated for any given system with Equation 8 (Figure 1).

Frugivorous birds and shrubs in montane California

Seed dispersal is a mutualistic interaction that is critically important to plant population dynamics. For those species that depend on animals, interactions with partner species can be essential for a population’s persistence (Howe and Miriti 2004) and the ability to respond to climate change through a geographic range shift (e.g., Davis and Shaw 2001). Fleshy-fruited shrubs largely depend on frugivorous birds to disperse their seeds, especially over long distances such as those required for species to match the pace of climate change (Clark et al. 1999,

McEuen and Curran 2004, Loarie et al. 2009). Frugivorous birds, in turn, rely on the fruits produced by these shrubs to build up fat stores for migration or winter survival in temperate regions (Parrish 1997). We used an empirical seed dispersal network to illustrate how our proposed interactions-area curve (Equation 8) fits this real-world data and how the interactions within this network scale with space. We focus our case study on the avian seed dispersal networks of the central Sierra Nevada Mountains, California. We employ a combination of on- the-ground plant surveys to determine the plant species and citizen scientist bird surveys from the eBird platform (Sullivan et al. 2009) to determine the bird species contained within each area’s networks, as well as an interaction network compiled from multiple sources. Avian seed dispersal networks have the benefit that both partner species are easily observable, and an abundance of bird diet information exists within the literature (e.g., Rodewald 2015).

Study system

The Sierra Nevada Mountains lie in the California Floristic Province biodiversity hotspot

36 and have high levels of both biodiversity and endemism (Myers et al. 2000, Kraft et al. 2010).

We sampled both sets of partners in the avian seed-dispersal network of the central Sierra

Nevada Mountains. We focused on angiosperm shrubs and understory trees with fleshy or semi- fleshy bird-dispersed fruits. Our study region ranged between ~1000 m and 4000 m elevation

(Figure S1). The west and east side of the mountains, divided by the Sierra Nevada crest, experience largely distinct precipitation regimes. Depending on elevation, the west side receives

75 to 200 cm per year of precipitation and the east side receives <25 to 100 cm per year. In typical years, much of this precipitation falls as snow during winter.

Shrub sampling and patch area

In 2014 and 2015, we collected georeferenced shrub data in and around Yosemite

National Park and the Lake Tahoe region. Sampling followed a generalized random tessellation stratified (GRTS) design, which results in a spatially-balanced random sample (Stevens and

Olsen 2004). We laid a 2 km2 hexagonal grid over the central Sierra Nevada and selected hexagons from this grid using the R package “spsurvey” (Kincaid and Olesen 2013), which implements GRTS sampling from a user-provided grid. From the selected hexagons, we discarded any that were inaccessible because of prohibitively steep slopes or glaciers as well as any that were more than 10 km away from a paved or gravel road. Within the remaining 34 hexagons, we randomly selected 20 survey plots, of which we surveyed 2-5 per hexagon, for a total of 140 plots. The first plot per hexagon was randomly selected, and others were randomly selected from the subset of 20 that were 0.5 to 1 km away from the original plot. Survey plots were 30 m in diameter, within which we thoroughly surveyed the plot for the presence/absence of all focal shrub species.

37 To create patches from our shrub sampling for our species-area and interactions-area analyses, we placed 5 km buffers around all shrub survey plots and dissolved overlapping buffers in QGIS 2.18 (QGIS Development Team 2018). By chance, some of the hexagons containing shrub survey plots fell within overlapping 5 km buffers. These combined sites became larger patches while shrub survey plots contained within more isolated hexagons became smaller patches (Figure S1). Because of a rapid shift in annual precipitation when moving across the

Sierra crest, the plant communities experience rapid turnover (Richerson and Lum 1980). We ensured that no patches crossed the crest so as to avoid this within-patch turnover of habitat and species. Because samples need to be equivalent to properly compare species richness, we standardized survey effort per area of patch (species density) at each of our patches (Gotelli and

Colwell 2001). Using QGIS, we calculated each patch’s area. We then calculated the minimum number of shrub survey plots per area, and randomly selected survey plots within each patch to match the calculated minimum density of shrub survey plots, discarding any additional survey plots above minimum density. One patch had none of the focal plant species and was not included in the analyses.

Bird sampling

We used eBird (Sullivan et al. 2009), a large bird observation database in which citizen scientists submit checklists of bird species they observed, to determine presence/absence of bird species at our patches. We pulled checklists in which all species detected were recorded for 2014 and 2015 from the eBird database for locations within the central Sierra Nevada. In QGIS we limited these checklists to only those that fell within one of our patches. Because the frugivore data are used in conjunction with the shrub data in calculations of number of interactions, we

38 standardized effort across patches for both groups of species. We eliminated any checklists in which the number of observers was recorded as more than 2, and any in which the hours of effort fell outside of the range 0.2-0.8, to standardize survey effort by number of observers and length of survey time across checklists. For each polygon, we kept only checklists from April-June at lower elevations (600-1250 m), May-July at mid elevations (1250-2100 m), and July-August at high elevations (2100-4000 m), based on the fruiting phenology of the species found at these elevations. We randomly selected checklists for each patch, matching the number of shrub surveys to the number of checklists selected. Three patches had no bird surveys and were not included in the analyses.

Network construction and characterization

We specified a comprehensive seed dispersal network for all of the central Sierra Nevada using a number of approaches: literature searches, citizen science observations, camera traps, fecal collection, and preserved stomach collections (see Ch. 3 for details on the construction of this network). Using plant and bird species lists for each patch, we reduced the full network to only those species found in each patch. We summed the total number of bird-shrub interactions within each patch’s network and also calculated the network connectance at each patch (Jordano

1987).

Comparison of our theoretical interactions-area curve to the frugivore-shrub network

We fit linear regressions to the number of (a) shrub species, (b) bird species, (c) interactions (Burkle and Knight 2012), and (d) variation in connectance by patch area. For all regressions both area and response variable were common log-transformed. All regressions were

39 performed in a Bayesian framework. The slope and intercept of each regression were given vague priors with a mean of 0 and standard deviation of 0.001. The models were fit in JAGS

(Plummer 2003) via the R package ‘R2jags’ (Su and Yajima 2015). We ran each model with three chains for 50,000 iterations. We checked for convergence by ensuring the scale reduction factors approached one for all parameters (Gelman and Rubin 1992).

Finally, we compared the statistical model describing our empirical measures of interaction richness over patches of different area against our theoretical expectation for this relationship (Equation 8). We used the full posterior distribution of c and z for both plant and bird species-area curves, as well as the full posterior distribution for a and d, from our connectance linear regression to calculate the mean and 95% confidence interval for our theoretical expectation.

Results

Our complete network consisted of 28 plant species, 39 bird species, and 223 bird-shrub interactions. To calculate species-area curves, we used 104 shrub survey sites and 87 bird survey locations across 12 habitat patches with an areal range of 79.8 km2 to 280.5 km2 (Table S1). The number of species at each habitat patch was 3-12 shrubs, and 1-14 bird species (Table S1). Mean connectance in each patch was 0.33 (sd = 0.8, min = 0.19, max = 0.5) (Table S1).

The z estimates for shrub and bird species, respectively, were 0.56 (95% credible interval

(CI): 0.07-1.04) and 1.09 (0.20-1.97) (Figure 2). Connectance did not significantly change with increasing area (slope = -0.07; 95% CI: -0.45 to 0.32) (Figure S2). Using equation 8, our mean fitted z values (Figure 2), and our fitted intercept and slope for C (Figure S2), the mean slope of our interactions-area curve was predicted to be 1.33 (0.54-2.11). In comparison, the mean slope

40 of the interactions-area curve that we fit to our data is 1.25 (0.91-1.59), which demonstrates that our predicted slope overlaps completely with our empirical slope (Figure 3).

Discussion

Our seed-dispersal case study illustrated that the interactions-area curve is accurately predicted by the theory we have developed. A related but distinct area-interaction equation was proposed by Brose et al. (2004) who combined the species-area relationship with the species- interaction relationship (I = bSu, where b and u are constants, and u = 1 or 2). When u = 1 in the species-interaction relationship, it represents the link-species scaling law (Cohen and Briand

1984), and when u = 2, it represents the constant connectance hypothesis (Martinez 1992). We substitute the species-interaction relationship they used with the more flexible connectance equation (equation 8), and our theorized interactions-area curve fit our case study data better than these other proposed interactions-area curves (Cohen and Briand 1984, Martinez 1992, Brose et al. 2004) (Appendix B Methods and Results, Figure S3). However, our case only illustrates the situation in which connectance is constant across area (Figure S2). Only a few empirical studies have described the relationship between area and interaction richness (ant-plant systems: Sugiura

2010; plant-pollinator systems: Sabatino 2010, Burkle and Knight 2012, seed dispersal systems: de Assis Bomfim et al. 2018), and only two explicitly considered connectance (Sugiura 2010, de

Assis Bomfim et al. 2018). That study found that the connectance of ant-plant networks decreased as island area increased (Sugiura 2010). Whether this is a general pattern, or if the opposite can also occur, has repercussions for how interaction networks respond to habitat loss.

Connectance displays the counterintuitive property of having a higher value when a network comprises very few species than when a network comprises more species. For example,

41 for any given area and minimum interaction richness, if S = 2 for one of the species groups, C =

0.5, and if S = 4 for one of the species groups, C = 0.25. This property of connectance means that

C has the potential to decrease as species are added with increasing area. If connectance does generally decline with increasing area (Sugiura 2010), networks within large areas would incorporate many species that only interact with a few partners. It follows then that as area is lost, these species that only interact with a few partners – specialist species – would have a greater chance of being lost with relatively small area reductions simply because they lose all potential partners. This pattern has been observed in simulations (Fortuna and Bascompte 2006,

Aizen et al. 2012), but has not been investigated in field studies.

Similar to the species-area relationship, our theorized interactions-area curve describes the accumulation of interactions per area. The species-area relationship has been used to describe expected species richness loss with loss of area (e.g., Thomas et al. 2004), but this method has also been criticized (e.g., He and Hubbell 2013). As such, using our interactions-area curve is an approximation of the loss of interactions per area with specific assumptions (e.g., how species are distributed in space) that could result in imprecise estimates if these assumptions are not met

(He and Hubbell 2013). Regardless, like the species-area curve, it could be used to provide an initial estimate for the loss of interactions within a bipartite network, given an amount of habitat loss.

Our theoretical interactions-area curve does not take into account some of the factors that could make networks more robust to habitat loss: abundance, strength of interactions, and the probability of partner switching. The species-area curve only takes into account presence or absence of species so our interactions-area curve only takes into account presence or absence of interactions. While our equations could be used to calculate loss of species interactions across

42 large regions, much as the species-area curve has been used, like the species-area curve it becomes less practical at the local area and at the scale of populations. While some work has been done on the effects of habitat loss on biotic interactions within metapopulations (Fortuna and Bascompte 2006), we know very little about how more abundant species could compensate for lost interactions (Hagen et al. 2012), how species could compensate for lost interactions by switching partners (Kiers et al. 2010, Valiente-Banuet et al. 2014, Barnum et al. 2015), or how strength of interactions varies, or does not, over spatial scales (Schleuning et al. 2011).

Island biogeography relates two factors to the number of species found in any given patch: area and isolation (MacArthur and Wilson 1967). Evidence also suggests that isolation can affect patch occupancy, at least in some cases (e.g., Haddad et al. 2015). Our mathematical expectations for interaction richness over area do not address isolation specifically, and our patches within the Sierra Nevada largely lack isolation. Since the species-area relationship was first described, however, many studies have used the relationship to describe patterns of richness in non-isolated patches (Connor and McCoy 1979, Drakare et al. 2006, Rybicki and Hanski

2013). Further, in a study of a plant-pollinator network (Sabatino et al. 2010), no clear relationship was found between isolation and interaction richness, while area was shown to explain 67% of the variation in patch interaction richness. Clearly, we need more examples before we can draw broad conclusions.

Our theoretical interactions-area curve provides a framework for understanding how biological interactions accumulate with area and how these interactions could be lost with habitat loss. We show that the interactions-area curve is directly tied to the species-area relationships of the groups of species involved. The inclusion of a connectance function distinguishes our interactions-area curve from those previously hypothesized (Cohen and Briand 1984, Martinez

43 1992, Brose et al. 2004). Explorations of how bipartite networks will respond to habitat loss through network properties (e.g., Memmott et al. 2004, Fortuna and Bascompte 2006, Okuyama and Holland 2008) show that general patterns across different types of networks emerge, indicating that our theory should apply generally. Field or lab studies of habitat loss and how it affects interaction richness and the connectance of bipartite networks and food webs are needed to determine both if our theory applies across interaction types and ecosystems and how these networks will respond to the increasing habitat loss facing ecosystems.

