Climatic Range Filling of North American Trees Benjamin Seliger University of Maine, [email protected]

Climatic Range Filling of North American Trees Benjamin Seliger University of Maine, Benjamin.Seliger@Maine.Edu

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Fall 12-14-2018 Climatic Range Filling of North American Trees Benjamin Seliger University of Maine, [email protected]

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This Open-Access Thesis is brought to you for free and open access by DigitalCommons@UMaine. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of DigitalCommons@UMaine. For more information, please contact [email protected]. CLIMATIC RANGE FILLING OF NORTH AMERICAN TREES

Benjamin James Seliger

B.S. University of Wisconsin-Madison, 2013

A DISSERTATION

Submitted in Partial Fulfillment of the

Requirements for the Degree of

Doctor of Philosophy

(in Ecology and Environmental Sciences)

The Graduate School

The University of Maine

December 2018

Advisory Committee Jacquelyn L. Gill, Assistant Professor of Paleoecology and Plant Ecology (Advisor) David E. Hiebeler, Professor of Mathematics Brian J. McGill, Professor of Biological Sciences José Eduardo Meireles, Assistant Professor of Plant Systematics Mark Vellend, Professor of Biological Sciences

CLIMATIC RANGE FILLING OF NORTH AMERICAN TREES

By Benjamin James Seliger

Dissertation Advisor: Dr. Jacquelyn Gill

An Abstract of the Dissertation Presented

in Partial Fulfillment of the Requirements for the

Degree of Doctor of Philosophy

(in Ecology and Environmental Sciences)

December 2018

Understanding the degree to which species distributions are controlled by climate is crucial for forecasting biodiversity responses to climate change. Climatic equilibrium, when species are found in all places which are climatically suitable, is a fundamental assumption of species distribution models, but there is evidence in support of climate disequilibria in species ranges. Long-lived, sessile organisms such as trees may be especially vulnerable to being outpaced by climate change, and thus prone to disequilibrium. In this dissertation, I tested the degree to which North American trees are in equilibrium with their potential climatic ranges using the ‘range filling’ metric, which is calculated by taking the proportion of modeled potential climatic range which is realized. In Chapter I, I demonstrated that most species are missing from the majority of their potential ranges (mean range filling 48%). Range filling was strongly positively correlated with realized range size. I found that small-ranged species have underfilled climatic ranges relative to a spatially randomized ecological null model, suggesting that climate is a poor predictor of their distributions. I further show small-ranged species tend to have range shapes more indicative of dispersal limitations than climatic filling. In Chapter II, I found that

range filling correlates with species’ functional differences; I found low range filling among species with traits correlated with pioneer plant strategies, while “climax” species showed high range filling. An analysis of range filling through time from pollen-derived distributions

(Chapter III) revealed that while range filling decreased during post-glacial climatic change events, much of the contemporary disequillibrium is the result of decreases in the last 5000 years. Megafauna-adapted species which are proposed to have dispersal limitations due relying on now-extinct Pleistocene megafauna mostly do not show the hypothesized consequences, possibly due to compensatory dispersers. Overall, these findings suggest that nonclimatic factors and dispersal limitations drive climatic disequlibrium in North American trees. Considering this in light of human induced landscape fragmentation and climate change which exacerbate dispersal potential and increase equilibrium, attention should be given towards transplanting narrowly distributed tree species.

ACKNOWLEDGEMENTS

This work depends greatly upon the tremendous mapping efforts of E.L. Little’s “Atlas of

United States trees”. This research also would not have been possible without the Neotoma

Paleocology Database and the hundreds of paleoecologists who contributed their data to it. Their effort in obtaining the data, along with that of the Neotoma community in constructing and maintaining the database, is greatly appreciated. Lastly, this research uses the US Forest

Inventory Analysis, which would not be possible without the hard of the work of thousands of dedicated members of the survey crew collecting and the staff maintaining data. I would like to further acknowledge Alejandro Ordonez for help with calculating climate velocity, and Chris

Jackson who obtained the photos of squirrels and deer eating Osage orange and allowed their use. I would like to acknowledge the members of the University of Maine’s BEAST Lab and ecological community for many helpful suggestions, and the UMaine GSG for funding.

ii TABLE OF CONTENTS

ACKOWLEDGEMENTS ...... ii LIST OF TABLES ...... v LIST OF FIGURES ...... vi CHAPTER I: WIDESPREAD UNDERFILLING OF THE POTENTIAL CLIMATIC RANGES OF NORTH AMERICAN TREES ...... 1 Chapter Abstract ...... 1 Introduction ...... 1 Methods...... 4 Results ...... 9 Discussion ...... 11 Conclusion ...... 20 CHAPTER II: A MILE WIDE AND AN INCH DEEP: LARGE-RANGED PIONEER TREES FILL LESS OF THEIR POTENTIAL CLIMATIC RANGES THAN SMALL- RANGED CLIMAX SPECIALISTS ...... 21 Chapter Abstract ...... 21 Introduction ...... 21 Methods...... 23 Results ...... 27 Discussion ...... 30 Conclusion ...... 34 CHAPTER III: NORTH AMERICAN TREES FAIL TO FILL CLIMATIC RANGES DURING POST-GLACIAL WARMING ...... 35 Chapter Abstract ...... 35 Introduction ...... 35 Methods...... 38 Results ...... 43 Discussion ...... 49 Conclusion ...... 53 CHAPTER IV: NO EVIDENCE FOR EXTINCT MEGAFAUNA DISPERSAL LIMITATIONS AMONG EASTERN UNITED STATES’ TREES; DID NATIVE AMERICANS DISPERSE THE FAVORED FRUITS OF PLEISTOCENE MEGAFAUNA? ...... 55 Chapter Abstract ...... 55 Introduction ...... 55 iii

Methods...... 59 Results ...... 63 Discussion ...... 63 Conclusion ...... 69 SYNTHESIS ...... 70 REFERENCES ...... 78 APPENDIX ...... 95 BIOGRAPHY ...... 100

LIST OF TABLES Table 1. Range Filling Correlates………………………………………………………………….8

Table 2. Phylogenetic Signal Table……………………………………………………………....30

LIST OF FIGURES Figure 1. Workflow Diagram.………………………………………………………………...... 5

Figure 2. Range Size-Range Filling.…………………………………………………………….....9

Figure 3. Range Filling in Space.……………………………………………………………...... 10

Figure 4. Potential Range Size.………………………………………………………………...... 11

Figure 5. Range Shape Ratios.…………………………………………………………………....12

Figure 6. Functional Trait Correlation Matrix.…………………………………………………...25

Figure 7. Variance Partitioning Summary………………………………………………………..27

Figure 8. Traits Against Range Metrics.……………………………………………………….....29

Figure 9. Pollen Workflow Diagram.…………………………………………………………….39

Figure 10. Example Fossil Ranges.……………………………………………………………….42

Figure 11. Fossil Range Filling Values.……………………………………………………...... 44

Figure 12.1 Fossil Range Results by Taxa.……………………………………………………….45

Figure 12.2 Fossil Range Results by Taxa (Continued).……………………………………….....46

Figure 13. Mean Fossil Range Results…………………………………………………………....47

Figure 14. Paleo Correlation Matrix……………………………………………………………...48

Figure 15. Megafauna-Adapted Geographic Ranges.…………………………………………….61

Figure 16. Megafauna-Adapted Potential Climatic Ranges.……………………………………...62

Figure 17. Megafauna-Adapted Range Differences.……………………………………………..63

Figure 18. Compensatory Dispersal Vectors of Megafauna Adapted Trees……………………...68

Appendix Figure 1. Example Model Outputs………………………………………………….....95

Appendix Figure 2. Range Filling by Resolution…………………………………………………96

Appendix Figure 3. Randomized Range Example……………………………………………...... 97

Appendix Figure 4.1. Range Examples (California buckeye)………………………………….....98

Appendix Figure 4.2. Range Examples (big leaf maple)…………………………………………98

Appendix Figure 4.3. Range Examples (Yellowwood)………………………………………...... 99

Appendix Figure 4.4. Range Examples (Eastern hemlock)………………………………………99

vii

CHAPTER I: WIDESPREAD UNDERFILLING OF THE POTENTIAL CLIMATIC RANGES OF NORTH AMERICAN TREES

Chapter Abstract Climatic equilibrium is a common assumption of species ranges, and a fundamental principle of environmental niche models, but the relative influence of climate and non-climatic factors in limiting ranges has rarely been quantified. We tested this assumption for 447 species of

North American trees by modeling their potential climatic range filling, and compared our results against a geometric, non-ecological null model. We also analyzed the shapes of ranges relative to their potential ranges to detect the drivers of disequilibrium. We found that the climatic ranges of

North American tree are under-filled (mean range filling 48%), and that range filling is positively correlated with geographic range size. Large-ranged species outperformed the null model and had shape ratios indicative of climatic restrictions, while small-ranged species showed a stronger influence of dispersal limitation. Overall, climatic disequilibrium and biogeographic patterns highlight the role of post-glacial dispersal legacies in limiting modern range extents.

Introduction Species’ geographic ranges are generally thought to be in equilibrium with contemporary climate (Copenhaver-Parry et al. 2017), which is a foundational principle behind species distribution models (SDMs) used to forecast the response of biodiversity to climate change

(Araújo & Pearson 2005; Pearson & Dawson 2005). However, lagged responses of ranges to past

(Svenning & Skov 2007; Svenning et al. 2008; Normand et al. 2011; Svenning & Sandel 2013;

Ordonez & Svenning 2016, 2017) and present climate change (Woodall et al. 2009; Zhu et al.

2012; Corlett & Westcott 2013) suggests this assumption may be unrealistic, leading us to overestimate species’ ability to track their climatic niches and underestimate future biodiversity

1 loss. Accurate forecasts of climate-driven range shifts thus requires us to understand the relative influence of climate and non-climatic factors in governing species’ realized ranges (Alexander et al. 2018).