Figure 1. All theorized possibilities of the relationship between number of interactions within a bipartite network and area. The solid black lines show the lower (log(!) = max {log('() + 9( ∗ log(*+) , log('/) + 9/ ∗ log(*+)}) and upper bounds (log(!) = log('() + log('/) + (9( + 9/) ∗ log (*+)) for the interactions-area curve over the specified area, based on the number of species in each group within the network (c = 1.2, z = 0.35 for both groups). The dashed line is the maximum slope possible, and traverses from lowest possible connectance to the highest as area increases. The dotted line represents one possible interactions-area curve in which connectance decreases with increasing area. The solid blue line represents a constant connectance value of 0.5, for reference.

45 1.5 1.0 0.5 Number of Species (Log[10]) of Species Number 0.0

7.9 8.0 8.1 8.2 8.3 8.4

Area (Log[10])

Figure 2. Fleshy-fruited shrub species and frugivorous bird species plotted against area for surveyed areas of habitat in the Sierra Nevada Mountains of California, USA. Black dots represent the number of bird species and blue triangles represent the number of shrub species. The black line is the fitted linear relationship between area and bird species. Surrounding dashed black lines are the 95% credible interval. The blue line is the fitted linear relationship between area and number of shrub species. Surrounding dashed blue lines are the 95% credible interval.

46 2.0 1.5 1.0 Log (No of (No Interactions) Log 0.5 0.0

7.9 8.0 8.1 8.2 8.3 8.4

Log (Area)

Figure 3. Interaction richness of fleshy-fruited shrub species and frugivorous bird species plotted against area for surveyed areas of habitat in the Sierra Nevada Mountains of California, USA. Black dots represent the observed empirical number of interactions for a given area. The black line is the statistically fitted linear relationship between area and interaction richness. Surrounding dashed black lines are the 95% credible interval. The grey polygon is the 95% confidence interval of the proposed theoretical interactions-area curve based on Equation 8 and the means of the fitted species-area relationships and connectance.

47 Species-area relationships can be used to predict knock-on species losses

Introduction

The world faces an extinction crisis arising from habitat destruction, invasive species, resource extraction, pollution, and climate change (Ceballos et al. 2015). As we lose species at an increasing rate, concern has grown about how the loss of individual species affects the other species with which they interact (e.g. Dunn et al. 2009, Bellard et al. 2012, Urban et al. 2013).

Species loss due to habitat loss could be exacerbated by secondary extinctions, or knock-on species losses, that result from the loss of biotic interactions (Dunne et al. 2002, Tylianakis et al.

2010, Brodie et al. 2014). Others argue that secondary extinction may be a rarer and more extreme result of the loss of biotic interactions (Kiers et al. 2010, Valiente-Banuet et al. 2014,

Fricke et al. 2016). This question is especially pressing for mutualists, which rely on each other for a critical part of their life history (Christian 2001, Kiers et al. 2010, Aslan et al. 2013).

Globally common examples of mutualisms include plants and their pollinators, plants and their seed dispersers, plants and mycorrhizal fungi, and plants and their ant protectors.

Loss of biotic interactions could have detrimental effects on species populations via reduced fecundity or increased mortality, even if secondary extinctions are rare (Aslan et al.

2013). Most mutualist species have multiple partner species, which ensures ecological redundancy in the case of a loss of one partner species (Koh et al. 2004, Aizen et al. 2012).

Mutualist species and their interactions are often portrayed as a network, with each species as a node and each interaction between a species and its partner a connection (Bascompte et al.

2003). If a species within the network became extinct, its interactions with all partners would be lost. If any of these partner species had no remaining interactions, it too would be set on a path

48 towards possible extinction. Examples from islands like Guam and New Zealand, where bird populations have been decimated by invasive species, demonstrate that the loss of pollinators can significantly reduce fecundity in plant species that cannot or only occasionally self-fertilize

(Mortensen et al. 2008, Anderson et al. 2011). Similar examples also from Guam plus from

Mediterranean islands off the coast of Spain show that the loss of avian and reptilian seed dispersers can cause plant population declines and range contractions of plants due to increased seedling mortality, reduced seedling establishment, or both (Traveset et al. 2012, Rogers et al.

2017). These examples illustrate how altered vital rates can increase the risk of extinction after the loss of all mutualist partners (Anderson et al. 2011, Traveset et al. 2012, Aslan et al. 2013).

Animal species that rely on pollen, nectar, or fruit for nourishment can also suffer increased mortality with the loss of the resources that their partner species provided (van Schaik 1993).

Even when individuals persist, the loss of mutualist interactions can render species functionally extinct because there is no way for the population to recover (Cronk 2016). We label these species that lose all interactions with partner species as “detached species” because they are no longer connected to the network but not necessarily extinct.

The species-area relationship is a widely used theory that has been utilized to predict mathematically the loss of species as habitat area declines (Simberloff 1991, Pimm and Askins

1995, Pimm and Raven 2000). These studies of species loss have focused on habitat destruction, such as deforestation, but species could also lose habitat as climate change forces them to shift their ranges (Peters and Darling 1985, Thomas et al. 2004). Those living at mid- to high- elevations may be especially vulnerable due to running out of habitat at higher elevations (Elsen and Tingley 2015). The species-area relationship has also been shown to predict food web and network properties, such as total number of interactions and species clustering, over several

49 spatial scales (Wood et al. 2015, Sandor et al. unpublished). We extend the species-area relationship to how habitat loss necessarily affects networks, specifically to predict the number of detached species.

Mathematically, the species-area relationship is calculated as S = cAz, where S is the number of species, A is the area of a habitat patch, and c and z are constants describing the intercept and slope, respectively, when plotted on a common log-log scale (Connor and McCoy

1979). In any given ecosystem, z can vary among taxonomic groups, meaning that species losses may differ between the two groups of organisms forming a mutualist network. When the difference in z is greater between the groups, the difference in S is also greater at some, but not always all, area sizes. The loss of a species from the group with fewer species has a disproportionately large effect in terms of the number of detached species on the group with more species. This effect is greater when network connectance, a measure of the average number of interactions per species, is lower.

The number of species expected to be detached following the loss of interacting species depends on connectance (Dunne et al. 2002, Dunne and Williams 2009). Network connectance describes the average number of interactions for each species within the network and ranges from 0, when there are no interactions, to 1, when each species is connected to every species in the partner group (Jordano 1987). Networks with high connectance have many generalist species, while networks with low connectance have many specialist species that interact with few partners. Lower connectance, thus, is predicted to cause more species to become detached from the network as area is lost.

While much concern has been expressed over the role of co-extinctions in the on going global extinction crisis, we still lack a predictive framework to address how habitat loss can lead

50 to the loss of connections in a network and ultimately to co-extinctions (Gonzalez et al. 2011,

Blois et al. 2013). Here, we present a novel framework wherein we use the species-area curve to predict the number of detached species. We use a simulation, based on the general properties of networks, to investigate how the relative number of species in each mutualist group and the connectance of the network affect the number of detached species as area declines. We then use network and species-area curve properties from an empirically-studied avian seed dispersal network to predict the number of species that will become detached as species are lost with declining area. Finally, we simulated area loss in our real-world seed dispersal network and determined the number of detached species for a given habitat size, which we compared to our predictions. Given that real-world mutualist networks, including our seed-dispersal network, typically have low connectance (Bluthgen et al. 2007), we predicted many detached species, with this number accelerating as area declines. Our framework provides an important new tool for quantifying the potential of co-extinctions due to habitat loss.

Methods

Detached species simulation using random networks

We used simulations of randomly-derived plant-animal networks to determine 1) the relationship between the reduction in habitat area and number of detached species, and 2) how this relationship is affected by network connectance and the difference in plant and animal species richness, which was modeled by using different values of z within the species-area relationship equation. To run these simulations, we first chose a series of decreasing areas (5000 km2, 1000 km2, 500 km2, 100 m2, 50 km2, 10 km2, 5 km2, and 1 km2). Our simulations stepped through these areas from largest to smallest, calculating with the species-area relationship the

51 number of species lost at each loss of area (e.g. 5000 km2 to 1000 km2) and the consequent number of species detached from the network. We set c as the mean of the intercepts for plants and animals as determined by linear fits to their full datasets within our system (see description of herbarium and eBird data below), because c is highly variable and does not have a known set of empirical estimates (Drakare et al. 2006).

Each iteration of the simulation used one pair of assigned values for z. Using two interaction groups, group A and group B, we set the z for group A at 0.35. The z for group B cycled through values between 0.15 and 0.35 at increments of 0.01 (i.e., 0.15, 0.16, …, 0.34,

0.35), encompassing most empirical estimates of z (Lomolino 1989), and the global and taxonomic average of 0.27 (Drakare et al. 2006). With our specified c for groups A

(corresponding to the plant group in our dataset) and B (the animal group in our dataset), the difference between the number of species in each partner group when the z for group A equaled

0.35 and the z for group B equaled 0.15 ranged from 4 (smallest area) to 246 (largest area)

(Figure S2).

In each iteration of the simulation we did the following: 1) calculated the number of species in each partner group for each cell size, using the specified species-area relationship; i.e. for our specified c values for each group in addition to the number of species in group A, which stayed constant for each iteration (since the z for group A did not change) and the number of species in group B only changed with z; 2) drew a connectance value from a beta distribution so as to bound it between 0 and 1, with alpha and beta parameters adjusted so that the distribution was centered over the assigned connectance value, to determine the number of interactions for each species in group A; 3) randomly assigned these interactions to specific species in group B to construct the full network; 4) starting with the full network at the largest area, we iteratively

52 subtracted the number of species lost from group A when moving from one area size to the next;

5) randomly selected and removed from the network the number of species calculated in step 4 to simulate these species being lost; 6) repeated steps 4 and 5 for species in group B; 7) counted the number of species in group A that had lost all interactions; 8) repeated steps 3–7 1000 times; 9) repeated steps 2–8 1000 times; 10) repeated steps 2–9 for connectance levels of 0.1, 0.2, 0.3, 0.4,

0.5, 0.6, 0.7, 0.8, and 0.9, producing 1000 random networks at each connectance level; 11) repeated steps 1-10 for each of 21 values for the difference in z, with a minimum difference of 0 when z = 0.35 for both groups and a maximum difference of 0.2 when z = 0.35 for group A and

0.15 for group B; finally, 12) we compiled the mean of the mean number of detached plant species for each connectance level for each pair of assigned values for z. This overall mean represents the number of species that would become detached from the full network for each increment of area lost. We then divided the mean number of detached species at each step by the area lost to get the number of detached species per km2 (Figure 1).

Study system and real-world network construction

We sampled shrubs and avian frugivores from the central Sierra Nevada Mountains,

California, USA. Our study region ranged from 123° to 117.5° W and 34° to 43° N, between

~1000 m and 4000 m elevation (Figure S1). The east and west side of the mountains, divided by the Sierra Nevada crest, experience largely distinct precipitation regimes. Depending on elevation, the west side receives 75 to 200 cm of precipitation per year, and the east side receives

<25 to 100 cm per year. Typically, much of this precipitation falls as snow during winter.

The Sierra Nevada are part of the California floristic province, a biodiversity hotspot

(Myers et al. 2000) because of high plant species diversity, rates of endemism, and beta diversity

53 (Baldwin et al. 2012). We studied the interaction network between shrubs with fleshy or semi- fleshy bird-dispersed fruits and avian frugivores. A high diversity of fleshy-fruited shrubs is found in the Sierra Nevada: 100 native species from 15 families, 7 species of which are endemic to the mountain range or to California (Table S1). While several mammal species consume the fruits of many these shrubs, the seed-disperser effectiveness of mammals in North America is largely unknown whereas birds are known to be effective dispersers (Willson 1993).

We specified a seed dispersal network for all of the central Sierra Nevada using data from a literature search, citizen science observations, preserved stomach collections, plus our own camera traps and fecal collections. Information on species-specific interactions was not always available, and fruits of the same genus often have similarly sized seeds and are of a similar quality from the perspective of birds (Stiles 1980). As such, we assumed that if a bird species was listed in the literature as consuming a particular genus, it consumes all available species in that genus (Morales-Castilla et al. 2015, Rezende et al. 2007). Bird species were included in our seed dispersal network if they were recorded as consuming fruits through one of our approaches.

Plant genera were included in our network if they were angiosperms and shrubs or small trees and if the fruits were known to be consumed by birds.

For the literature search, we systematically searched the BIOSIS, Zoological Record, and

Scopus databases for the phrase "diet" OR "fruit" OR "frugivory" OR "seed dispersal" OR

"fruigivore", and refined the results displayed by each frugivorous bird species separately. All searches were conducted from May-August 2017. We also recorded the fruit species identified as being consumed by each bird species in the Birds of North America monograph series

(Rodewald 2015).

We created a submission portal on iNaturalist to which citizen-scientists can submit

54 photographs of birds eating fruit (https://www.inaturalist.org/projects/birds-and-berries). We solicited participants through a searchable database of citizen science projects (SciStarter), targeted posts on California birding listservs, and posted signs in Sierra Nevada campgrounds and visitor centers including Yosemite National Park.

In July and August of 2014 and 2015 we used motion-activated cameras to capture birds eating the fruit of key plants at the University of California Natural Reserve System’s Valentine

Eastern Sierra Reserve (VESR), in Mammoth Lakes, CA. We used Bushnell Trophy Cam HD

Trail Cameras and BirdCam PRO Motion-Activated Digital Wildlife Cameras, moving cameras among plant species on a weekly basis.

We used mist-nets to capture birds and collect fecal samples at VESR between July 17th and August 22nd, 2015. We also received fecal samples collected by the Institute for Bird

Populations at their five Monitoring Avian Productivity and Survival (MAPS) program stations in Yosemite National Park (http://www.birdpop.org/pages/mapsMap.php) between June 11th and

August 5th, 2015 and 2016, and by six additional MAPS bird-banding stations in Cranebrake

Ecological Reserve (southern Sierra Nevada), and near Lassen National Park and Truckee,

California (northern Sierra Nevada) between May 14th and August 11th, 2016. Trapped birds were extracted, held in cloth bird bags until they produced feces, fitted with an aluminum United

States Geological Survey leg band, and measured. Fecal samples were placed in vials containing

70% ethanol or frozen the same day. In the lab, we examined each sample for whole seeds, which we identified to genus or species morphologically using reference photographs compiled at the California Department of Food and Agriculture Seed Herbarium and from CalPhotos

(http://calphotos.berkeley.edu).

55 Finally, we sorted through the preserved stomach contents (n = 260) of all frugivorous bird species that occur in the Sierra Nevada region in the extensive collection of preserved bird stomachs at the Louisiana State University Museum of Natural History. We identified all seeds to genus or species using morphology, as we did with the fecal samples.

We calculated connectance of our real world network as C = I/(AP), where C is connectance, I is the number of interactions , A is the total number of animal species, and P is the total number of plant species (Jordano 1987).

Habitat loss and detached species in the Sierra Nevada

The Sierra Nevada contain a high number of conservation areas protected at different levels of allowed use from wilderness areas to multiple use. These conservation areas do not protect species from the effects of climate change, however, nor do all of them completely protect against certain types of land use change, including logging and cattle pasturing. To estimate what networks in areas of different size look like, we subsampled our study region at different spatial resolutions. This approach does not directly mimic habitat fragmentation and provides a conservative estimate of species losses with reduced area because it does not account for demographic process and other mechanisms that cause species to disappear from smaller patches. To subsample our study region, we placed a series of hexagonal grids, of decreasing area (5000 km2, 1000 km2, 500 km2, 100 m2, 50 km2, 10 km2, 5 km2, and 1 km2) over the Sierra

Nevada region. The largest cell size is approximately double the size of Yosemite National Park, and the smallest grid cell size is 100 ha.

56 Habitat patch networks: herbarium and eBird data

We compiled herbarium records and shrub observations from California using the Global

Biodiversity Information Facility (GBIF), using our list of 91 native, fleshy-fruited shrub species

(Table S1) and the R package “rgbif” (Chamberlain 2017), selecting only those with a collection date, geographic coordinates, and marked as not having geospatial issues. Using the R packages

“sp” (Pebesma and Bivand 2005) and “rgdal” (Bivand et al. 2017), we eliminated all records that did not fall within the Sierra Nevada ecoregion (Baldwin et al. 2012). We also eliminated any that occurred before 1950. We used the R package “vegan” (Oksanen et al. 2018) to create a collector’s species accumulation curve to determine the number of years required for the curve to plateau, starting at 2015 and working backwards in time (Figure S3). All but three species appeared in our records by 1990 so we restricted the records to only those collected from 1990-

2015 (Figure S3), leaving us with 3182 records representing 83 species (Table S1). We sorted all herbarium records into the grid cells in which they were contained so that at each grid cell size of a certain area, we created a list of shrub species contained within each grid cell.

For birds, we downloaded eBird data (Sullivan et al. 2009), restricting records to the state of California and to our species of interest (Table S2). From this dataset we extracted April to

September records to only use observations from times when plants were fruiting. We further reduced the records to those collected from 1990-2015 to match the herbarium data, and to locations within the Sierra Nevada, using the clip tool in QGIS 2.18, leaving 339,676 records representing 64 species. We sorted all eBird records so that at each grid cell size of a certain area we created a list of bird species contained within each grid cell.

To determine how species richness of shrubs and birds relates to survey area, we used simple linear regressions of grid cell species richness versus cell area, using log-transformed data

57 to estimate the intercept (c) and slope (z) for each group. The terms c and z were given vague priors with a mean of 0 and standard deviation of 0.001. The two models were fit in JAGS

Detached species in a real-world seed-dispersal network

We calculated the predicted number of detached species, given our network, using a version of our random network simulation where we instead used a single value for connectance and each z, defined by the real network. We fixed the connectance at 0.19, the value for our full network, and held the values for z constant at 0.34 for shrubs and 0.37 for birds, based on the fit species-area relationship (see Results). From this simulation, we estimated the number of detached species at each loss of area step.

To estimate the number of detached species using our herbarium and eBird datasets as well as simulated area loss, we first had to determine which grid cells contained herbarium and eBird data, due to the patchiness of both datasets. We used QGIS to create “paths” by which to move through nested grid cells, from any given largest grid cell to a second largest grid cell contained within it, all the way down to a smallest grid cell. For any given path, we merged plant and bird species lists for the largest grid size as well as with our full network to pare down the network to just the species found at that particular grid cell. We then simulated habitat loss by removing the plant and bird species not found at the second to largest grid cell and tallying the number of resulting detached species, if any. We repeated this procedure for each grid size, and each path. We followed a total of 46 paths of grid cell sizes from large to small.

58 Results

Random network simulation

The number of detached species resulting from simulated habitat loss was greatest when connectance was low and the difference in z between the groups was high. When connectance was > 0.7, species generally did not become detached from the network following species extinctions (Figure 2). As connectance decreased below this level, the number of detached plant species increased, connectance values ≤ 0.3 showed the greatest number of detached species, with a mean of 0.5-3.4 species per km2 of area lost (Figure 2). As the difference in z increased, the number of species that become detached also generally increased, although the mean number of detached species when the difference in z was 0.2 and connectance was 0.8 or 0.9 was generally lower than the mean number of detached species when z did not differ and connectance was low (0.1 or 0.2; Figure 2). The calculated standard deviation ranged up to 5 spp per km2 at low connectance and high difference in z (Figure 2), indicating that the underlying structure of the network, not just connectance, had a large effect on the number of detached species.

Network construction

We tallied a total of 1022 bird-shrub interactions in our Sierra Nevada seed dispersal network (Table S3, Figure S4). Of this total, 119 were based on direct observations of a bird species consuming the fruit of a plant species, while the remainder were assigned based on observations where the fruit could be assigned to genus but not species, and the assumption that a bird species that consumes fruit from one species within the genus, it will also consume the fruit of all other species in the genus. These 1022 linkages were generated through citizen science observations (n = 23), camera traps (n = 3), fecal collections (n = 42), preserved stomach

59 collections (n = 4), or from references in the literature (n > 900), with many linkages duplicated across methods.

Species-area relationship and detached species from our empirical network

Our fitted z for the plant species was 0.38 (95% credible interval (CI): 0.33 - 0 .43), with a c value of -2.39 (-2.77 to -2.02). Our fitted z for the animal species was 0.36 (0.32 - 0.41), with a c value of -1.53 (-1.89 to -1.17) (Figure 3).

Given a connectance of 0.19 for the full network, based on the measured connectance of our real-world network, we predicted that at each loss of area, a mean of 0-0.5 plant spp/10 km2

(max of 0.5-2 plants spp/10 km2) could become detached from the network (Figure 4).

We found that the number of detached shrub species at each simulated loss of area spanned a wide range of values, with most resulting in 0 detached species and one simulated loss of area resulting in up to 2 detached species. Mean detached species ranged from 0 to 1 spp/10 km2 (Figure 4). The network was more resilient than predicted at large area sizes, with species losses from the network resulting in few to no detached species. However, the network was prone to higher levels of detached species than predicted at smaller area sizes (Figure 4).

Discussion

The species-area relationship has been used to predict the number of species that will go extinct from habitat loss, but species loss is likely underpredicted by not incorporating biotic interactions, such as mutualisms (Lewis 2006, Aizen et al. 2012, Urban et al. 2013, Brodie et al.

2014). Through simulation, we have shown that over three species can become detached from mutualist networks per km2 of habitat lost, resulting in a loss of mutualist function which could

60 ultimately lead to extinction of the detached species. Even if secondary extinction is not the ultimate result of a species becoming detached, the species could still experience detrimental and population-level reductions in fecundity (Mortensen et al. 2008, Anderson et al. 2011, Traveset et al. 2012, Aslan et al. 2013, Rogers et al. 2017). If most detached species become secondarily extinct, the detachment of species from mutualist networks is expected to be the major source of secondary extinctions which is likely to mean the underprediction of the current global biodiversity crisis (Koh et al. 2004, Colwell et al. 2012, Blois et al. 2013, Brodie et al. 2014,

Wood et al. 2015).

One of the main results from the simulations is that as the difference between z increases between the two interacting groups, more species become detached from the network. This result relates to empirical findings that more diverse food webs and networks are more resilient to secondary extinctions than less diverse ones (Menge 1995, Montoya et al. 2006, Sanders et al.

2018), as well as the disproportionate effects of the loss of a top predator on food webs (e.g.

Paine 1969). More detached species with a greater difference in z also relates to the number of interactions of each species within the network. Connectance summarizes the average number of links per species within a given network, but number of interactions per species is highly variable within any network, with some species interacting with most species within the network, and some species interacting with only a few species within the network. In two networks with the same connectance, the network with a greater difference in z between mutualist groups will have fewer links overall, making species more vulnerable to becoming detached (Figure 2).

Another result of our detached species analysis was that networks were relatively robust to species loss until a point at which they collapsed. In comparison to our real-world network, we overpredicted the number of detached species at larger areas and underpredicted the number of

61 detached species at smaller areas. At the smallest area size, all networks only contained one or two plant species. In 14 of these networks, the remaining plant species or one of the two remaining plant species became detached. In 3 of these networks, both of the remaining plant species became detached. That networks are resilient to loss of species until a collapse point has been found in other species-loss simulations (e.g. Sole and Montoya 2001, Dunne et al. 2002,

Memmott et al. 2004, Rezende et al. 2007, Srinivasan et al. 2007). One property of bipartite networks, those that contain two groups of interacting species, that explains this initial resilience is nestedness (Bascompte et al. 2003, Verdu and Valiente-Banuet 2008, Thebault and Fontaine

2010), wherein a few species within the network are more highly connected than other species.

When one of these well-connected species is removed from the network, many connections are lost (Memmott et al. 2004, Bascompte and Stouffer 2009). For our network, these well- connected bird species included American robin (Turdus migratorius) and Townsend’s solitaire

(Myadestes townsendii). Their presence kept many plant species connected, and with only a few exceptions, their absence led to species being detached. This resilience that our and other studies have found indicates that networks may persist without detached species, or secondary extinctions, for large area losses, but with enough area lost, the entire network could collapse.

Although it is often assumed that detachment from a network leads to secondary extinctions, we often do not know for certain what happens to species that become detached from their mutualist partners. Some plant species have mysteriously persisted well after their presumed dispersers have gone extinct (e.g. the Kentucky coffee tree Gymnocladus dioicus, which is distributed widely across the southeastern U.S., and for which extinct megafauna are the presumed disperser, despite some current water dispersal; Zaya and Howe 2009). For some plant species, not enough time has passed for the full repercussions of disperser loss to be seen

62 (Valiente-Banuet et al. 2015). For example, tree species in Guam that lost their avian dispersers to the brown tree snake could persist for many decades as an integral part of the forest, even if seedling recruitment has dropped below a sustainable level for the populations (Caves et al.