Species’ ranges are the intersection of their abiotic constraints, biological interactions, and dispersal limitations (Soberón 2007; Soberón & Nakamura 2009). The area containing all naturally-occurring individuals of a species, or the “realized range”, is a subset of that species’ potential range, which is the total habitable area for the species assuming no dispersal constraints

(Gaston 1994, 2003). In reality, most species are not found in all places that meet their ecological requirements (Rapoport 1982). An early explanation for this phenomenon suggested that more ancient species have had more time to expand their ranges, and thus should have greater range occupancy and size (Willis 1922). Evidence for this ’age-area’ hypothesis over macroevolutionary timescales is mixed (Jablonski 1987; Paul et al. 2009), and subsequent work has found niche breadth and position within a community to be a stronger predictor of range occupancy and size (Hurlbert & White 2005, 2007; Morin & Lechowicz 2013). However, Willis

(1922) may have been the first to propose temporal dispersal lags influence species’ ranges.

The realized proportion of a species’ potential range, or “range filling”, is a metric of climatic equilibrium (Svenning & Skov 2004). Potential ranges have previously been modeled using SDMs (Svenning & Skov 2004; Munguía et al. 2008, 2012; Dullinger et al. 2012; Nogués-

Bravo et al. 2014; Booth 2016), which correlate climate variables with realized ranges influenced by biotic interactions (Loehle 1998; Wisz et al. 2013) to estimate a species’ invadable range. Range underfilling thus results either from strong associations to non-climatic factors (e.g. soils or biotic interactions) which did not correlate with environmental variables or from dispersal lags in response to past climate change. Previous work has found that range filling

2 increases with latitude (Svenning & Skov 2004; Munguía et al. 2008; Nogués-Bravo et al. 2014) and proximity to ice age refugia (Svenning & Skov 2007; Normand et al. 2011). Range filling is also higher among volant mammals (Munguía et al. 2008) and in plants with greater capacity for long distance dispersal (Normand et al. 2011). Thus, range filling appears to be an effective proxy for postglacial dispersal ability.

Range shape, which is the ratio between the latitudinal and longitudinal extent of a range, can be used to further detect dispersal limitations in under-filled ranges (Baselga et al. 2012).

Under neutral ecological dynamics (Hubbell 2001), a species should expand its range equally in all directions, resulting in circular ranges with shape ratios equal to 1 (Cain 1944; Castro-Insua et al. 2018). These ranges are limited by the species’ intrinsic ability to disperse over time, influenced by their functional traits (Cain 1944; Rapoport 1982; Castro-Insua et al. 2018).

Species with greater dispersal distances, more abundant propagules, shorter generations, or generalist ecological preferences should be better at establishment (Angert et al. 2011) and attain larger range sizes (Baselga et al. 2012). This should be particularly true for trees, for which seed weight, seed production, and generation times can span orders of magnitude even within the same community. Given enough time to expand, species will either run into extrinsic dispersal barriers (e.g., mountain ranges, ice sheets) or fill their potential climatic ranges. In North

America, the north-south orientation of mountain ranges and coastlines (Brown 1995) should cause species limited by extrinsic barriers to have range shape ratios >1. In contrast, because climate is more strongly controlled by latitude than longitude, climatically determined ranges should have range shape ratios <1 (Cain, 1944; Rapoport 1982). Thus by analyzing the shape of a range we can disentangle the relative influences of intrinsic and extrinsic dispersal limitations from climatic limitations (Baselga et al. 2012).

We performed a range filling analysis (sensu Svenning & Skov 2004; see Figure 1 for walkthrough) of North American trees and shrubs (447 species) to 1) test the assumption of climatic equilibrium and 2) detect if dispersal is a significant limitation on equilibrium. We analyzed range shapes to help detect the drivers of underfilling and compared to randomized ranges as a null model. Providing climate strongly controls species’ ranges, range shapes should match their climatic expectations, and filling should be higher across realized ranges than their randomized counterparts. Dispersal limitations should cause the opposite. We find measures of climatic equilibrium strongly correlate with range size and discuss the implications of these results on our understanding of range controls.

Methods We obtained species’ realized range data (Figure 1a) from the USGS tree atlas

(Department of the Interior 2006), which is a digitized version of the expert ranges drawn by

E.L. Little (1971). The USGS ranges intentionally do not include populations transplanted by

European colonists, and so they represent the best available approximation of the natural ranges of North America’s woody species (though they do not account for the influence of indigenous peoples). We started with the full list of 679 species mapped by Little, updating nomenclature and taxonomy using The Plant List (2013). We combined ranges for species that had been taxonomically lumped since the publication of the atlas. We set the geographic extent from the majority of their ranges outside the study extent, and 2) species with ranges <20,000 km2, which were too small relative to the grain size and extent of our analysis to be accurately modeled. By

Figure 1. Workflow Diagram. a) Realized range data for Gleditsia triacanthos (honey locust). b) Climate data used; see methods for details. c) Example potential range prediction for honey locust. Range filling is the sum potential range values realized divided by the total predicted values across the extent. d) Histogram of range filling for realized and randomized ranges. excluding the smallest ranges, we are likely to omit species with the strongest dispersal limitation (Normand et al. 2011), biasing our results towards higher climate equilibrium. Our final list included 447 species, with a minimum of 14 presences for the smallest-ranged species at the coarsest scale we modeled.

Species ranges were rasterized, and presence points were generated for each grid cell.

Pseudo-absences were generated in all areas >100 km from species’ realized ranges at a 1-degree resolution. We ran the analysis at 5, 10 and 20 arcminutes (roughly equivalent to 9 x 9 km, 18 x

18 km, and 36 x 36 km respectively; see Appendix Figures 1 & 2). At the finest scale, a grid cell is equivalent to the smallest satellite population (about 9km in diameter) mapped by Little. We

5 kept climate, species ranges, and potential range models at the same spatial resolution for each iteration to avoid issues of scale mismatch. Potential range extent values used for range shape measures were taken using a 95% raster volume of an ensemble of all three modeled ranges.

Four climate variables with physiological mechanisms for plant performance were used to construct potential ranges: 1) minimum temperature of the coldest month, 2) growing degree days (base 5 °C), 3) water balance (precipitation divided by potential evapotranspiration), and 4) precipitation timing (summer precipitation divided by winter) (see Figure 1b). Cold temperatures can allow for the formation of ice crystals in xylem and other tissues, causing mortality, and often constitute a hard limit for plant survival (Sakai & Weiser 1973). Growing degree days

(GDD) is an index of growing season intensity, calculated as the annual net heat accumulation above a specified threshold, and is an important proxy for phenological constraints on plant growth (Prentice et al. 1992; Sykes et al. 1996; Chuine & Beaubien 2001). Water balance was calculated by dividing the monthly sum of precipitation by potential evapotranspiration

(Thornthwaite 1948; Thornthwaite & Mather 1955). This metric more accurately represents drought stress and moisture availability experienced by a plant than raw precipitation

(Stephenson 1990), although it does not account for the influence of soil type. Precipitation timing is a seasonality index, calculated by dividing the sum of summer precipitation (June, July,

August) by the winter sum of precipitation (December, January and February). This index is important for distinguishing biomes among the arid Southwest, as well as separating climates in the Pacific Northwest from those in along the North Atlantic. Both precipitation variables were log-transformed to better constrain potential ranges, as these variables commonly spanned orders of magnitude within a species’ range. All climate data are from WorldClim (Hijmans et al. 2005) except for potential evapotranspiration, which is from CGIAR (Trabucco & Zomer 2010).

Potential ranges were estimated using three modeling algorithms: bioclimatic envelopes

(BIOCLIM; Busby 1991; Booth et al. 2014), Maximum Entropy (MaxEnt; Phillips et al. 2004,

2006), and Generalized Additive Models (GAMs; Hastie & Tibshirani 1990; Guisan et al. 2002).

Rather than thresholding the model predictions to generate presences and absences, range filling was calculated for each replication by summing the predicted values at each cell within a species’ realized range and dividing it by the total sum of values at every cell across the entire continent (Figure 1c). This results in more conservative range filling estimates, since areas of highest predicted suitability tend to be within the realized ranges. To obtain a single estimate of range filling for a given species, we took an average of range filling across all replications. First, range filling values for each algorithm were rescaled by the maximum range filling value for obtainable for that algorithm across all species (0.975, 0.793, and 0.795 for BIOCLIM, MaxEnt, and GAMs, respectively). For MaxEnt and GAMs, areas of low suitability approached but did not equal zero. Because we did not threshold, this resulted in inflated sums of potential range values and lower maximum range filling for these algorithms. Rescaling the maximum values to

1 allowed us to standardize range filling values across algorithms and avoid thresholding, since predicted values are not equivalent for different algorithms. Finally, a mean of the range filling for every replication was taken for each species. BIOCLIM and MaxEnt models were created using the R package ‘dismo’ (Hijmans et al. 2016), and GAMs were built using the ‘mgcv’ package (Wood & Wood 2013).

To test for the influence of non-ecological biases and geometric constraints on range filling, we used a spreading-dye algorithm to generate random ranges as a null model (Jetz &

Rahbek 2002). Spreading-dye models create randomized shapes by simulating a volume of liquid being poured onto a flat surface (Jetz & Rahbek 2002). Randomized ranges were drawn for all

447 species (see Appendix Figure 4 for examples). These models assume that species ranges are contiguous and are bounded by coastlines (Rahbek et al. 2007). In each case, simulated ranges originated randomly within the study area, and expanded until their size was equivalent to the actual realized range of the species. This resulted in a set of ranges which were spatially randomized (i.e., non-ecological), while maintaining the same range size distribution observed in the USGS dataset to account for the influence of range size. Range filling was then calculated on the randomized ranges following the same protocol as the realized ranges above.