2013). Further, the extent to which a mutualist depends on its partners affects the impact the loss of all partner species has on that mutualist species (Fricke et al. 2016). A bird species that is occasionally or opportunistically frugivorous should suffer less at the loss of all partners in comparison to a bird species that has a diet comprised mostly of fruit.

One way in which detached species could be re-integrated into a network is through link switching, where a species that previously did not interact with a given partner species begins to do so (Kiers et al. 2010, Valiente-Banuet et al. 2014, Barnum et al. 2015). Link switching often occurs in generalist species, and can be a response to greater abundance of a given species, or resource that the species provides (Hagen et al. 2012). In seed dispersal or pollination networks, if link switching occurs, the critical seed dispersal or pollination services that a plant receives would be restored, rescuing the species from extinction.

Our predictions for detached species resulting from area loss likely represent overly optimistic estimates for detached species. There are several reasons for assuming optimism.

First, we assume perfect substitutability of dispersers, assuming that all bird species are equally effective at providing dispersal services for a linked plant. In many cases a subset of the dispersers within a network provides more dispersal services than the others, meaning that the identity of disperser species lost matters (e.g. Vazquez et al. 2007, Schupp et al. 2010, Costa

Viera and Almeida-Neto 2015). For example, in our empirical network cedar waxwing

(Bombycilla cedrorum), American robin (Turdus migratorius), and Townsend’s solitaire

(Myadestes townsendii) have a disproportionate impact due not just because of their wide diet

63 breadth but also because of their high rate of frugivory (Cohen et al. 2009, Carlisle et al. 2012).

Second, our predictions assume that disperser abundance has no effect on the strength of an interaction so negative allee effects are ignored. For example, a study of fruit dispersal by flying foxes on a Pacific archipelago found that below a certain threshold of abundance, flying foxes were functionally absent as seed dispersers (McConkey and Drake 2006). In our system, several species of birds are infrequent visitors to certain areas or elevations of the Sierra Nevada

Mountains. Conversely, robins, solitaires, and waxwings again likely have a disproportionately large effect because they are also more abundant than many other potential dispersers. Third, where species-specific information in the literature was lacking we assumed a disperser consumed all species within the same genus. This assumption could have inflated the number of interactions within the network and for certain species. A species is at a greater chance of becoming detached with fewer interactions so through this assumption we could have reduced the risk of detachment. Together, all three of these assumptions mask probable additional loss of functional dispersal services when remaining dispersers engage in only occasional frugivory, are at low abundances, or do not consume all species within a genus.

Previous studies have approached the question of quantifying co-extinctions in a variety of ways, including using linear models (e.g. Stork and Lyal 1993 and others reviewed in Moir et al. 2010), randomly-chosen species eliminations (Koh et al. 2004), eliminations in increasing and decreasing order of number of connections (Memmott et al. 2004), metacommunity effects

(Fortuna and Bascompte 2006), geographic prevalence (Srinivasan et al. 2007), and historical precedence (Strona and Lafferty 2016). Our work extends the species-area relationship to quantify the expected number of knock-on species lost, or detached, with habitat loss. We add the network property connectance to quantify its effect on co-extinctions within our species-area

64 framework. The number of detached species depends heavily on connectance, with low connectance networks more at risk, as well as the difference in number of species between interacting groups, with networks with greater disparity between species numbers also more at risk. Our results additionally suggest that networks may be robust to species loss initially, but once enough habitat has been lost, high levels of detached species result.

65 Figures

Figure 1. One iteration of a simulation to calculate the number of detached species with area loss. Each hexagon with associated square kilometers represents one area size. The number of birds and shrubs above each hexagon represents the number bird and shrub species contained within that area size, based on the species-area relationship. Arrows represent a simulated decrease in habitat by moving from one area size to next largest area size and the associated loss of bird and shrub species (red x’s) as well as detached shrub species (blue x’s).

Figure 2. A) Mean of maximum, B) mean of mean, and C) standard deviation of the mean of the number of detached species per area lost as predicted by random network simulations. Each dot represents the number of detached species per km2 of lost habitat, as estimated for connectance values ranging from 0.1 to 0.9 and for different slopes of the species-area curve (z), where z = 0.35 for plants and varies between 0.15 and 0.35 for animals.

Figure 3. Fit species-area curves for fleshy-fruited shrubs (left) and frugivorous birds (right) found in the Sierra Nevada mountains, California, USA. Shrub data are herbarium records and observations downloaded from the Global Biodiversity Information Facility (GBIF). Bird data are downloaded from eBird. Each point is the number of species reported in any given area. The mean slopes (z) are 0.36 for shrubs and 0.38 for birds. Solid blue lines show the mean relationship between area and number of species. Dashed blue lines are the 95% credible intervals around the mean.

68 0.5 0.4 0.3 0.2 Number of Detached Species/km^2 of Detached Number 0.1 0.0

2 3 4 5 6 7 8

Area Lost (km^2) (log)

Figure 4. Number of predicted vs. actual detached species by simulated reduction in area. Predictions are based on simulations of a network with connectance of 0.19, c constant, and z taken from the species-area relationships for fleshy fruit-producing shrubs and frugivorous birds from the Sierra Nevada mountains, California, USA (see Figure 3). Black dots represent the mean of the predicted number of detached species per km2 in the network. Black triangles represent the mean of the maximum number of detached species per km2 predicted in the network. Vertical bars represent 95% confidence intervals around the mean of the predicted number of detached species per km2. Red dots represent the mean number of detached species per km2 at each simulated reduction in area. Red triangles represent the maximum number of detached species per km2, over 46 simulations, at each simulated reduction in area.

69 Appendix A – Supporting Information for Chapter 1

Model Code For the combined presence-absence, presence-only model used in the analysis of shrub species distributions in the Sierra Nevada Mountains, California, USA.

#priors for across-species variables delta ~ dnorm(0, 0.001) delta2 ~ dnorm(0, 0.001)

#priors for individual species variables, where each k is a species for(k in 1:m){ A0[k] ~ dnorm(0, 0.001) A1[k] ~ dnorm(0, 0.001) A2[k] ~ dnorm(0, 0.001) A3[k] ~ dnorm(0, 0.001) A4[k] ~ dnorm(0, 0.001) A5[k] ~ dnorm(0, 0.001) }

#for the presence-only data and quadrature points for(i in 1:sPOBG){ yAbb1POBG[i,1] ~ dpois(L[i]) L[i] <- wgt[i]*lambda[i] log(lambda[i]) <- A0[SpNumPOBG[i]]+A1[SpNumPOBG[i]]*yAbb1POBG[i,5]+A2[SpNumPOBG[i]]*yAbb1POBG[i,6]+A3[Sp NumPOBG[i]]*yAbb1POBG[i,7]+A4[SpNumPOBG[i]]*yAbb1POBG[i,8]+A5[SpNumPOBG[i]]*yAbb1POBG [i,9]+delta*yAbb1POBG[i,3]+delta2*yAbb1POBG[i,4] }

#for the presence-absence data for(j in 1:sPA){ yAbb1PA[j,1] ~ dbern(theta[j]) logit(theta[j]) <- A0[SpNumPA[j]]+A1[SpNumPA[j]]*yAbb1PA[j,5]+A2[SpNumPA[j]]*yAbb1PA[j,6]+A3[SpNumPA[j]]*yAbb1 PA[j,7]+A4[SpNumPA[j]]*yAbb1PA[j,8]+A5[SpNumPA[j]]*yAbb1PA[j,9] }

70 Table S1. Environmental and bias covariates included in the shrub species distribution models for the Sierra Nevada Mountains, California, USA. The sources of the data for each covariate are listed.

Table S2. Results of a principal component analysis of precipitation variables from the Sierra Nevada Mountains, California, USA. Rows 1-4 are the input variables and rows 5 and 6 summarize the proportion of variance explained by the principal components.

PC1 PC2 PC3 PC4 Standard Deviation of 0.501 -0.450 -0.739 0.004 Annual Precipitation Winter Precipitation 0.507 -0.271 0.511 0.639 Mean Annual 0.510 -0.089 0.395 -0.759 Precipitation Growing Season 0.482 0.846 -0.188 0.127 Precipitation Proportion of Variance 0.9583 0.03791 0.00376 0 Cumulative Proportion 0.9583 0.99624 1 1

72 Table S3. Results of a principal component analysis of temperature variables from the Sierra Nevada Mountains, California, USA. Rows 1-8 are the input variables and rows 9 and 10 summarize the proportion of variance explained by the principal components.

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Mean of Daily -0.378 0.006 -0.035 -0.344 0.271 -0.214 0.223 0.754 Maximum Temperature Mean of Daily -0.376 -0.059 0.166 0.348 0.258 0.617 0.501 -0.087 Minimum Temperature Maximum -0.370 0.053 0.413 0.328 0.385 -0.578 -0.169 -0.269 Temperature Minimum -0.376 -0.119 0.020 -0.436 0.222 0.407 -0.643 -0.159 Temperature Minimum Maximum -0.373 0.008 0.333 0.264 -0.729 0.071 -0.233 0.299 Temperature Maximum Minimum -0.377 -0.031 -0.021 -0.512 -0.360 -0.209 0.435 -0.482 Temperature Standard Deviation 0.317 0.476 0.721 -0.344 0.048 0.152 0.088 0.032 of Daily Minimum Temperature Standard Deviation -0.236 0.867 -0.411 0.114 -0.010 0.043 -0.076 -0.054 of Daily Maximum Temperature Proportion of 0.8698 0.09484 0.03012 0.00456 0.00042 0.00011 0.00007 0.00004 Variance Cumulative 0.8698 0.96468 0.99480 0.99936 0.99978 0.99989 0.99996 1 Proportion

Table S4. Maximum TSS and Kappa scores of model fit for each species. TSS is measured on a scale of 0 to 1, and Kappa is measured on a scale of -1 to 1.

Species max TSS max Cohen's Kappa Group Arctostaphylos manzanita 0.27 0.07 1 (a) Arctostaphylos nevadensis 0.26 0.29 1 (a) Arctostaphylos patula 0.11 0.12 1 (a) Arctostaphylos viscida 0.67 0.59 1 (a) Prunus virginiana 0.26 0.26 1 (a) Ribes nevadense 0.19 0.19 1 (a) Ribes roezlii 0.11 0.10 1 (a) Ribes viscosissimum 0.19 0.22 1 (a) Rosa gymnocarpa 0.07 0.07 1 (b) Rubus leucodermis 0.27 0.20 1 (b) Sambucus nigra 0.20 0.14 1 (b) Symphoricarpos mollis 0.19 0.17 1 (b) Toxicodendron diversilobum 0.14 0.14 1 (b) Umbellularia californica 0.12 0.13 1 (b) Vaccinium uliginosum 0.19 0.25 1 (b) Cornus nuttallii 0.28 0.28 2 Lonicera conjugialis 0.10 0.11 2 Lonicera involucrata 0.19 0.19 2 Ribes inerme 0.50 0.66 2 Ribes montigenum 0.08 0.10 2 Rubus parviflorus 0.32 0.28 2 Vaccinium cespitosum 0.22 0.25 2 Amelanchier alnifolia 0.17 0.19 3 Cornus sericea 0.22 0.27 3 Frangula rubra 0.30 0.32 3 Prunus andersonii 0.01 0.01 3 Prunus emarginata 0.43 0.46 3 Ribes cereum 0.36 0.30 3 Rosa woodsii 0.26 0.21 3 Symphoricarpos rotundifolius 0.10 0.10 3

74 calinski K-means partitions comparison criterion 6 6 5 5 4 4 3 3 Number of groups in each partition each in of groups Number 2 2

20 40 60 80 100 17 18 19 20

Objects Values

Figure S1. Left: K-means partitioning of fleshy-fruited shrub species in the Sierra Nevada, California, USA, over 100 iterations, to divide our 30 species into groups for species distribution modeling. Right: Number of groups identified (3) is denoted in red based on the Calinski- Harabasz criterion.

Figure S2. Coefficient values for precipitation and temperature for models describing current distributions of all shrub species found in the Sierra Nevada Mountains, California, USA. Dots represent the mean value of the posterior distribution for each species and error bars show the 95% credible interval for each species. Significant coefficient values, those in which the 95% credible interval does not cross 0, are blue. Coefficients were standardized across all species. Each dot with corresponding error bar represents one of the species included in our analyses. Species are organized from left to right based on the center of their distribution along a west-east axis: Arctostaphylos manzanita, Arctostaphylos nevadensis, Arctostaphylos patula, Arctostaphylos viscida, Prunus virginiana, Ribes nevadense, Ribes roezlii, Ribes viscosissimum, Rosa gymnocarpa, Rubus leucodermis, Sambucus nigra, Symphoricarpos mollis, Toxicodendron diversilobum, Umbellularia californica, Vaccinium uliginosum, Cornus nuttallii, Lonicera conjugialis, Lonicera involucrata, Ribes inerme, Ribes montigenum, Rubus parviflorus, Vaccinium cespitosum, Amelanchier alnifolia, Cornus sericea, Frangula rubra, Prunus andersonii, Prunus emarginata, Ribes cereum, Rosa woodsia, Symphoricarpos rotundifolius.