All statistical analyses were performed in R version 3.3.3 (R Core Team 2018). A generalized linear model was fit between range size (log-scale) and range filling using a

Gaussian distribution in both realized and randomized ranges. Residuals from the model fit were then used to calculate a measure of range filling while controlling for the influence of range size.

Linear models were fitted between range filling residuals and range size against latitude and longitude to test for spatial patterns in range filling. A linear model of the influence of range size on shape ratio (latitudinal extent/longitudinal extent) was fitted for realized and potential ranges.

A linear model was again fitted between potential range size and range filling residuals. Variable Range Filling Residuals Randomized Filling Range Size (Realized) 0.609*** - -0.291*** Range Size (Potential) 0.087*** -0.249*** 0.221*** Range Shape (Realized) -0.136*** 0.004* -0.082*** Range Shape (Potential) -0.053*** 0.003 (n.s.) -0.019*** Latitude 0.025*** -0.092*** 0.132*** Longitude 0.205*** 0.095*** 0.009** Table 1. Range Filling Correlates. Adjusted R-squared values from fitted models between independent variables (rows) and dependent variables (columns). Sign reflects if the trend was positive or negative. Significance is represented as follows; * for p <0.1, ** for p <0.05, *** for p <0.01, otherwise non-significant (n.s.)

Results We found significant deviations between potential climatic and realized ranges, with mean and median range filling of 48.7% and 49.7%, respectively. Realized range size was the strongest correlate of range filling (adj. r2 = 0.61) among the variables we analyzed (see Table 1), with species in the largest range size quartile having higher range filling (mean 75.2%) than species in the smallest range quartile (mean 26.5%). Range filling was significantly higher in realized ranges than for randomized ranges (p = 0.0125), and there was a significant interaction between their range size-range filling relationships (Figure 2), with a steeper slope in realized

Figure 2. Range Size-Range Filling. Range size (x-axis) against range filling for 447 species. The correlation is stronger in realized (green) than randomized ranges (gray), indicating ecological drivers of range filling. However, the impact of geometric effects seen among randomized ranges is significant. The interaction in realized and randomized ranges shows small-ranged species under-perform null while larged-ranged species over-perform.

9 ranges (slope = 0.29) than randomized ranges (slope = 0.17). In other words, large-ranged species performed better than the null model, while small-ranged species performed worse than expected. Range size and range filling were higher in the east (adj. r2 = 0.11 and 0.09, respectively), and range size increased with latitude (adj. r2 = 0.21) while range filling decreased

(adj. r2 = 0.093) (Figure 3). Realized range size positively correlated with potential range size, while range filling showed a negative correlation (Figure 4). The ratio between longitudinal and latitudinal range extents (range shape) decreased with increasing range size in both realized (adj. r2 = 0.34) and potential ranges (adj. r2 = 0.09) (Figure 5). The smallest-ranged species approached and exceeded range shape ratios of 1 (respectively indicating intrinsic and extrinsic dispersal limitations), while larger-ranged species more closely matched potential range shapes (Figure 5).

Figure 3. Range Filling in Space. Point color reflects the range filling residuals (distance from regression line in Fig 2.) and point size is range size. Inset plots in the lower left show these trends as xy-plots. Range filling increases towards the south and the east and is also high in areas along the pacific coast.

Discussion We found evidence that the potential climatic ranges of North

American trees and shrubs are under-filled, with mean range filling values of just under half

(48%). The strongest predictor of range filling was realized range size, with higher filling in large- ranged species and lower filling in small-ranged species (Figure 2).

Range shape ratios indicate that large-ranged species are climatically restricted, while small- ranged species had shapes indicative of dispersal limitations.

Overall, our results suggest Figure 4. Potential Range Size. Potential range the importance of climate as a control on size against range filling residuals (top) and realized range size (bottom). ranges increases with size, which is consistent with previous work attributing contemporary climatic disequilibrium to post-glacial dispersal limitations (Svenning & Skov 2004; Munguía et al. 2008; Dullinger et al. 2012;

Nogués-Bravo et al. 2014).

Using a geometric null model, we were able to replicate the range-size range-filling relationship and explain half of its variance (Figure 2). Given that our randomized ranges were drawn without consideration for climate or ecology, they allow us to distinguish geometric influences from biogeographic patterns (Jetz & Rahbek 2002; Colwell et al. 2004), and predict the range filling for a given range size based only on topology. Randomized ranges fared worse overall at range filling, which was expected, since realized ranges should show greater ecological coherence than random ellipses. Yet, the fact that randomized ranges replicated the overall patterns found in realized ranges suggests that range filling and its correlation with range size is partially the result of non-ecological factors, which we attempted to account for in our analysis.

First, mismatches between the grain size of climate and species’ range data could cause us to overestimate potential climate areas, reducing range filling. This should be especially true for

Figure 5. Range Shape Ratios. Longitudinal extents plotted against latitudinal ranges (left) for realized ranges (green) and potential (blue) with a dashed 1-1 line. Overall, potential ranges were larger, especially in longitudinal extent, and had a narrower range than the realized ranges. Shape ratios (right) decrease with realized range size, approach potential climatic matches in the largest-ranged species. Shape ratios of small-ranged species approached and exceeded 1, indicating dispersal limitations. Shape ratios of potential ranges are lower overall owing to being constrained by latitude.

12 mountains, which have a greater climatic diversity than a topographically homogenous region of equal area. Species that occupy narrow elevational bands could be recorded as being present in climates in which they do not actually occur, thus over-predicting their potential range areas. To control for the influence of grain size, we ran the analysis at three spatial scales (5, 10 and 20 minutes of a degree). Range filling was significantly lower at finer spatial scales of analysis, although this trend was only true for MaxEnt and GAMs, and was overall weak (adj. r2 = 0.007; see Appendix Figure 2). Secondly, SDMs may be biased toward type I (commission) errors over type II (omission), resulting in over-predicting the area of suitable climate and therefore underestimating actual range filling. Our approach counteracts this bias by not thresholding model predictions and maintaining a probabilistic measure of potential range. Thus, areas of highest suitability will tend to be within the realized ranges used to build the model, and areas of potential range outside the realized range will tend to be of lower suitability. Finally, the positive relationship between range size and range filling could be an artifact of geometry. As a range’s size increases, it takes up a greater amount of the total available land area on a continent, which increases the probability that potential range space is occupied. For example, a species which has a range size of 107 km2 is occupying nearly 50% of the total area available in North America. A species this widespread would be expected to inhabit about half of its potential climatic range by chance alone. This geometric constraint constitutes a hard lower-limit for range filling that scales with range size, and likely helps drive the positive correlation between range filling and range size. We can control for this bias by examining range filling as a residual from the range-size range-filling relationship (Figures 2 & 3). Despite the influence of these non-ecological biases, we believe range filling reveals ecological processes governing range size. The relationship between range size and filling is much stronger and steeper in realized ranges than randomized

13 ranges (Figure 2). While about half of the variation in range filling appears attributable to non- ecological biases, the significant interaction found in the realized ranges suggests the remaining variation is likely resulting from ecological processes.

At the continental scale of our analysis, realized ranges are expected to be primarily controlled by climate (McGill 2010). Species’ absences from their modeled potential climatic range can be explained in two ways. Providing the SDMs accurately modeled species’ potential habitat, unrealized portions of ranges should result from intrinsic or extrinsic dispersal limitations (Gaston 2003). Alternatively, unrealized areas could be uninhabitable due to factors not captured by our SDMs, such as soils, disturbance regimes, or undetected-but-important biotic interactions like competition, facilitation, and symbioses (Wisz et al. 2013). These non-climatic processes are thought to be important primarily at smaller ecological scales (McGill 2010), and have long been considered negligible at influencing range limits at larger scales. However, increasing evidence supports the idea that non-climatic factors are limiting species ranges even on macro-scales (Araújo & Luoto 2007; Boucher-Lalonde et al. 2013; Wisz et al. 2013; Walthert

& Meier 2017). Our results support this interpretation for small-ranged species, which performed worse at range filling than our randomized null model and had range shapes ratios which deviated from the shapes associated with climatic restrictions. Large-ranged species outperformed the null model and more closely conformed to their potential climatic distributions.

Together these results indicate the factors controlling ranges and the importance of climate varies with range size. We suggest two non-exclusive mechanisms to explain this pattern; niche breadths and dispersal lags.

Large-ranged species may have higher range filling because they can outperform other species due to broader niches. Niche theory posits that a ‘generalist’ species which is broad in its

14 habitat and resource requirements would attain a larger range size than a ‘specialist’ (MacArthur

1972). Adaptations to highly variable, seasonal environments (Janzen 1967; McCain 2009), low energetic availability (Morin & Chuine 2006), cold temperatures (Pither 2003), or stressful edaphic conditions (Stephenson 1990) are all thought to allow trees to obtain large range sizes.

Morin & Lechowicz (2013) found that increased environmental niche breadths, measured from both climatic and soil variables, were positively correlated with range size in North American trees. Studies of animal taxa have found niche position along a resource-defined axes to be a strong predictor of range size and occupancy (Gregory & Gaston 2000a; Tales et al. 2004; Heino

2005; Hurlbert & White 2005, 2007), where species with more ‘central’ niche positions (i.e. use common resources) attain larger ranges while being more frequent throughout. Thus, the broad tolerances and generalist strategies associated with large range sizes are likely advantageous for potential range filling.