Figure S3. Coefficient values for models describing current distributions of all shrub species found in the Sierra Nevada Mountains, California, USA. Dots represent the mean value of the posterior distribution for each species and error bars show the 95% credible interval for each species. Significant coefficient values, those in which the 95% credible interval does not cross 0, are blue. Coefficients were standardized across all species. Upper: Coefficient values for distance to water variable. Species are organized from left to right based on the center of their distribution along a west-east axis: Arctostaphylos manzanita, Arctostaphylos nevadensis, Arctostaphylos patula, Arctostaphylos viscida, Prunus virginiana, Ribes nevadense, Ribes roezlii, Ribes viscosissimum, Rosa gymnocarpa, Rubus leucodermis, Sambucus nigra, Symphoricarpos mollis, Toxicodendron diversilobum, Umbellularia californica, Vaccinium uliginosum, Cornus nuttallii, Lonicera conjugialis, Lonicera involucrata, Ribes inerme, Ribes montigenum, Rubus parviflorus, Vaccinium cespitosum, Amelanchier alnifolia, Cornus sericea, Frangula rubra, Prunus andersonii, Prunus emarginata, Ribes cereum, Rosa woodsia, Symphoricarpos rotundifolius. Each dot with corresponding error bar represents one of the species included in our analyses. Bottom left: Coefficient values for distance to road variable. Bottom right: Coefficient values for slope (of land) variable. Distance to road and slope (of land) variables are fit per species group. Each point with associated error bar is a single group. From left to right for each plot: group 1(a), group 1(b), group 2, group 3.

Figure S4. Future (RCP 4.5) species richness of fleshy-fruited shrubs within the Sierra Nevada Mountains, California, USA. Yosemite National Park is outlined on the map.

Figure S5. Left: Future (RCP 4.5) species richness minus current species richness of fleshy-fruited shrubs within the Sierra Nevada Mountains, California, USA. Yosemite National Park is outlined on the map. Right: Elevation versus the change in species richness from current to future. Each point is one quadrature. Line is LOESS smoothing average.

Figure S6. Left: Species turnover from current to future (RCP 4.5) in the Sierra Nevada Mountains, California, USA, as calculated by the Sorensen dissimilarity index. Yosemite National Park is outlined on the map. Right: Elevation versus species turnover from current to future. Each point is one quadrature.

80 0.10 0.05 0.00 -0.05 Suitability Difference for cedar waxwing for cedar Difference Suitability -0.10

-15 -10 -5 0 5 10

Shrub Species Richness Difference

Figure S7. Change in shrub species richness between current and future (RCP 8.5) scenarios as compared with the difference in suitability for the cedar waxwing (GFDL Scenario A2), a highly frugivorous species, by 1000 m elevation band, within the Sierra Nevada Mountains, California, USA. Zero change is delimited for the bird and shrub species by lines. The top left quadrant is a positive change in suitability for cedar waxwings, but a loss of species richness for shrubs. The bottom right quadrant is a gain in species richness for shrubs, but a loss in suitability for cedar waxwings. The top right quadrant (gain in suitability and species richness) and bottom left quadrant (loss in suitability and species richness) indicate changes of the same direction. Patterns for CCM Scenario A2 are comparable to GFDL Scenario A2. Points are colored by elevation: 0- 499 m (black), 500-999 m (grey), 1000-1499 m (orange), 1500-1999 m (yellow), 2000-2499 m (green), 2500-2999 m (cyan), 3000-3499 m (blue), 3500-4000 m (purple).

81 0.2 0.0 -0.2 Suitability Difference for American Robin for American Difference Suitability -0.4

-15 -10 -5 0 5 10

Shrub Species Richness Difference

Figure S8. Change in shrub species richness between current and future (RCP 8.5) as compared with the difference in suitability for the American robin (GFDL Scenario A2), a highly frugivorous species, within the Sierra Nevada Mountains, California, USA. Zero change is delimited for the bird and shrub species by lines. The top left quadrant is a positive change in suitability for American robins, but a loss of species richness for shrubs. The bottom right quadrant is a gain in species richness for shrubs, but a loss in suitability for American robins. The top right quadrant (gain in suitability and species richness) and bottom left quadrant (loss in suitability and species richness) indicate changes of the same direction. Patterns for CCM Scenario A2 are comparable to GFDL Scenario A2. Points are colored by elevation: 0-499 m (black), 500-999 m (grey), 1000-1499 m (orange), 1500-1999 m (yellow), 2000-2499 m (green), 2500-2999 m (cyan), 3000-3499 m (blue), 3500-4000 m (purple).

0.6 0.4 0.2 0.0 -0.2 Suitability Difference for Townsend's solitaire for Townsend's Difference Suitability -0.4 -0.6

-15 -10 -5 0 5 10

Shrub Species Richness Difference

Figure S9. Change in shrub species richness between current and future (RCP 8.5) as compared with the difference in suitability for the Townsend’s solitaire (GFDL Scenario A2), a highly frugivorous species, by 1000 m elevation band, within the Sierra Nevada Mountains, California, USA. Zero change is delimited for the bird and shrub species by lines. The top left quadrant is a positive change in suitability for Townsend’s solitaires, but a loss of species richness for shrubs. The bottom right quadrant is a gain in species richness for shrubs, but a loss in suitability for Townsend’s solitaires. The top right quadrant (gain in suitability and species richness) and bottom left quadrant (loss in suitability and species richness) indicate changes of the same direction. Patterns for CCM Scenario A2 are comparable to GFDL Scenario A2. Points are colored by elevation: 0-499 m (black), 500-999 m (grey), 1000-1499 m (orange), 1500-1999 m (yellow), 2000-2499 m (green), 2500-2999 m (cyan), 3000-3499 m (blue), 3500-4000 m (purple).

Figure S10. Each fleshy-fruited shrub species in the Sierra Nevada, California, USA, and their distribution within Yosemite National Park. Purple indicates where current (red) and future (blue) % of Yosemite occupied by each species overlap. Species bars where red is visible are projected to lose part of their distribution within Yosemite. Species where blue is visible are projected to gain distribution within Yosemite. RCP 4.5 was used for future climate.

Figure S11. Differences in distribution within Yosemite National Park, by species. White columns are the differences between future (RCP 4.5) and current distributions. Dark grey columns are the differences between future (RCP 8.5) and current distributions. Green indicates a gain in species distribution and yellow indicates a loss. Greys indicate no change in distribution: light grey indicates that the species is present at a location currently and is predicted to be there in the future, and medium grey indicates that the species is not present currently and is not predicted to be there in the future. Species with near-entire losses in future (RCP 4.5 and RCP 8.5): Arctostaphylos nevadensis, Vaccinium uliginosum, Lonicera conjugialis, Ribes montigenum, Vaccinium cespitosum, Amelanchier alnifolia, Prunus emarginata; near-entire losses in future (only RCP 8.5): Sambucus nigra. Species with near-entire gains in future (RCP 4.5 and RCP 8.5): Rosa gymnocarpa, Rubus parviflorus.

85 Appendix B – Supporting Information for Chapter 2

Methods and Results

Fitting additional theoretically hypothesized interactions-area relationships

The link-species scaling law (L = bSu), where L is the total number of interactions, b is a constant, S is the total number of species within the network (summed number of species in groups 1 and 2), and u is a constant, has been proposed as the relationship between the number of interactions and the number of species within a food web or network (Cohen and Briand 1984,

Martinez 1992). Considerable debate has surrounded whether u is equivalent to 1 or 2 (Cohen and Briand 1984, Martinez 1992, Bersier and Sugihara 1997, Schmid-Araya et al. 2002). Brose et al. (2004) proposed combining this relationship with the species-area curve to produce an interactions-area relationship

L = bcuAuz, Equation S1

where b and u come from the link-species scaling law, and c, A, and z are from the species-area relationship.

To compare the predictions of our Equation 8 to those using Equation S1, we log- transformed Equation S1 and fit linear regressions to the total number of species (both shrub and bird) by area and to the number of interactions by total number of species. All regressions were performed in a Bayesian framework with JAGS (Plummer 2003), using vague normal priors for slope and intercept terms with a mean of 0 and standard deviation of 0.001, three chains per analysis for 50,000 iterations each, and the R package ‘R2jags’ (Su and Yajima 2015). We

86 checked for convergence by ensuring the scale reduction factors approached one for all parameters (Gelman and Rubin 1992). We then used the full posterior distribution of c and z from the total species-area regression as well as the full posterior distribution of b from the interactions-total species regression to calculate Equation S1 for values of u = 1 and 2. Our fit value for u was 1.31 (95% CI = 1.08-1.56). Our calculated interactions-area relationships based on Equation S1 only overlapped part of the interactions-area curve for both u = 1 and u =2

(Figure S3). Observed data for nine of our 12 patches lay within the 95% CI of our theoretical interactions-area curve, whereas only 5 and 7 patches were contained within the 95% CI of the curves based on Equation S1 using u = 1 and 2, respectively (Figure S3).

References

Bersier, L.F., and G. Sugihara. 1997. Scaling regions for food web properties. Proceedings of the National Academy of Sciences 94: 1247-51.

Brose, U., A. Ostling, K. Harrison, N. D. Martinez. 2004. Unified spatial scaling of species and their tropic interactions. Nature 428: 167-71.

Cohen, J. E., and F. Briand. 1984. Trophic links of community food webs. Proceedings of the National Academy of Sciences 81: 4105-09.

Martinez, N. D. 1992. Constant connectance in community food webs. The American Naturalist 139: 1208-18.

Schmid-Araya, J. M., P. E. Schmid, A. Robertson, J. Winterbottom, C. Gjerlov, A. G. Hildrew. 2002. Connectance in stream food webs. Journal of Animal Ecology 71: 1056-62.

87 Proof for the full slope of the interactions-area curve when C changes with area:

Assume (z1+z2) = b, c1 is the constant (intercept) for the species-area curve of group 1, c2 is the constant (intercept) for the species-area curve of group 2, Aa is the smaller area, Ab is the larger area, and Ca and Cb are connectance values at Aa and Ab, respectively. The slope in the interactions-area curve is equivalent to the change in I (interactions) over the change in A:

log (& ) − log (& ) ' * log (+') − log (+*)

Using Equation 7 to calculate log (Ib) and log (Ia):

log(, ) + log(. ) + log(. ) + b ∗ 123(+ ) − (log(, ) + log(. ) + log(. ) + b ∗ 123(+ )) ' / / ' * / 4 * log (+') − log (+*)

123(, ) + b ∗ 123(+ ) − 5123(, ) + b ∗ 123(+ )6 ' ' * * 123(+') − 123(+*)

123(, ) − 123(, ) + b5123(+ ) − 123(+ )6 ' * ' * 123(+') − 123(+*)

123(, ) − 123(, ) b5123(+ ) − 123(+ )6 ' * + ' * 123(+') − 123(+*) 123(+') − 123(+*)

123(, ) − 123(, ) ' * + b 123(+') − 123(+*)

88 Table S1. Area, number of species, number of interactions, and number of connections for each habitat patch used in our species-interactions analyses of shrub-bird frugivory interactions in the Sierra Nevada Montains, California, USA. Bird Shrub Total Connectance Area (km2) Richness Richness Interactions (C) 79.8 3 3 4 0.44 81.5 9 5 16 0.36 86.4 3 6 5 0.28 91.0 3 4 4 0.33 91.6 1 3 1 0.33 157.4 8 9 14 0.19 168.8 12 6 25 0.35 178.8 9 5 17 0.38 201.7 12 8 26 0.27 241.9 6 7 21 0.5 267.4 14 12 44 0.26 280.5 9 5 14 0.31

Figure S1. Location of survey patches within California, USA (in green, with county divisions). Each dot or clump of dots constitutes one patch (n = 20). National parks are labeled on the map and are represented by grey areas. The thin black line running from southeast to Northwest is the Pacific Crest Trail, which closely follows the Sierra Nevada Crest that divides the western and eastern slopes of the range.