Small range sizes are a form of ecological rarity which can result from ecological specificity at either regional or local scales (Rabinowitz 1981). Regionally restricted small ranged species likely represent specialization to rare, restricted macro-climatic conditions

(Ohlemüller et al. 2008). A classic example of this sort of species is Carnegiea gigantea

(saguaro cactus), which is an indicator for the Sonoran desert’s unique bi-seasonal rainfall. This sort of climatic specialization would result in high range filling despite small range sizes, counter to the positive trend we found between range size and range filling. When we converted range filling to residuals from the range-size range-filling relationship, we found that species with smaller potential ranges tended to have higher residuals, providing evidence of this adaptation to unique climates (Figure 4). Such species were mostly found in Mediterranean (e.g. Aesculus californica) or subtropical climates (e.g. Sabal palmetto). Though not widespread, these species

15 were relatively successful at filling their potential ranges (see Appendix Figure 4.1), and their realized ranges are controlled more by the limited extent of their potential ranges than non- climatic factors.

Yet, broadly we did not find species with small ranges and high range-filling, as expected if small ranges are restricted by rare macro-climates. Instead, small-ranged species showed low potential range filling, consistent with habitat specialization and restriction to local conditions like soils and microclimates (Rabinowitz 1981; McGill 2010). Cladastris kentuckea

(yellowwood), with 7.8% range filling, is one such example (see Appendix Figure 4.3). Natural populations of C. kentuckea are almost exclusively found in alkaline, calcareous soils (Hill

2007), a factor not considered by our model. While C. kentuckea can grow in acidic soils and a variety of climates when cultivated, its inability to fix nitrogen like many other fabaceous plants

(Graves & Van de Poll 1992) likely makes the species non-competitive in other habitats. Such small-ranged species may prove a challenge for forecasters. Further, climate change could make the macro-climates where habitat specialists are found unsuitable, causing a spatial disconnect between a species’ climatic and non-climatic niches. Given that small range sizes are a strong predictor of extinction risk (Gaston & Fuller 2009), small-ranged trees may require aggressive conservation measures, like managed relocation (Barlow & Martin 2007; Schwartz et al. 2012).

Large-ranged species may have high range filling because of reduced dispersal lags in response to post-glacial warming. Quaternary climate changes drove continental-scale range shifts for many species, and paleoecological records provide evidence for both rapid equilibrium

(Webb 1986; Prentice et al. 1991; Williams et al. 2004; Ordonez & Williams 2013) and lagged disequilibrium responses across woody taxa (Davis 1989; Johnstone & Chapin 2003; Gavin &

Feng 2006; Gavin 2009; Elias 2013). Species which maintained climatic equilibrium should have

16 generally expanded their ranges during the Holocene as the retreating ice sheets exposed new areas. Species which could not keep pace (or which were limited by narrow niche restrictions to non-climatic factors as discussed above), would have maintained relatively smaller range sizes.

This disparity would result in the bimodal range filling patterns observed among realized ranges seen here and in previous work (Svenning & Skov 2004; Munguía et al. 2008); large-ranged, high-filling species at high latitudes; and small-ranged, low-filling species at low latitudes.

We find several lines of evidence suggesting dispersal limitations are a significant constraint on range filling. Range filling was lower in mountainous regions when controlled for the influence of range size (Figure 3). This is consistent with mountains functioning as barriers to dispersal, but also as refugia which promote greater long-term local survival (Sandel et al. 2011) given the relatively short distances required to track climates across elevational bands. Range shape ratios further indicate the influence of dispersal limitations amongst small-ranged species, while large-ranged species better matched their climatic expectations (Figure 5). Together these results support the interpretation that small-ranged, low-filling species failed to track their suitable climates following deglaciation.

Disparities among range shift rates have been attributed to a variety of autecological factors that can impede dispersal, colonization, and expansion rates. Proximity to glacial refugia is a strong predictor of regional species richness in Europe (Svenning & Skov 2007; Normand et al. 2011), as species with glacial refugia closer to the ice sheet margins could more readily colonize newly available areas. Subsequent migrants would face increased competition for resources and space in newly colonized habitats (Moorcroft et al. 2006; Svenning et al. 2014) due to priority effects (Lockwood et al. 1997). Certain functional traits may have provided an advantage (or disadvantage) during the postglacial race for habitat. Adaptation and reliance on

17 extinct Pleistocene megaherbivores, which were likely effective long-distance dispersal agents for large-fruited trees (Janzen & Martin 1982; Gill 2014), have been used to explain some constrained distributions (Barlow & Martin 2007; Zaya & Howe 2009). European plants with spores or more long distance dispersal mechanisms have higher range filling and size today

(Normand et al. 2011), and trees with lighter seeds and more diverse below-ground fungi symbioses exhibited faster rates of postglacial migration in North America (Pither et al. 2018).

Despite this, attempts to attribute range shift rates, range sizes, or range filling to species’ intrinsic dispersal abilities have been surprisingly unsuccessful (Lester et al. 2007), and the correlation between species’ functional traits and their range properties is weak at biogeographic scales (Munguía et al. 2008; Angert et al. 2011; Nogués-Bravo et al. 2014; Harnik et al. 2018).

Further research on how autecological factors interact with ranges is needed, and could increase the accuracy of our forecasts and better focus resources towards the most vulnerable species

The question of climatic equilibrium has been a persistent debate in Quaternary paleoecology (Webb 1986; Davis 1989). Pollen data suggests vegetation is broadly in equilibrium with climate at millennial timescales (Prentice et al. 1991; Shuman et al. 2004;

Williams et al. 2004, 2010), though this may be challenged during periods of rapid climate change (Ordonez 2013). Pollen data trades taxonomic resolution and a bias towards common, wind-dispersed trees for broad spatial and temporal coverage, which may account for the high degree of equilibrium recorded. We also found that large-ranged species, many of which are common pollen taxa, are generally in equilibrium with climate, but small-ranged species (often poorly-represented in pollen records) tend to have disproportionately lower range filling, indicating that dispersal and other non-climatic factors limit their range size and filling. Thus,

18 measures of climatic equilibrium are heavily influenced by the taxonomic, temporal, and spatial scale of the analysis.

Ecological processes are highly scale-dependent (McGill 2010), so it is unsurprising that the mechanisms controlling ranges would depend upon the scale of the species’ geographic range. Based on the classic understanding of geographic ranges, even relatively restricted species are strongly influenced by climate (Grinnell 1917). In this way, climate is a control on individual species ranges arising from the ‘bottom-up’ (Boucher-Lalonde et al. 2014). Instead, our analysis supports climate as top-down control which constrains species pools, but does not strongly control individual species’ ranges (Boucher-Lalonde et al. 2014). This is a subtle change to traditional range theory, which posits climate is the strongest range control at continental scales, while the importance of habitat increases at local scales (Andrewartha & Birch 1954; McGill

2010). Just as climate is the most important (though not sole) control on species ranges at larger spatial scales, it is also the most important for species with the largest ranges. This means that the species for which we generally have the best range data (modern and fossil) also demonstrate the strongest signal of climatic limitations. As range sizes decrease, so does the importance of climate in determining distributions, while the influence of non-climatic factors like dispersal and habitat specializations increase. This is a bittersweet conclusion for conservationists.

Because macroclimates do not strongly control their distributions, the ranges of small-ranged species may be less sensitive to the direct impacts of climate change. However, this will make their responses more unpredictable and complicate our efforts to model species’ distributions, especially as novel species assemblages form as the result of individualistic responses to climate change (Jackson & Overpeck 2000; Jackson & Williams 2004).

Conclusion We found evidence of widespread climatic disequilibrium among North American tree species, with the mechanisms controlling range filling varying across range size. Small-ranged species show the strongest signals non-climatic limitations, with postglacial dispersal lags appearing as significant contributors to their contemporary range limits. The degree which the assumption of climatic equilibrium was broken causes concern for attempts to forecast species’ ranges using SDMs. Management efforts for rare, small-ranged species will need to account for a complex interplay of factors in addition to climate to predict responses to climatic change.

Landscape use and habitat fragmentation will impede range shift rates (Lazarus & McGill 2014), leading to increased climatic disequilibrium in the near future. As such, managed relocation strategies and horticultural naturalizations may need to be embraced to ensure persistence for vulnerable species (McLane & Aitken 2012; Schwartz et al. 2012; Bellemare & Deeg 2015).

CHAPTER II: A MILE WIDE AND AN INCH DEEP: LARGE-RANGED PIONEER TREES FILL LESS OF THEIR POTENTIAL CLIMATIC RANGES THAN SMALL-RANGED CLIMAX SPECIALISTS

Chapter Abstract Understanding the controls of geographic distributions is not only of significance to ecological theory, but will also improve forecasts of biodiversity loss under climate change. Here we test the relative influences of dispersal and life history traits with climate and geography in determining the range size and range filling among North American woody plants. While climate is the strongest control on range size, range filling is influenced by a variety of ecological interactions. While large range sizes were most strongly associated with deciduousness and high

SLA values, high range filling was correlated to heavy and animal dispersed seeds. We find reversal of trends between range size and range filling among seed mass and wood density, which can be attributed to different strategies along a colonization and specialization trade-off.

Integrating range filling and trait-based approaches can thus inform future biogeographic responses to climate change.

Introduction Global temperatures are forecasted to warm by over 2 °C in the next century (IPCC

2014), a rate faster than any change in the last 10,000 years (Marcott et al. 2013). Predicting which species will have the most difficulty maintaining climatic equilibrium is a central goal for ecology and would help to focus limited conservation resources. Conserving Earth’s biodiversity is further challenged by the vast number of species without descriptions (Mora et al. 2011) or accurate distributions (Ficetola et al. 2013). Threats to biodiversity present a ‘big data’ problem, and one solution is to identify signals across easily measurable, functionally based traits rather

21 than studying individual species (McGill et al. 2006; Foden et al. 2013; Pacifici et al. 2015;

Clark 2016). As species show remarkable differences in these traits, their variation should have predictable biogeographic consequences. For example, 'pioneer’ species with greater dispersal capacities, reproductive rates, and ecological generalization display faster range shift rates and larger range sizes (Gregory & Gaston 2000b; Morin & Chuine 2006; Angert et al. 2011; Morin

& Lechowicz 2013). While considerable attention has been paid to the role of dispersal traits in climatic disequilibrium (Normand et al. 2011; Nogués-Bravo et al. 2014), most attempts at linking trait variables to biogeographic metrics have yielded low explanatory power. Further, other biological processes (e.g., competition, succession) may play a significant role and are often ignored as they remain difficult to quantify, thus their influence on climatic equilibrium remains mostly speculative.

The realized portion of a species’ potential climatic range, or “range filling,” is a metric of climatic equilibrium thought to be highly influenced by dispersal (Nogués-Bravo et al., 2014;

Svenning & Skov, 2004). Low range filling has been found in North American (Chapter I) and

European woody plants (Svenning & Skov 2004, 2007; Normand et al. 2011; Nogués-Bravo et al. 2014), and Central American mammals (Munguía et al. 2008), and have been attributed to dispersal lags following post-glacial warming. Nogués-Bravo et al. examined the correlation between range filling and dispersal traits and found seed mass in trees to negatively correlate with range filling (2014), although they detected no significant differences between wind or animal dispersal syndromes on range filling. Range filling is influenced by more than species’ ecology alone, and proportional range filling is strongly correlated with range size (Chapter I).

Measuring the residuals from this relationship produces a metric of range occupancy that is range-size independent, and thus more likely to reveal ecological patterns like post-glacial

22 dispersal limitations. Range filling and species diversity have been shown to be strongly associated with accessibility to ice-age refugia in Europe (Svenning and Skov 2007).

Here we analyzed four key functional traits (seed mass, specific leaf area, maximum height, and wood density) which can be used to categorize plant strategies (Westoby 1998;

Westoby & Wright 2006; Falster et al. 2011) to test their contribution towards shaping range sizes and potential range filling amongst North American woody plants. We compared the predictive power of traits to known associations between climate and geography (Chapter I).

Providing that range filling is the result of ecological interactions, we expect that functional traits should play a significant role in determining range filling. Specifically, we expect that seed mass and wood density should be negatively correlated with range filling and size, and SLA and height positively. We expect that dispersal traits should be the most strongly associated with range filling, providing it is a proxy for dispersal. If range filling is also the result of niche limitations (Chapter I) or accessibility (Svenning & Skov 2007; Normand et al. 2011), these would manifest as contributions by climate and geography, respectively.

Methods We obtained data for species’ realized ranges from the USGS Atlas of United States

Trees (Department of the Interior 2006). The USGS ranges are a digitized version of the expert- informed ranges mapped by E.L. Little, Jr. (1971), who approximated the natural ranges of North

America’s woody plants before European colonization. We used these realized ranges to measure potential climatic range filling for 447 species from 22° N (where temperate databases reach the limit of their useful coverage) to the Arctic coast at 72° N (near tree line). We rasterized the USGS ranges at a scale of 5, 10 and 20 arcminutes (roughly equivalent to 9 x 9 km, 18 x 18 km, and 36 x 36 km respectively) per grain. At the finest scale of our analysis, a grid

23 cell is approximate equivalent to the smallest satellite population (about 9km in diameter) mapped by Little (1971).

We used the following climate variables to model potential ranges: minimum temperature of the coldest month, 2) growing degree days (base 5 °C), 3) precipitation balance (precipitation divided by potential evapotranspiration), and 4) precipitation timing (summer precipitation divided by winter). These variables all have direct physiological mechanisms affecting plant performance: cold temperatures are a strong control limiting for plant survival (Sakai & Weiser

1973). Growing degree days (GDD; the annual net heat accumulation above a specified threshold) is an index of growing season intensity which is an important phenological constraint

(Prentice et al. 1992; Sykes et al. 1996; Chuine & Beaubien 2001). We calculated precipitation balance by dividing the monthly sum of precipitation by potential evapotranspiration

(Thornthwaite 1948; Thornthwaite & Mather 1955), which is a more accurate metric of drought stress and moisture availability than absolute precipitation (Stephenson 1990). Precipitation timing (the sum of June, July, and August precipitation divided by the winter sum of December,

January and February precipitation) indicates the season of predominant rainfall, diagnostic for the deserts of the Southwest as well as Mediterranean and temperate rainforest climates along

Pacific coast. We log-transformed both precipitation variables as they often span orders of magnitude within a species’ range. Climate data are from WorldClim (Hijmans et al. 2005) and

CGIAR (potential evapotranspiration; Trabucco & Zomer 2010).

We estimated potential ranges using three modeling algorithms: bioclimatic envelopes

(BIOCLIM; Busby 1991; Booth et al. 2014), Maximum Entropy (MaxEnt; Phillips et al. 2004,

2006), and Generalized Additive Models (GAMs; Hastie & Tibshirani 1990; Guisan et al. 2002).

Rather than thresholding the model predictions to generate presences and absences, range filling

24 was calculated as the sum of predicted values within a species’ realized range and divided by the total sum of values across the study extent. This produces more conservative range filling estimates, as areas of highest predicted suitability tend to be within the realized ranges. To obtain a single estimate of range filling for each species, we averaged range filling across all predictions. Potential range areas were obtained by a raster volume using the package

‘spatialEco’ (Evans 2015), which calculates the smallest contiguous concave area containing

95% of the values. BIOCLIM and MaxEnt models were created using the R package ‘dismo’

(Hijmans et al. 2016), and GAMs were built using the ‘mgcv’ package (Wood & Wood 2013).

We analyzed the following key functional traits: seed mass, wood density, specific leaf area, maximum height. We further include following categorical traits which are strong correlates of these functional traits (in corresponding order): dispersal syndrome, angiosperm/gymnosperm, leaf phenology, and growth form. Traits were retrieved from the Botanical

Information and Ecology Network

(BIEN; Enquist et al. 2009) and

USDA PLANTS characteristics (U.S.

Dept. of Agriculture 2017b) databases. Where species were missing data for dispersal syndrome, leaf phenology, growth form, and Figure 6. Functional Trait Correlation Matrix. Traits height, we used values from the U.S. used correlation little with each other except wood Forest Service’s species profiles (U.S. density, which is moderately correlated with seed mass and height 25

Dept. of Agriculture 2017a), literature searches, and expert judgement. Dispersal syndrome was assigned at the genus level (except for Pinus) as either animal or wind. Because Pinus is known to have both small seeded species which are almost exclusively dispersed by wind and large seeded species that are dispersed by animals (Vander Wall & Balda 1977; Greene & Johnson

1992; Vander Wall 1992, 2008; Benkman 1995; Lanner 2000), we classified species within the genera Pinus individually by seed weight following Benkman (1995). There were 53 species which could not be classified as either wind or animal which were omitted from analyses of dispersal syndrome. Growth forms were categorized as either shrub or tree. For species classified as both shrubs and trees, we assigned them to the growth form which was consistent in both the

USDA and BIEN databases. In cases where both growth forms were shared in each of the trait databases, these species were included in analyses for both growth forms. For leaf phenology, only species which regularly lose their leaves on an annual basis were classified as deciduous; otherwise species were assigned to evergreen.

To test for phylogenetic signal, we used a dataset prepared by Ma, Sandel, and Svenning

(2016). This dataset is a compilation of Leslie et al.'s (2012) gymnosperm phylogeny and Zanne et al.'s (2014) angiosperm phylogeny with additional relationships for species in the USGS tree atlas but which were missing from the previous phylogenies (Ma et al. 2016). We then used

Pagel’s λ to assess phylogenetic signal (Pagel 1999) amongst range size, range filling, seed mass, wood density, specific leaf area and height

In Chapter I, we found that range sizes explain 61% of the variation in proportional range filling. Thus, we used a sequential regression approach by first taking a generalized linear model between realized range size and range filling. By analyzing the residuals from the range size/range filling relationship (hereafter referred to as range filling), we obtained a size-

26 independent metric of range filling. We performed variance partitioning analysis (using the R package ‘vegan’) to explain range size and range filling by three factors: functional traits, geography, and climate. We used the coordinates of species’ range centroids to a measure geographic distance, which serves as a proxy for extrinsic dispersal limitations like post-glacial accessibility and mountain ranges. For climate, we used the log-transformed area of potential climatic range predicted by our species distribution models and included all four functional traits

(values were log-scaled).

Results Using variance partitioning between functional traits, climate (potential area determined by species distribution models), and geography (latitude and longitude of realized range centroids), we were able to explain 85% of the variation in range size and 51% of the variation in range filling (Figure 7). Climate was the greatest contributor to range size, explaining nearly 1.5 times the variation as geography and 3 times that of traits. For range filling, climate was still the

Figure 7. Variance Partitioning Summary. Results from a variance partition for range size (left) and range filling (right). We used three separate factors; traits (seed mass, wood density, specific leaf area, and height), geography (range centroid latitude and longitude), and climate (potential range area predicted by species distribution models).

27 most important factor in terms of unique variation explained (followed by geography and traits), although differences between factors was not nearly as great and each factor contributed similarly towards predicting overall variation.

Among the functional traits we analyzed, range filling was positively correlated with seed mass, wood density, specific leaf area, and height, in descending order of strength (Figure 8).

Angiosperms and animal-dispersed species also showed higher range filling over gymnosperms or wind dispersal. Conversely, range size was negatively correlated with seed mass and wood density, and positively correlated with specific leaf area (Figure 8). Deciduous species and those with wind dispersal had larger range sizes compared to those with animal dispersal or evergreen leaves. Height was not significantly correlated with range size, even when accounting for differences in growth form.

All traits analyzed showed significant phylogenetic signal, revealing that closely related species were more like each other than random (Table 2). We found that range size is not phylogenetically conserved, with very little to no signal, however, the phylogenetic signal approached significance for range filling residuals, reflective of functional traits influencing range filling.