90 0.0 -0.2 -0.4 -0.6 Connectance (Log[10]) Connectance -0.8 -1.0

7.9 8.0 8.1 8.2 8.3 8.4

Area (Log[10])

Figure S2. Log connectance vs. log area for a frugivory network of shrubs and birds in the Sierra Nevada Mountains, California, USA. Connectance does not change with area. A connectance of 0.1 corresponds to -1 on the y-axis, 0.2 corresponds to -0.7, 0.3 corresponds to - 0.5, 0.4 corresponds to -0.4, and 0.5 corresponds to -0.3.

91 8 8 2.0 6 6 1.5 4 4 1.0 No of Interactions (Log[10]) of No Interactions (Log[10]) of No Interactions (Log[10]) of No Interactions 2 2 0.5 0 0 0.0

7.9 8.0 8.1 8.2 8.3 8.4 7.9 8.0 8.1 8.2 8.3 8.4 7.9 8.0 8.1 8.2 8.3 8.4

Area (Log[10]) Area (Log[10]) Area (Log[10])

Figure S3. Data (points) and fit curve (black line = mean, dashed lines = 95% CI) for interactions-area curve for frugivory and seed dispersal network of the Sierra Nevada, California, USA, compared to three theoretical predictions. Left: Theorized interactions-area curve combining the species-area curves for frugivores and fleshy-fruited shrubs plus the connectance of the network (grey area = 95% CI). Middle: Theorized interactions-area curve combining the link-species scaling law (L = bSu, where L is the total number of interactions, b is a constant, S is the total number of species within the network, and u = 1) and the species-area relationship. For this curve, u = 1 (brown area = 95% CI). Left: Theorized interactions-area curve combining the link-species scaling law and the species- area relationship, with u = 2 (brown area = 95% CI).

92 Appendix C – Supporting Information for Chapter 3

Supplementary Tables

Table S1. List of 91 shrub and small tree species found in the Sierra Nevada Mountains, California, USA. The 83 species included in our analyses are starred. Species that are endemic to California are listed as “CA”, and species endemic to the Sierra Nevada are listed as “SN”. Status is based on International Union for Conservation of Nature (IUCN), US federal, and California state listings, as of 10 Nov, 2017. Unless noted in table, species have not been assessed by IUCN. The year of the most recent record is listed for any species eliminated from the analysis because it was not recorded during 1990–2015. An asterisk beside the species name indicates those species included in the analysis of detached species from a real-world network.

Most recent Species Family Endemic? Status record Amelanchier alnifolia* Rosaceae Amelanchier pallida* Rosaceae Amelanchier utahensis* Rosaceae Arbutus menziesii* Ericaceae Arctostaphylos glauca* Ericaceae Arctostaphylos manzanita* Ericaceae CA Arctostaphylos mewukka* Ericaceae CA Threatened Arctostaphylos myrtifolia* Ericaceae SN (US Federal) Arctostaphylos nevadensis* Ericaceae Arctostaphylos nissenana* Ericaceae CA Arctostaphylos patula* Ericaceae Arctostaphylos uva-ursi* Ericaceae Arctostaphylos viscida* Ericaceae Berberis aquifolium* Berberidaceae Berberis nervosa Berberidaceae 1977 Berberis 1890 pinnata ssp. insularis Endangered (US Federal, Berberis pinnata Berberidaceae CA State) Celtis reticulata Cannabaceae 1931 Cornus glabrata* Cornaceae Cornus nuttallii* Cornaceae Least Concern Cornus sericea* Cornaceae (IUCN) Cornus sessilis* Cornaceae CA Crataegus douglasii* Rosaceae

93 Frangula californica* Rhamnaceae Frangula purshiana* Rhamnaceae Frangula rubra* Rhamnaceae Gaultheria humifusa* Ericaceae Gaultheria ovatifolia* Ericaceae Heteromeles arbutifolia* Rosaceae Lonicera cauriana* Caprifoliaceae Lonicera conjugialis* Caprifoliaceae Lonicera hispidula* Caprifoliaceae Lonicera interrupta* Caprifoliaceae Lonicera involucrata* Caprifoliaceae Lonicera subspicata* Caprifoliaceae Least Concern Prunus andersonii* Rosaceae (IUCN) Least Concern Prunus emarginata* Rosaceae (IUCN) Least Concern Prunus subcordata* Rosaceae (IUCN) Prunus virginiana* Rosaceae Rhamnus alnifolia* Rhamnaceae Rhamnus crocea* Rhamnaceae Rhus aromatica* Ericaceae Ribes amarum* Grossulariaceae CA Ribes aureum* Grossulariaceae Ribes binominatum Grossulariaceae 1905 Ribes californicum Grossulariaceae 1969 Ribes cereum* Grossulariaceae Ribes divaricatum* Grossulariaceae Ribes inerme* Grossulariaceae Ribes lacustre Grossulariaceae 1915 Ribes lasianthum* Grossulariaceae Ribes malvaceum* Grossulariaceae Ribes menziesii* Grossulariaceae Ribes montigenum* Grossulariaceae Ribes nevadense* Grossulariaceae Ribes quercetorum* Grossulariaceae Ribes roezlii* Grossulariaceae Ribes sanguineum Grossulariaceae 1942 Ribes tularense* Grossulariaceae SN Ribes velutinum* Grossulariaceae Ribes viscosissimum* Grossulariaceae

94 Rosa bridgesii* Rosaceae Rosa californica* Rosaceae Rosa gymnocarpa* Rosaceae Rosa pinetorum* Rosaceae Rosa pisocarpa* Rosaceae Rosa spithamea* Rosaceae Rosa woodsii* Rosaceae Rubus glaucifolius* Rosaceae Rubus leucodermis* Rosaceae Rubus parviflorus* Rosaceae Rubus ursinus* Rosaceae Sambucus nigra* Adoxaceae Sambucus racemosa* Adoxaceae Shepherdia argentea* Elaeagnaceae Solanum parishii* Solanaceae Solanum umbelliferum* Solanaceae Solanum xanti* Solanaceae Symphoricarpos albus* Caprifoliaceae Symphoricarpos longiflorus Caprifoliaceae 1978 Symphoricarpos mollis* Caprifoliaceae Symphoricarpos rotundifolius* Caprifoliaceae Toxicodendron diversilobum* Anacardiaceae Umbellularia californica* Lauraceae Vaccinium cespitosum* Ericaceae Vaccinium deliciosum* Ericaceae Vaccinium membranaceum* Ericaceae Vaccinium parvifolium* Ericaceae Vaccinium uliginosum* Ericaceae Viburnum ellipticum* Adoxaceae Vitis californica* Vitaceae Vitis girdiana* Vitaceae

95 Table S2. List of bird species found in the Sierra Nevada Mountains, California, USA, that engage in frugivorous behavior. Some bird species have been described in the literature as occasional frugivores, but we found no direct evidence of fruit consumption for the following: California gull (Larus californicus), acorn woodpecker (Melanerpes formicivorus), black-backed woodpecker (Picoides arcticus), pinyon jay (Gymnorhinus cyanocephalus), hooded oriole (Icterus cucullatus), Scott’s oriole (Icterus parisorum), or American goldfinch (Spinus tristis). Common Name Scientific Name Ring-necked pheasant Phasianus colchicus Sooty grouse Dendragapus fuliginosus Mountain quail Oreortyx pictus California quail Callipepla californica Band-tailed pigeon Patagioenas fasciata Yellow-billed cuckoo Coccyzus americanus Northern flicker Colaptes auratus Lewis's woodpecker Melanerpes lewis Downy woodpecker Picoides pubescens Hairy woodpecker Picoides villosus Nuttall's woodpecker Picoides nuttallii Pileated woodpecker Dryocopus pileatus Williamson's sapsucker Sphyrapicus thyroideus Red-breasted sapsucker Sphyrapicus ruber Ash-throated flycatcher Myiarchus cinerascens Western kingbird Tyrannus verticalis Warbling vireo Vireo gilvus Hutton's vireo Vireo huttoni Steller's jay Cyanocitta stelleri California scrub-jay Aphelocoma californica Black-billed magpie Pica hudsonia American crow Corvus brachyrhynchos Common raven Corvus corax Barn swallow Hirundo rustica Cactus wren Campylorhynchus brunneicapillus Wrentit Chamaea fasciata Ruby-crowned kinglet Regulus calendula Golden-crowned kinglet Regulus satrapa Mountain bluebird Sialia currucoides Western bluebird Sialia mexicana Varied thrush Ixoreus naevius Townsend's solitaire Myadestes townsendi American robin Turdus migratorius Hermit thrush Catharus guttatus Northern mockingbird Mimus polyglottos

96 California thrasher Toxostoma redivivum Sage thrasher Oreoscoptes montanus Phainopepla Phainopepla nitens Cedar waxwing Bombycilla cedrorum European starling Sturnus vulgaris Yellow-rumped warbler Setophaga coronata Yellow-breasted chat Icteria virens Western tanager Piranga ludoviciana Summer tanager Piranga rubra Lazuli bunting Passerina amoena Black-headed grosbeak Pheucticus melanocephalus Spotted towhee Pipilo maculatus Green-tailed towhee Pipilo chlorurus California towhee Melozone crissalis Savannah sparrow Passerculus sandwichensis Fox sparrow Passerella iliaca Song sparrow Melospiza melodia White-crowned sparrow Zonotrichia leucophrys Golden-crowned sparrow Zonotrichia atricapilla Bullock's oriole Icterus bullockii Great-tailed grackle Quiscalus mexicanus Evening grosbeak Coccothraustes vespertinus House finch Haemorhous mexicanus Purple finch Haemorhous purpureus Cassin's finch Haemorhous cassinii Lesser goldfinch Spinus psaltria Lawrence's goldfinch Spinus lawrencei Pine siskin Spinus pinus Pine grosbeak Pinicola enucleator

97 Table S3. Data underlying the full shrub-frugivore network for the Sierra Nevada Mountains, California, USA. Cells containing letters indicate an interaction between the shrub species (rows) and bird species (columns). Cells containing a “Y” indicate a species-by-species interaction. Cells containing a “G” indicate interactions that were inferred based on frugivory by the bird species and another shrub species in the same genus.

Ring- Band- Yellow- necked Sooty Mountain California tailed billed Northern Scientific Name pheasant grouse quail quail pigeon cuckoo flicker Amelanchier alnifolia G Amelanchier pallida G Amelanchier utahensis G Arbutus menziesii Y Arctostaphylos glauca G G Arctostaphylos manzanita G G Arctostaphylos mewukka G G Arctostaphylos myrtifolia G G Arctostaphylos nevadensis G G Arctostaphylos nissenana G G Arctostaphylos patula G G Arctostaphylos uva-ursi Y G G Arctostaphylos viscida G G Berberis aquifolium Cornus glabrata G Cornus nuttallii G Cornus sericea G Cornus sessilis G Crataegus douglasii G G G Frangula californica G G Frangula purshiana Y G Frangula rubra G G Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Lonicera cauriana G Lonicera conjugialis G Lonicera hispidula G Lonicera interrupta G Lonicera involucrata G Lonicera subspicata G Prunus andersonii G G Prunus emarginata G G Y Prunus subcordata G G Prunus virginiana G G Rhamnus alnifolia G G Rhamnus crocea Y G G Rhus aromatica G Ribes amarum G Ribes aureum G Ribes cereum G Ribes divaricatum G

98 Lewis's Downy Hairy Nuttall's Pileated Scientific Name woodpecker woodpecker woodepecker woodpecker woodpecker Amelanchier alnifolia G G Amelanchier pallida G G Amelanchier utahensis G G Arbutus menziesii Arctostaphylos glauca Arctostaphylos manzanita Arctostaphylos mewukka Arctostaphylos myrtifolia Arctostaphylos nevadensis Arctostaphylos nissenana Arctostaphylos patula Arctostaphylos uva-ursi Arctostaphylos viscida Berberis aquifolium Cornus glabrata G G G Cornus nuttallii G G G Cornus sericea G G Y Cornus sessilis G G G Crataegus douglasii G Frangula californica Frangula purshiana Frangula rubra Gaultheria humifusa G Gaultheria ovatifolia G Heteromeles arbutifolia Lonicera cauriana Lonicera conjugialis Lonicera hispidula Lonicera interrupta Lonicera involucrata Lonicera subspicata Prunus andersonii G G Prunus emarginata G G Prunus subcordata G G Prunus virginiana G G Rhamnus alnifolia Rhamnus crocea Rhus aromatica G G Ribes amarum G Ribes aureum G Ribes cereum G Ribes divaricatum G

99 Red- Ash- Williamson's breasted throated Western Warbling Hutton's Scientific Name sapsucker sapsucker flycatcher kingbird vireo vireo Amelanchier alnifolia Amelanchier pallida Amelanchier utahensis Arbutus menziesii G Arctostaphylos glauca Arctostaphylos manzanita Arctostaphylos mewukka Arctostaphylos myrtifolia Arctostaphylos nevadensis Arctostaphylos nissenana Arctostaphylos patula Arctostaphylos uva-ursi Arctostaphylos viscida Berberis aquifolium Cornus glabrata G Cornus nuttallii G Cornus sericea G Cornus sessilis G Crataegus douglasii G Frangula californica G Y Frangula purshiana G Frangula rubra G Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Lonicera cauriana Lonicera conjugialis Lonicera hispidula Lonicera interrupta Lonicera involucrata Lonicera subspicata Prunus andersonii Prunus emarginata Y Y Prunus subcordata Prunus virginiana Rhamnus alnifolia G Rhamnus crocea G Rhus aromatica Ribes amarum Ribes aureum Ribes cereum Ribes divaricatum

100 Black- Steller's California billed American Common Barn Scientific Name jay scrub-jay magpie crow raven swallow Amelanchier alnifolia Amelanchier pallida Amelanchier utahensis Arbutus menziesii Arctostaphylos glauca G Arctostaphylos manzanita G Arctostaphylos mewukka G Arctostaphylos myrtifolia G Arctostaphylos nevadensis G Arctostaphylos nissenana G Arctostaphylos patula G Arctostaphylos uva-ursi G Arctostaphylos viscida G Berberis aquifolium Cornus glabrata G Cornus nuttallii G Cornus sericea G Y Cornus sessilis G Crataegus douglasii Frangula californica Frangula purshiana Frangula rubra Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Lonicera cauriana Lonicera conjugialis Lonicera hispidula Lonicera interrupta Lonicera involucrata Lonicera subspicata Prunus andersonii G G G Prunus emarginata G G G Prunus subcordata G G G Prunus virginiana G Y Y Rhamnus alnifolia Rhamnus crocea Rhus aromatica Ribes amarum Ribes aureum Ribes cereum Ribes divaricatum

101 Ruby- Golden- Cactus crowned crowned Mountain Western Scientific Name wren Wrentit kinglet kinglet bluebird bluebird Amelanchier alnifolia Y Amelanchier pallida Amelanchier utahensis Arbutus menziesii Arctostaphylos glauca Arctostaphylos manzanita Arctostaphylos mewukka Arctostaphylos myrtifolia Arctostaphylos nevadensis Arctostaphylos nissenana Arctostaphylos patula Arctostaphylos uva-ursi Arctostaphylos viscida Berberis aquifolium Cornus glabrata G Cornus nuttallii G Cornus sericea G Cornus sessilis G Crataegus douglasii Frangula californica G Y G Frangula purshiana G G Y Frangula rubra G G Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Lonicera cauriana Lonicera conjugialis Lonicera hispidula Lonicera interrupta Lonicera involucrata Y Lonicera subspicata Prunus andersonii G Prunus emarginata Y Y G Prunus subcordata G Prunus virginiana Y Y Rhamnus alnifolia G G Rhamnus crocea G G Rhus aromatica G G Ribes amarum G G Ribes aureum G G Ribes cereum G G Ribes divaricatum G G

102 Varied Townsend's American Hermit Swainson's Northern Scientific Name thrush solitaire robin thrush thrush mockingbird Amelanchier alnifolia Y G Amelanchier pallida G Amelanchier utahensis G G Arbutus menziesii Y Y Arctostaphylos glauca G Arctostaphylos manzanita G Arctostaphylos mewukka G Arctostaphylos myrtifolia G Arctostaphylos nevadensis G Arctostaphylos nissenana G Arctostaphylos patula G Arctostaphylos uva-ursi Y Arctostaphylos viscida Y G Berberis aquifolium Y G Cornus glabrata G G G G Cornus nuttallii G G G G Cornus sericea Y G G G Cornus sessilis G G G G Crataegus douglasii G G Frangula californica G G G G G G Frangula purshiana Y G G G G G Frangula rubra G G G G G G Gaultheria humifusa G Gaultheria ovatifolia G Heteromeles arbutifolia Y Y Lonicera cauriana G G G G G Lonicera conjugialis G G G G Lonicera hispidula Y G G G G Lonicera interrupta G G G G Lonicera involucrata G Y G G Lonicera subspicata G G G G Prunus andersonii G G Prunus emarginata Y Y Prunus subcordata G G Prunus virginiana Y Y Rhamnus alnifolia G G G G G G Rhamnus crocea G G G G G G Rhus aromatica G G G G Ribes amarum G Ribes aureum G Ribes cereum Y G Y Ribes divaricatum G

103 Yellow- California Sage Cedar European rumped Scientific Name thrasher thrasher Phainopepla waxwing starling warbler Amelanchier alnifolia G G Amelanchier pallida G G Amelanchier utahensis G G Arbutus menziesii Y Arctostaphylos glauca G Arctostaphylos manzanita G Arctostaphylos mewukka G Arctostaphylos myrtifolia G Arctostaphylos nevadensis G Arctostaphylos nissenana G Arctostaphylos patula G Arctostaphylos uva-ursi G Arctostaphylos viscida G Berberis aquifolium G Cornus glabrata G G G Cornus nuttallii G G G Cornus sericea Y G G Cornus sessilis G G G Crataegus douglasii G Frangula californica G G G G G Frangula purshiana G G G G G Frangula rubra G G G G G Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Y Y Lonicera cauriana G G G Lonicera conjugialis G G G Lonicera hispidula G G G Lonicera interrupta G G G Lonicera involucrata Y G G Lonicera subspicata G G G Prunus andersonii G G Prunus emarginata Y G Prunus subcordata G G Prunus virginiana Y G Rhamnus alnifolia G G G G G Rhamnus crocea G G G G G Rhus aromatica G G Ribes amarum G Ribes aureum G Ribes cereum G Ribes divaricatum G

104 Yellow- Black- Green- breasted Western Summer Lazuli headed Spotted tailed Scientific Name chat tanager tanager bunting grosbeak towhee towhee Amelanchier alnifolia Y G Y G G Amelanchier pallida G G G G Amelanchier utahensis G G G G Arbutus menziesii Y Arctostaphylos glauca G Arctostaphylos manzanita G Arctostaphylos mewukka G Arctostaphylos myrtifolia G Arctostaphylos nevadensis G Arctostaphylos nissenana G Arctostaphylos patula G Arctostaphylos uva-ursi G Arctostaphylos viscida G Berberis aquifolium Cornus glabrata Cornus nuttallii Cornus sericea Cornus sessilis Crataegus douglasii G Frangula californica G G Y Frangula purshiana G G G Frangula rubra G G G Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Y Lonicera cauriana G Lonicera conjugialis G Lonicera hispidula G Lonicera interrupta G Lonicera involucrata G Lonicera subspicata G Prunus andersonii G G Prunus emarginata Y Y G Prunus subcordata G G Prunus virginiana G Y G Rhamnus alnifolia G G G Rhamnus crocea G G G Rhus aromatica G Ribes amarum G Ribes aureum G Ribes cereum G Ribes divaricatum G

105 White- Golden- California Savannah Fox Song crowned crowned Scientific Name towhee sparrow sparrow sparrow sparrow sparrow Amelanchier alnifolia G Y Amelanchier pallida G Amelanchier utahensis G Arbutus menziesii Y Arctostaphylos glauca Arctostaphylos manzanita Arctostaphylos mewukka Arctostaphylos myrtifolia Arctostaphylos nevadensis Arctostaphylos nissenana Arctostaphylos patula Arctostaphylos uva-ursi Arctostaphylos viscida Berberis aquifolium Cornus glabrata Cornus nuttallii Cornus sericea Cornus sessilis Crataegus douglasii Frangula californica Y Y G Frangula purshiana G G Frangula rubra G G Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Lonicera cauriana G Lonicera conjugialis G Lonicera hispidula G Lonicera interrupta G Lonicera involucrata G Lonicera subspicata G Prunus andersonii G Prunus emarginata G Y Prunus subcordata G Prunus virginiana G Rhamnus alnifolia G G Rhamnus crocea Y G G Rhus aromatica Ribes amarum Ribes aureum Ribes cereum Y Ribes divaricatum

106 Great- Bullock's tailed Evening House Purple Cassin's Lesser Scientific Name oriole grackle grosbeak finch finch finch goldfinch Amelanchier alnifolia G G Amelanchier pallida G G Amelanchier utahensis G G Arbutus menziesii Y Arctostaphylos glauca Arctostaphylos manzanita Arctostaphylos mewukka Arctostaphylos myrtifolia Arctostaphylos nevadensis Arctostaphylos nissenana Arctostaphylos patula Arctostaphylos uva-ursi Arctostaphylos viscida Berberis aquifolium G Cornus glabrata G Cornus nuttallii G Cornus sericea G Cornus sessilis G Crataegus douglasii G Frangula californica G G G Y Frangula purshiana G G G G Frangula rubra G G G G Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Lonicera cauriana Lonicera conjugialis Lonicera hispidula Lonicera interrupta Y Lonicera involucrata Lonicera subspicata Prunus andersonii G G Prunus emarginata G G Y Prunus subcordata G G Prunus virginiana G G Y Rhamnus alnifolia G G G G Rhamnus crocea G G G Y Rhus aromatica G G Ribes amarum Ribes aureum Ribes cereum Ribes divaricatum

107 Scientific Lawrence's Pine Name goldfinch Pine siskin grosbeak Amelanchier alnifolia G Amelanchier pallida G Amelanchier utahensis G Arbutus menziesii Arctostaphylos glauca Arctostaphylos manzanita Arctostaphylos mewukka Arctostaphylos myrtifolia Arctostaphylos nevadensis Arctostaphylos nissenana Arctostaphylos patula Arctostaphylos uva-ursi Arctostaphylos viscida Berberis aquifolium Cornus glabrata Cornus nuttallii Cornus sericea Cornus sessilis Crataegus douglasii Frangula californicaY G Frangula purshiana G Frangula rubra G Gaultheria humifusa Gaultheria ovatifolia Heteromeles arbutifolia Lonicera cauriana G Lonicera conjugialis G Lonicera hispidula G Lonicera interrupta G Lonicera involucrata G Lonicera subspicata G Prunus andersonii Prunus emarginata Y Prunus subcordata Prunus virginiana Rhamnus alnifolia G Rhamnus crocea G Rhus aromatica Ribes amarum Ribes aureum Ribes cereum Ribes divaricatum

108 Ring- Band- Yellow- necked Sooty Mountain California tailed billed Northern Scientific Name pheasant grouse quail quail pigeon cuckoo flicker Ribes inerme G Ribes lasianthum G Ribes malvaceum G Ribes menziesii G Y Ribes montigenum G Ribes nevadense G Ribes quercetorum G Ribes roezlii G Ribes tularense G Ribes velutinum G Ribes viscosissimum G Rosa bridgesii G Rosa californica G Rosa gymnocarpa G Rosa pinetorum G Rosa pisocarpa G Rosa spithamea G Rosa woodsii G Rubus glaucifolius G G G G G G Rubus leucodermis G G G G G G Rubus parviflorus G G G G G G Rubus ursinus G Y G G G G Sambucus nigra Y Y Y Sambucus racemosa Y Shepherdia argentea Solanum parishii Solanum umbelliferum Solanum xanti Symphoricarpos albus Symphoricarpos mollis Symphoricarpos rotundifolius Toxicodendron diversilobum Y Y Umbellularia californica Vaccinium cespitosum G Vaccinium deliciosum G Vaccinium membranaceum G Vaccinium parvifolium Y Vaccinium uliginosum G Viburnum ellipticum G Vitis californica G G G G Vitis girdiana G G G G

109 Lewis's Downy Hairy Nuttall's Pileated Scientific Name woodpecker woodpecker woodepecker woodpecker woodpecker Ribes inerme G Ribes lasianthum G Ribes malvaceum G Ribes menziesii G Ribes montigenum G Ribes nevadense G Ribes quercetorum G Ribes roezlii G Ribes tularense G Ribes velutinum G Ribes viscosissimum G Rosa bridgesii Rosa californica Rosa gymnocarpa Rosa pinetorum Rosa pisocarpa Rosa spithamea Rosa woodsii Rubus glaucifolius G G Rubus leucodermis G G Rubus parviflorus G G Rubus ursinus G G Sambucus nigra G Y Sambucus racemosa G Y Shepherdia argentea Solanum parishii Solanum umbelliferum Solanum xanti Symphoricarpos albus Symphoricarpos mollis Symphoricarpos rotundifolius Toxicodendron diversilobum G Y Umbellularia californica Vaccinium cespitosum Vaccinium deliciosum Vaccinium membranaceum Vaccinium parvifolium Y Vaccinium uliginosum Viburnum ellipticum G Vitis californica G G Vitis girdiana G G