Figure 8. Traits Against Range Metrics. Seed mass (upper left), wood density (upper right), specific leaf area (lower left), and maximum height (lower right) against range filling (top inset plots) and range size (bottom inset plots). Results significant p < 0.05 are shown.

Discussion We find that dispersal and life Variable λ p Range Size 0.326476 1.12E-05 history traits have detectable influences Range Filling (%) 0.043442 0.125402 on range size and range filling. Range Range Filling (Residuals) 0.488457 1.47E-12 Specific Leaf Area 0.824996 2.29E-15 filling is more strongly influenced by Seed Mass 0.985132 6.53E-39 Maximum Height 0.843694 8.08E-58 traits than range size is influenced by Wood Density 0.749289 1.11E-21 traits, suggesting that range filling can Table 2. Phylogenetic Signal Table. Pagel’s λ help reveal ecological processes at ranges from 0 (no signal) to 1 (very high signal). biogeographic scales more clearly than geographic ranges alone (Figure 7). Range filling was influenced the most by seed mass and dispersal syndrome, which supports the interpretation that range filling is an effective proxy for dispersal ability (Svenning and Skov 2004, Munguía et al.

2008, Normand et al. 2011, Nogués-Bravo et al. 2014, Chapter I). We found inverse relationships for seed mass and wood density with range size and range filling. Species with light seeds and less dense wood have larger ranges with lower filling, while species with large seeds and dense wood have smaller ranges but filled their potential climatic ranges better, indicating a trade-off in range patterning which we attribute to differences between ‘pioneer' and ‘climax’ life-history strategies.

Wind-dispersed and small-seeded species have larger range sizes, which we attribute to the fact that lighter seeds are generally more of an ‘R’ strategy. Small seeds are produced in greater numbers, and able to travel further distances more easily (Shipley & Dion 1992; Guo et al. 2000; Fenner & Thompson 2004; Moles & Westoby 2004; Thomson et al. 2011). Species with light seeds thus have more potential dispersal events, despite a lower chance of success for any given propagule. This can be conceptualized as a trade-off between ‘quantity’ versus

‘quality’, where overall dispersal effectiveness is the product of the number of dispersal events 30 by their probability of producing a new adult (Schupp 1993; Schupp et al. 2010). Using this framework, wind dispersed species have more dispersal events (although at a lower probability of surviving relative to animal dispersal), which by doing so wind dispersed species increase their likelihood of rare long distance-dispersal events. The greater average dispersal distances of lighter seeds (Greene & Johnson 1992; Fenner & Thompson 2004; Thomson et al. 2011) further increases the chance of long distance dispersal events, which could have allowed such species to have respond more rapidly to postglacial warming. Thus, quantity of dispersal may be more a more effective strategy than quality for prioritizing rapid range expansion on glacial timescales, which can be achieved by light, wind dispersed seeds.

In contrast, heavy-seeded and animal-dispersed species have higher range filling. We attribute the differences in range filling between dispersal syndromes to their ecological selectivity and the competitive advantage of heavier seeds (Westoby et al. 1996). From a habitat perspective, animal dispersal is active, while wind dispersal is passive. Wind acts independently of ecological preference and may cast large amounts of seeds into unsuitable areas. By contrast, animal dispersers often share similar ecological preferences with the seeds they consume and are more likely to deposit seeds in suitable conditions to grow, contributing to better local dispersal and higher range filling. Further, animal dispersal facilitates larger seeds to travel further distances from their parent tree, preserving their competitive advantage during germination

(Westoby et al. 1996, Turnbull et al. 2004) but reducing intraspecific competition. However, large seeds have increased investment costs which restricts their ability to grow in energy-poor areas (Morin & Chuine 2006), likely explaining the smaller average range sizes in large seeded species (Figure 8). Another possible reason why large-seeded animal dispersed species have smaller range sizes is that megaherbivore dispersers have been extinct from the North American

31 continent for over 10,000 years (Janzen and Martin 1982, Gill 2014, Chapter IV). If this analysis had been conducted during the last interglacial, when large the guild of large animals in North

America was still intact, the differences in range size between animal and wind dispersed trees might not have been as great.

Angiosperms and species with high SLA values or less dense wood have higher range filling values. In other words, range filling is higher amongst species which strategize for quicker returns and lower initial investments associated with faster growth rates. This is analogous to r/K life history strategies, where wind dispersed species produce more offspring with less investment in their individual success. Height was the least predictive of the functional traits, only having a slightly positive trend with range filling. Height is notable for both determining the amount of light a plant gets (Givnish 1995; Westoby 1998; Falster & Westoby 2003), important in successional dynamics, and influencing dispersal distances (Thomson et al. 2011). While tall heights should generally be more advantageous, extremely tall heights are resource intensive and will take longer to attain maturity without high growth rates, slowing their regeneration.

Despite our finding that species’ traits influenced their distributions, the explanatory power of these variables did not vastly exceed that of climate or geography (effects due to dispersal barriers or postglacial accessibility). Indeed, of the three factors, traits explained the smallest amount of unique variation. Among dispersal traits, although we find consistent signals of dispersal limitation at broad scales, these results are often poorly predictable from known autecological differences. Prior attempts to attribute range sizes or biogeographic proxies to range shift abilities have been generally unsuccessful (Lester et al. 2007), and the signal of species’ functional traits is weak at large biogeographic scales (Munguía et al. 2008; Angert et

32 al. 2011; Nogués-Bravo et al. 2014; Harnik et al. 2018). Given that dispersal traits can play a role shaping ranges, why do they not have more of an influence?

Although functional traits are presumed to relate to species’ performance, this may not always be true. For instance, dispersal traits are often categorical and may be overly simplistic

(Lester et al. 2007). The link between such categorical variables (e.g., dispersal syndromes) and the actual modes of long distance dispersal (LDD) is also not direct (Higgins et al. 2003). LDD events, commonly defined as dispersal >100 m, are thought to be relatively rare despite being crucial for driving the pace of continental-scale range shifts. Research on LDD suggests it often occurs from chance events and via ”non-standard” modes of dispersal (e.g. extreme wind storms); thus, their very nature would preclude that a given species would evolve adaptions to

LDD per se (Higgins et al. 2003). Further, functional traits are not independent from climate and environmental conditions. As noted by Šímová et al. (2018) and supported by our variance partitioning analysis, there are significant interactions between traits, climate, and geography.

Investigating the nature of these interactions can help us better understand the controls on range sizes and range filling. Lastly, the probability of dispersing successful colonizers in a new region is influenced by local abundance and population sizes (Hanski 1982, 1991), which may have been sensitive to the stochastic climate change following the Last Glacial Maximum.

The interaction between dispersal, demographic, and environmental stochasticity would make predicting early arrival species to deglaciated areas difficult, and positive feedbacks would allow pioneers to attain greater range sizes (and filling) than expected based on their ecological characteristics alone. Indeed, after including niche properties, functional traits, and geography, we still have nearly half of the variation in range filling unexplained (Figure 7). Thus, despite attempts to increase the predictability of range shifts by studying species’ ecology, there is still a

33 large amount of stochasticity involved, and range shifts may be more akin to a ‘neutral’ process than appreciated (Hubbell 2001).

Low range filling is often interpreted as large-scale post-glacial dispersal limitations.

However, if species are tracking climate not by an advancing front but via satellite populations established from long-distance dispersal events, then species may actually be readily tracking climate despite apparent disequilibria. High range filling can result from small potential range sizes indicative of climatic specialization (Chapter I). While a high degree of climatic equilibrium suggests such species are resilient to climate change, their association with rare climates could prove detrimental if such climates disappear. Counterintuitively, we found contrasting trends between seed mass and wood density and range size and range filling, where both are negatively correlated with size but positively correlated with filling. This finding indicates species that are most effective at tracking climate change via LDD may have low range filling when measured as the residuals from the range size/range filling relationship.

Conclusion We show range filling can be used to detect the impacts of functional traits on species distributions and is an effective proxy for measuring colonization lags associated with dispersal ability. However, considerable variation remains unexplained across species, indicating ecological theories still have progress to make to achieve a predictive framework for species’ future range responses to climate change. Further study of range filling will help us understand the interplay of factors which determine species’ biogeographic dynamics.

CHAPTER III: NORTH AMERICAN TREES FAIL TO FILL CLIMATIC RANGES DURING POST-GLACIAL WARMING

Chapter Abstract The degree to which species’ ranges maintained climatic equilibrium in response to postglacial warming is a natural test case for how species may respond in the future. To test species’ climatic equilibrium during climate change, we calculated the occupied proportion of potential climatic ranges, or “range filling,” for 24 taxa at one-thousand-year intervals during the last

21,000 years following deglaciation. Range filling has decreased overall since deglaciation and declined during periods of climate-driven high biotic velocities. Range filling is lowest during the most modern period, which we propose is due to recent climatic changes and human activity.

Overall, range filling is an effective proxy for estimating dispersal limitations, and low contemporary range filling should indicate that trees may be at greater risk due to climate change than may be inferred from pollen-derived migration estimates.

Introduction Understanding the ability for species to maintain equilibrium with their climatic envelopes through time is critical to predicting climate-driven range shifts, particularly for long- lived, sessile organisms like trees. Ecological theory often relies on the assumption that species’ ranges are in equilibrium with climate (Copenhaver-Parry et al. 2017). While the fossil pollen record validates this assumption at millennial timescales (Williams et al. 2004), growing evidence of lags (Ordonez 2013) and disequilibria (Svenning & Sandel 2013) suggests that postglacial climates and dispersal lags are contributing to species ranges (Svenning & Skov 2004,

2007; Normand et al. 2011; Nogués-Bravo et al. 2014), patterns of endemism (Sandel et al.