110 Red- Ash- Williamson's breasted throated Western Warbling Hutton's Scientific Name sapsucker sapsucker flycatcher kingbird vireo vireo Ribes inerme Ribes lasianthum Ribes malvaceum Ribes menziesii Ribes montigenum Ribes nevadense Ribes quercetorum Ribes roezlii Ribes tularense Ribes velutinum Ribes viscosissimum Rosa bridgesii Rosa californica Rosa gymnocarpa Rosa pinetorum Rosa pisocarpa Rosa spithamea Rosa woodsii Rubus glaucifolius Rubus leucodermis Rubus parviflorus Rubus ursinus Sambucus nigra Y G G G Sambucus racemosa G G G Shepherdia argentea Solanum parishii Solanum umbelliferum Solanum xanti Symphoricarpos albus Symphoricarpos mollis Symphoricarpos rotundifolius Toxicodendron diversilobum Y Y Umbellularia californica Vaccinium cespitosum Vaccinium deliciosum Vaccinium membranaceum Vaccinium parvifolium Vaccinium uliginosum Viburnum ellipticum Vitis californica Vitis girdiana

111 Black- Steller's California billed American Common Barn Scientific Name jay scrub-jay magpie crow raven swallow Ribes inerme Ribes lasianthum Ribes malvaceum Ribes menziesii Ribes montigenum Ribes nevadense Ribes quercetorum Ribes roezlii Ribes tularense Ribes velutinum Ribes viscosissimum Rosa bridgesii Rosa californica Rosa gymnocarpa Rosa pinetorum Rosa pisocarpa Rosa spithamea Rosa woodsii Rubus glaucifolius G G Rubus leucodermis G G Rubus parviflorus G G Rubus ursinus G G Sambucus nigra G G Sambucus racemosa G G Shepherdia argentea Y Solanum parishii Solanum umbelliferum Solanum xanti Symphoricarpos albus Symphoricarpos mollis Symphoricarpos rotundifolius Toxicodendron diversilobum Umbellularia californica Vaccinium cespitosum Vaccinium deliciosum Vaccinium membranaceum Vaccinium parvifolium Vaccinium uliginosum Viburnum ellipticum Vitis californica G Vitis girdiana G

112 Ruby- Golden- Cactus crowned crowned Mountain Western Scientific Name wren Wrentit kinglet kinglet bluebird bluebird Ribes inerme G G Ribes lasianthum G G Ribes malvaceum G G Ribes menziesii G G Ribes montigenum G G Ribes nevadense G G Ribes quercetorum G G Ribes roezlii G G Ribes tularense G G Ribes velutinum G G Ribes viscosissimum G G Rosa bridgesii Rosa californica Rosa gymnocarpa Rosa pinetorum Rosa pisocarpa Rosa spithamea Rosa woodsii Rubus glaucifolius G G Rubus leucodermis G G Rubus parviflorus G Y Rubus ursinus G G Sambucus nigra Y G G G G Sambucus racemosa G G G G Shepherdia argentea Solanum parishii G Solanum umbelliferum G Solanum xanti G Symphoricarpos albus Y Symphoricarpos mollis Symphoricarpos rotundifolius Toxicodendron diversilobum G Y Y Y Umbellularia californica Vaccinium cespitosum Vaccinium deliciosum Vaccinium membranaceum Vaccinium parvifolium Vaccinium uliginosum Viburnum ellipticum Vitis californica Y G G Vitis girdiana G G

113 Varied Townsend's American Hermit Swainson's Northern Scientific Name thrush solitaire robin thrush thrush mockingbird Ribes inerme G Ribes lasianthum G Ribes malvaceum G Ribes menziesii G Ribes montigenum G Ribes nevadense G Ribes quercetorum G Ribes roezlii G Ribes tularense G Ribes velutinum G Ribes viscosissimum G Rosa bridgesii G G G G Rosa californica G G G G Rosa gymnocarpa G G G G Rosa pinetorum G G G G Rosa pisocarpa G G G G Rosa spithamea G G G G Rosa woodsii G Y G G Rubus glaucifolius G G G G Rubus leucodermis G G G G Rubus parviflorus Y Y G Y Rubus ursinus G G G G Sambucus nigra G G Y Sambucus racemosa Y G Y G Shepherdia argentea Solanum parishii G Solanum umbelliferum G Solanum xanti G Symphoricarpos albus Y Symphoricarpos mollis Symphoricarpos rotundifolius Y Toxicodendron diversilobum G G G Umbellularia californica Vaccinium cespitosum G G G G Vaccinium deliciosum G G G G Vaccinium membranaceum G G G G Vaccinium parvifolium Y G G G Vaccinium uliginosum G G G G Viburnum ellipticum G G G G Vitis californica G G Vitis girdiana G G

114 Yellow- California Sage Cedar European rumped Scientific Name thrasher thrasher Phainopepla waxwing starling warbler Ribes inerme G Ribes lasianthum G Ribes malvaceum G Ribes menziesii G Ribes montigenum G Ribes nevadense G Ribes quercetorum G Ribes roezlii G Ribes tularense G Ribes velutinum G Ribes viscosissimum G Rosa bridgesii G G Rosa californica G G Rosa gymnocarpa G G Rosa pinetorum G G Rosa pisocarpa G G Rosa spithamea G G Rosa woodsii G G Rubus glaucifolius G G G G Rubus leucodermis G G G G Rubus parviflorus G G G G Rubus ursinus G G G G Sambucus nigra Y Y Y Y Sambucus racemosa G G Shepherdia argentea Solanum parishii G G Solanum umbelliferum G G Solanum xanti G G Symphoricarpos albus Symphoricarpos mollis Symphoricarpos rotundifolius Toxicodendron diversilobum Y G Y Umbellularia californica Vaccinium cespitosum G Vaccinium deliciosum G Vaccinium membranaceum G Vaccinium parvifolium G Vaccinium uliginosum G Viburnum ellipticum G G G Vitis californica G G G G G Vitis girdiana G G G G G

115 Yellow- Black- Green- breasted Western Summer Lazuli headed Spotted tailed Scientific Name chat tanager tanager bunting grosbeak towhee towhee Ribes inerme G Ribes lasianthum G Ribes malvaceum G Ribes menziesii G Ribes montigenum G Ribes nevadense G Ribes quercetorum G Ribes roezlii G Ribes tularense G Ribes velutinum G Ribes viscosissimum G Rosa bridgesii Rosa californica Rosa gymnocarpa Rosa pinetorum Rosa pisocarpa Rosa spithamea Rosa woodsii Rubus glaucifolius G G G G G G Rubus leucodermis G G G G G G Rubus parviflorus G G G G G G Rubus ursinus G G G G G G Sambucus nigra Y G G Y G Sambucus racemosa G Y G G Shepherdia argentea Solanum parishii G Solanum umbelliferum G Solanum xanti G Symphoricarpos albus G Symphoricarpos mollis G Symphoricarpos rotundifolius G Toxicodendron diversilobum Y Y Umbellularia californica Vaccinium cespitosum G G Vaccinium deliciosum G G Vaccinium membranaceum G G Vaccinium parvifolium G G Vaccinium uliginosum G G Viburnum ellipticum Vitis californica G Vitis girdiana G

116 White- Golden- California Savannah Fox Song crowned crowned Scientific Name towhee sparrow sparrow sparrow sparrow sparrow Ribes inerme Ribes lasianthum Ribes malvaceum Ribes menziesii Ribes montigenum Ribes nevadense Ribes quercetorum Ribes roezlii Ribes tularense Ribes velutinum Ribes viscosissimum Rosa bridgesii Rosa californica Rosa gymnocarpa Rosa pinetorum Rosa pisocarpa Rosa spithamea Rosa woodsii Rubus glaucifolius G G G Rubus leucodermis G G G Rubus parviflorus G G G Rubus ursinus G G G Sambucus nigra Y G G G Sambucus racemosa G Y G G Shepherdia argentea Solanum parishii Solanum umbelliferum Solanum xanti Symphoricarpos albus Y Symphoricarpos mollis Symphoricarpos rotundifolius Toxicodendron diversilobum Y Umbellularia californica Vaccinium cespitosum G G G G Vaccinium deliciosum G G G G Vaccinium membranaceum G G G G Vaccinium parvifolium G G G G Vaccinium uliginosum G G G G Viburnum ellipticum Vitis californica G G Vitis girdiana G G

117 Great- Bullock's tailed Evening House Purple Cassin's Lesser Scientific Name oriole grackle grosbeak finch finch finch goldfinch Ribes inerme Ribes lasianthum Ribes malvaceum Ribes menziesii Ribes montigenum Ribes nevadense Ribes quercetorum Ribes roezlii Ribes tularense Ribes velutinum Ribes viscosissimum Rosa bridgesii Rosa californica Rosa gymnocarpa Rosa pinetorum Rosa pisocarpa Rosa spithamea Rosa woodsii Rubus glaucifolius G G Rubus leucodermis G G Rubus parviflorus G G Rubus ursinus G G Sambucus nigra Y Sambucus racemosa Shepherdia argentea Solanum parishii Solanum umbelliferum Solanum xanti Symphoricarpos albus G Symphoricarpos mollis G Symphoricarpos rotundifolius G Toxicodendron diversilobum Y Umbellularia californica Vaccinium cespitosum Vaccinium deliciosum Vaccinium membranaceum Vaccinium parvifolium Vaccinium uliginosum Viburnum ellipticum Vitis californica G G G G G G Vitis girdiana G G G G G G

118 Lawrence's Pine Scientific Name goldfinch Pine siskin grosbeak Ribes inerme Ribes lasianthum Ribes malvaceum Ribes menziesii Ribes montigenum Ribes nevadense Ribes quercetorum Ribes roezlii Ribes tularense Ribes velutinum Ribes viscosissimum Rosa bridgesii G Rosa californica G Rosa gymnocarpa G Rosa pinetorum G Rosa pisocarpa G Rosa spithamea G Rosa woodsii G Rubus glaucifolius G Rubus leucodermis G Rubus parviflorus G Rubus ursinus G Sambucus nigra Sambucus racemosa Shepherdia argentea Solanum parishii Solanum umbelliferum Solanum xanti Symphoricarpos albus G Symphoricarpos mollis G Symphoricarpos rotundifolius Y Toxicodendron diversilobum Umbellularia californica Vaccinium cespitosum Vaccinium deliciosum Vaccinium membranaceum Vaccinium parvifolium Vaccinium uliginosum Viburnum ellipticum G Vitis californica G G Vitis girdiana G G

119 Table S3 References

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121 Chu, M., and G. Walsberg. 1999. Phainopepla (Phainopepla nitens), The Birds of North America (P. G. Rodewald, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America: https://birdsna-org.ezproxy.lib.uconn.edu/Species- Account/bna/species/phaino DOI: 10.2173/bna.415.

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131 Supplementary Figures

Figure S1. Western continental USA with states outlined (top left) and elevation map of the Sierra Nevada Mountains, California, USA (inset). Elevation is displayed by color ramp, in meters. The Sierra Nevada are roughly outlined in white. The Sierra Nevada crest is dark green and goes approximately southeast to northwest following consecutive county boundaries, which are outlines in black.

132

Figure S2. Number of species in each partner group for a given area in one iteration of the randomly selected communities. Number of species was determined using the species-area relationship, S = cAz. The z for group A was 0.35, and the z for group B was 0.15. The number of species shown here represents the greatest difference in number of species between group A and group B used in the random network simulations.

133 80 60 40 Number of species Number 20 0

0 10 20 30 40 50 60

Years before 2015

Figure S3. Species-accumulation curve for herbarium data for fleshy-fruited shrubs in the Sierra Nevada Mountains, California, USA. The x-axis shows years since 2015 (x = 0) and extends to 1955 (x = 60). The dashed line is at 1990, where the curve asymptotes to 83 species. Only three more species were added between 1990 and 1950: one in 1978 (Symphoricarpos longiflorus), one in 1977 (Berberis nervosa), and one in 1969 (Ribes californicum).

134

Figure S4. Seed dispersal network of the Sierra Nevada Mountains, California, USA. Each row is a species of shrub, and each column is a species of bird. Black squares indicate that the bird species of that column consumes the fruit of the shrub species of that row, and white squares indicate that they do not. The network is arranged such that the bird species that consume the fruits of the most shrub species are furthest to the left, and the shrub species that are consumed by the most bird species are towards the top. See Table S3 for details on each species.

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