2011) and functional diversity (Ordonez & Svenning 2015, 2016, 2017; Blonder et al. 2018).

The recent paleoecological record provides a natural experiment which can reveals when and how these disequilibria occur.

It can be challenging to detect whether species are in climatic equilibrium, particularly for long-lived species where range shifts may be slow, and ecological processes take time relative to the timescales of our observation. One approach is to calculate the ratio of the realized and potential ranges of a species as modeled by environmental niche models, called ‘range filling’ (Svenning & Skov 2004). Range filling studies have shown most species do not fill the majority of their potential climatic ranges (Svenning & Skov, 2004, 2007; Munguía et al., 2008;

Normand et al., 2011; Nogués-Bravo et al., 2014, Chapter I). Further, there are significant interactions between range filling, range size and geography (Chapter I). Small ranged species at low latitudes have the lowest filling, while large-ranged species at high-latitude species attain the highest filling (Svenning & Skov, 2004, Chapter I). Post-glacial dispersal limitations have typically been evoked to explain these patterns and the observed levels of present disequilibrium

(Svenning & Skov 2004, 2007; Munguía et al. 2008; Nogués-Bravo et al. 2014), although species’ niche breadths and biotic interactions may also play a role, and range filling also shows significant non-ecological influences (Chapter I). Analyzing range filling responses in the recent fossil record can help disentangle these drivers, as the temporal dynamics of range filling allow us to test hypotheses in ways that are not possible in a single snapshot. For instance, if climatic range filling is an effective proxy for dispersal limitations, then range filling should decrease through time following deglaciation.

Range filling should also decline when climate change drives potential ranges to move more quickly than taxa can keep pace. Range shift rates have been well studied in the pollen-record

(Davis 1981; Webb 1986), which have found migration rates as high as 1-10 km per decade.

These can be compared to rates of climate change using the metric ‘climate velocity’, which is calculated by converting rates of temperature change in degrees to the distance a species will have to physical move to stay in the same climatic conditions (Loarie et al. 2009). Providing species’ dispersal and demographic rates can keep pace, high climate velocities would lead to higher biotic velocities (Ordonez & Williams 2013). However, if species’ lag relative to the rate of climate change, then this will be apparent as disparities in the realized biotic velocities.

Considering past and future climate velocities both appear capable of exceeding the observed migration rates of 10 km per decade (Loarie et al. 2009; Sandel et al. 2011), the latter scenario may be more realistic

To test the drivers of range filling through time, we analyzed the potential range filling of

North American trees and shrubs since the last glacial maximum using pollen records. Eastern

North America has experienced the highest climate velocities since the Last Glacial Maximum

(LGM) in the world (Sandel et al. 2011), and it also has a dense network of well-dated pollen records necessary to evaluate prehistoric range dynamics at millennial timescales. Additionally, periods of rapid climate change during deglaciation provide useful analogs for future warming.

During the onset of the Bølling-Allerød, a warm and wet period from 14,700 to 12,700 BP, and the termination of the following cold period called the Younger Dryas (12,900 and 11,700 BP),

Northern Hemisphere temperatures rose 2 to 5 °C within 200 years (Veloz et al. 2012), which is analogous to the rate and magnitude of warming expected in the coming century (Willis &

MacDonald 2011). The Younger Dryas is further notable for the pace of its onset and termination, which occurred within decades and caused vegetation responses recorded by pollen

(Dansgaard et al. 1989; Shuman et al. 2002; Williams et al. 2004, 2011). If range filling

37 decreased during these abrupt events (with their corresponding high climate velocities), this would suggest dispersal limitations limit range filling. Further, range filling should be greatest at the LGM, since at that point ranges had been experiencing approximately 100,000 years of relatively low climate change and area contraction due to the growing ice sheet. If, however, range filling stayed relatively constant through periods of abrupt or dramatic changes, this would suggest the range filling metric is probably more influenced by biotic interactions and non- ecological factors than dispersal.

Methods Climate data were generated from 21,000 BP to the modern period using downscaled and de-biased versions of the Community Climate System Model Version 3 (CCSM3; Liu et al.

2009; He et al. 2013) made available by Lorenz et al. (2016). We used three climate variables to model species ranges: winter minimum temperature, growing degree days, and water balance.

We chose these variables as they are strongly associated with plant range limits. Freezing temperatures often constitute a hard limit for plant survival (Sakai & Weiser 1973). Growing degree days are the annual net heat accumulation above a specified threshold (here 5 °C), which provides growing season intensity index, an important proxy for phenological constraints on plant growth (Prentice et al. 1992; Sykes et al. 1996; Chuine & Beaubien 2001). We calculated water balance by dividing the monthly sums of precipitation by potential evapotranspiration

(Thornthwaite 1948; Thornthwaite & Mather 1955). Water balance more accurately represents the moisture available to a plant than raw precipitation (Stephenson 1990), although it does not incorporate the influence of soil. Lastly, we calculated climate velocity for mean annual temperature (Loarie et al. 2009) at each time interval.

Figure 9. Pollen Workflow Diagram. Steps needed to calculate range filling from pollen records.

We reconstructed the fossil ranges of Eastern North American tree taxa using pollen sediment records (Figure 9a) downloaded from the Neotoma Paleoecology Database (Williams et al., 2018; www.neotomadb.org), using the neotoma R package (Goring et al. 2015). We used a subset of high quality sites identified by Blois et al. (2011) which meet strict criteria for chronologies to reduce temporal uncertainty (all dates reported are calibrated calendar years before present). While this dataset can be used to detect responses in species at a resolution of up to 500 years (Blois et al. 2011), we used 1000-year time bins centered on millennia (i.e. 10,000

BP +/-500 years; except for the most recent, which is CE 1450 to 2011, hereafter the ‘modern period’) to allow for more accurate range construction and conserve range shift rate responses.

This resulted in a maximum of 440 sites (1000 BP) and a minimum of 15 sites (21,000 BP). We constrained our analysis to 24° N to 63° N and 50° W to 120° W, and accounted for changes in land area by subtracting regions occupied by the Laurentide ice sheet, large paleo lakes, and

39 changes in sea level. We further constrained our study extent for each time bin by taking a convex hull with a 1000 km buffer around all sites within each time bin, which prevented extrapolation into areas of the continent without pollen records.

We used the reduced list reported by Williams and Shuman (2008) for separating pollen taxa, and raw counts were converted to relative abundance by dividing by the sum of arboreal pollen for each sample. All taxa with more than 100 pollen grains total across all sites and time bins were analyzed at the generic level, except for Cupressaceae (Thuja, Juniperus and

Taxodium) and Ostrya-Carpinus, which produce indistinguishable pollen grains, and are typically lumped. While palynologists can resolve some genera to species, because this was not done consistently by all investigators in our dataset we lacked sufficient coverage to split taxa further. This resulted in 24 pollen taxa: Abies, Acer, Alnus, Betula, Carya, Castanea, Corylus,

Cupressaceae, Fagus, Fraxinus, Juglans, Larix, Liquidambar, Nyssa, Ostrya-Carpinus, Picea,

Pinus, Plantanus, Populus, Quercus, Salix, Tilia, Tsuga, and Ulmus.

Fossil realized ranges were calculated for each time interval from pollen sites using two methods: convex hulls and abundance-based thresholds. Convex hulls are the smallest polygon which can enclose all the sites where a taxon was present. Abundance-based ranges were obtained by spatially interpolating between sites using inverse distance weighting (Figure 9b).

The interpolated surfaces were then converted into presence/absences using taxon-specific thresholds (Figure 9c). Each approach has its own advantages and biases. Convex hulls ensure that all sites where a taxon is present will be included but may overestimate a species range by omitting disjunctions or holes within the range (e.g., those caused by a mountain range that bisects a distribution), which could in turn overestimate climatic niches. Alternately, the thresholding method allows for disjunctions and more complex range shapes but is influenced by

40 the taxon’s relative abundance through time. We calculated pollen thresholds as a percentage of a taxon’s maximum abundance following Nieto-Lugilde et al. (2015), who found thresholds of

1% and 5% of maximum pollen abundance most closely matched modern forest inventory abundances. We used a 1% threshold for all taxa but Pinus and Quercus, which are high pollen producers and were given a 5% threshold. Threshold ranges tend to be smaller relative to convex hulls as they allow for ranges with holes and disjunctions. Moreover, as pollen abundances fluctuate through time, abundances for some taxa occasionally fell below thresholds and prevented constructing a range. In those cases, we relied only upon the convex hulls to estimate that taxon’s fossil range.

We compared our realized range against the USGS tree atlas (U.S. Dept. of the Interior

2006) for the modern ranges (ca. 1950) and Williams et al. (2001) for LGM ranges, and found close agreement. See Figure 10 for example fossil realized ranges. Fossil potential ranges (Figure

9d) were calculated using three species distribution modeling (SDM) algorithms; bioclimatic envelopes (BIOCLIM; Busby 1991; Booth et al. 2014), Maximum Entropy (MaxEnt; Phillips et al. 2004, 2006), and Generalized Additive Models (GAMs; Hastie & Tibshirani 1990; Guisan et al. 2002). Fossil potential ranges were modeled separately for each algorithm, for a total of 6 models per taxon. Each model iteration was treated separately, and then results across the iterations were averaged to obtain a single value for each taxon in each time bin. We calculated range filling as the sum of SDM values within each fossil realized range, divided by the total predicted values across the study area. Because range filling is strongly positively correlated with range size (Chapter I), we evaluated range filling as residuals from a model of range size against range filling, then standardized the residuals using Z-scores to prevent a single taxon from

Figure 10. Example Fossil Ranges. A subset of realized (top rows) and potential ranges (bottom rows) for three pollen taxa. Realized ranges are in green, while yellow colors indicate high potential range suitability.

42 dominating the overall signal (see Figure 11). We obtain potential fossil range areas by taking a volume containing 95% of the values in the SDM outputs. We calculated biotic velocities following Ordonez & Williams (2013) as the distance that fossil range centroids (both realized and potential) moved between time periods. Lastly, we measured differences in latitude between potential and realized fossil range centroids as a measure of range separation. We used the R packages ‘dismo’ (Hijmans et al. 2016) ‘mgcv’ (Wood & Wood 2013) for the SDMs.

Results Range filling showed individualistic responses across taxa (Figures 11 and 12). When averaged across taxa, range areas increased from the LGM to the modern period (Figure 13), and range filling decreased (Figure 14). The strongest correlate of range filling was range separation

(potential range centroid latitude minus realized range centroid latitude; see Figure 14), which peaked at 12,000 BP, followed by the modern period. Range filling was highest at 16,000 BP, and lowest at the modern period, following a 5000-year downward trend. The largest range filling declines occurred at 15,000 and 13,000 BP.

Biotic velocities (realized and potential) were highest at 15,000 BP (Figure 13), coinciding with the onset of the Bølling-Allerød. Potential velocities were similarly high at the onset of the Younger Dryas at around 13,000 BP (Figure 13), although realized biotic velocities showed only a modest increase, causing potential biotic velocities to exceed the realized velocities. This also occurs at 10,000, 5,000, and 3,000 BP, though not to the same degree.

Following each of these periods range filling also decreased. We found potential biotic velocities were significantly correlated with climatic velocity following the LGM. Range filling tended to decrease when potential biotic velocities exceed realized velocities (Figure 13).

Figure 11. Fossil Range Filling Values. Top panel is range filling as raw percentage, which is highly correlated with realized range size (Chapter I). We then converted range filling to residuals from the range-size range filling relationship (middle). Finally, we standardized these values using Z-scores for further analysis to prevent taxa with extreme values from dominating. 44

Figure 12.1 Fossil Range Results by Taxa. All panels are as follows (top to bottom); Biotic Velocity (realized – solid, potential – dashed), Range Filling and Range Separation (potential latitude – realized latitude), Range Size (realized – dark, potential – light).

Figure 12.2 Fossil Range Results by Taxa (Continued)

Figure 13. Mean Fossil Range Results. Range metrics averaged of all taxa are shown along with the Greenland Ice Sheet Project 2 (GISP2) estimated temperatures and climate velocity from CCSM3. We used standardized residuals shown at the bottom of Figure 11 for range filling.

Figure 14. Paleo Correlation Matrix. Panels show correlations between variables across all taxa. Colors reflect direction of trend (blue positive; red negative) and values correspond to Pearson’s correlation coefficient.

Discussion We found potential climatic range filling has decreased since the last glacial maximum and dropped when climate is driving rapid range shifts. The strongest predictor of range filling was the separation in latitude between potential and realized range centroids (Figure 14).

Together, these support the view that range filling is an effective proxy for dispersal limitations

(Svenning & Skov 2004; Munguía et al. 2008; Normand et al. 2011; Nogués-Bravo et al. 2014).

Overall, range filling through time varied across taxa owing to their individualistic responses to postglacial warming. The degree to which members of ecological communities respond as individualists (“Gleasonian” sensu Gleason, 1926) or as groups (“Clementsian” sensu

Clements, 1936) has been a central question of ecology theory (Franz et al. 1988), and vegetation dynamics observed in pollen records have been used to validate Gleasonian views of community assembly (Jackson & Overpeck 2000; Jackson 2006). Fossil mammal ranges also show individualistic changes in position, size, and vector shifts over the last 40,000 years (Lyons

2003). Explaining the variation amongst range shift rates among species is critical to predicting future range responses more accurately. As functional traits vary significantly amongst taxa

(particularly amongst dispersal traits in trees) and are known to have real-world performance consequences (Westoby & Wright 2006), they are thus the logical source of species’ differences.

However, previous work has suggested that functional traits poorly explain variation in range shift rates across taxa (Angert et al. 2011), including in the pollen record (Harnik et al. 2018).

Despite individual responses to climate through time, averaging our model results across taxa revealed several overall trends. Range areas slowly increased over time as more land became available due to the ice sheet retreating. Range filling, when calculated as a percentage

(Figure 10), showed the same positive correlation with range size found previously in modern

49 ranges (Chapter I), where small ranges have low range filling and large ranges have high range filling. The fact that this trend holds across millennia, and is not just a modern artifact, is further evidence that the range size-range filling relationship is a general trend which can likely be found in other species groups or in other regions, The range size-range filling trend was found even amongst spatially randomized ranges (Chapter I), indicating that methodological biases help drive this trend, the most significant of which is likely geometry. As a range size increases, it takes up a larger percentage of the available land area, and thus is more likely to inhabit available areas of potential range. To get a range filling metric which was independent of the influence of range size, we thus took the residuals from the overall range size-range filling relationship including all taxa and time periods.

We also found range sizes to generally increase through time. This differs from mammal ranges, which show individualistic increases and decrease through time (Lyons 2003). However, as we studied taxa largely at the generic level, our range metrics are not entirely comparable to these species-level predictions. This could explain why we found comparatively larger ranges with higher range filling proportions compared with studies conducted on modern range data

(e.g. Svenning & Skov, 2004; Chapter I) . Because range size is not phylogenetically correlated

(Chapter II), we caution against over-interpreting trends in pollen ranges by extending findings to all the taxa within a genus or family. There are likely species-level differences even within genera that our approach cannot detect. However, pollen remains our best window into viewing the range dynamics of the past, and what we lose in terms of taxonomic specificity we gain in spatial and temporal coverage. And, at the millennial timescales of our analysis, much of the signal of interest is likely at the genus level.

Mean range separation (difference between potential and realized range centroid latitudes) peaked at 12,000 BP. This follows a peak in climate velocity and high differences between potential and realized biotic velocities (Figure 13), suggesting that realized ranges could not keep pace of their potential ranges during these periods of high climate velocity. Regions with histories of high climate velocity have lower numbers of small-ranged endemic, and poorly dispersing species (Jansson 2003; Sandel et al. 2011; Morueta-Holme et al. 2013). Climate velocity can act as an environmental filter over the course of many glacial cycles, whereby narrowly distributed-ranged, poorly dispersing species are driven to extinction during rapid periods of climate change, while species with strong dispersal abilities can colonize wide areas of previously glaciated habitat and attain large range sizes. Thus, North America may be missing the species which were most vulnerable to Quaternary climate changes, and we should expect formerly glaciated regions to be inhabited by taxa with higher range filling overall. Alternatively, regions with relatively stable climates tend to harbor more diverse assemblages of taxa with a wider range of phylogenetic ages (Feng et al. 2017), as these regions act as refugia, allowing for increased diversification over of time.

While mean range filling shows an overall decline throughout the past 21,000 years, we found that range filling rebounded within 1,000 years at most for both the Bølling-Allerød and

Younger Dryas. Thus, rather than present climatic disequilibrium being the result of Quaternary climate changes, our results suggest that the fingerprints of past climate changes have been largely erased for most Eastern North American taxa. In fact, all our pollen taxa show at least one period of increasing range filling since the end of the Pleistocene. Although for some taxa

(Corylus, Fraxinus, Ostrya-Carpinus, and Populus) range filling never fully recovered to

51 positive Z-scores following decreases to Late-Pleistocene climatic changes (Figure 10), so we cannot rule out their role as a driver of low modern range filling.

Surprisingly, much of the present climatic disequilibrium appears to have taken place just in the last 5000 years, a time when climate changes were less significant than the high- magnitude, abrupt events at the end of the Pleistocene. In fact, range filling during the modern period (1450-2011) is lower than any time during the last 21,000 years, following several millennia of declines across almost all taxa. Climatic events like the Little Ice Age and Medieval

Warm Period are potential drivers of the low modern range filling, and several factors indicate a climatic cause of late Holocene decreases in range filling. First, potential range velocities started to increase during the last time bin, coinciding with an increase in climatic velocities (Figure 13), but realized range velocities did not keep pace with this increase. This means that climatic changes were driving range shifts during this period which species were not fully tracking.

Furthermore, there is slight decrease in realized range sizes despite an increase in potential range sizes (Figure 13).

Another possible cause of the decrease in range filling during the last 5000 years are human activities. Overall, biotic velocities (both realized and potential) were lower relative to the previous 15,000 years, and yet range filling declined during this interval (Figure 13). This coincides with increases in indigenous human populations in the northeast (Munoz et al. 2010).

Human activities like hunting, herding, fire and agricultural practices have been shown to have significant legacies amongst North American woody plants (Vale 2002, 2013; Normand et al.

2017). Pre-historic hunting also contributed to the extinction of 34 genera of North American megafauna around 14,000 BP (Gill et al. 2009a), which the loss of likely influenced the formation of no-analog in vegetation assemblages (Gill et al. 2012). As megafauna are known

52 important long-distance dispersers, the loss of these animals may have caused dispersal limitations in some animal dispersed trees (Janzen & Martin 1982; Gill 2014). Indigenous people also planted trees, particularly those with desirable fruit and nuts (Keener & Kuhns 1997; Loeb

1998), although others argue that there is no evidence for widespread dispersal pre-European arrival beyond a few select species which were moved regionally (MacDougall 2003). European colonization was followed by intensified land-clearing and the introduction of foreign pests and diseases which may also have contributed to range filling decreases in the last 500 years.

Regardless of the cause, given the degree of contemporary climatic disequilibrium species distribution modeling attempts ought to carefully consider the assumption their focal taxa are in equilibrium with climate. Without accounting for climatic disequilibrium we may under- predict range size reductions due to climate change, especially since ranges are already in disequilibrium with contemporary climate. While more vagile taxa, like wide home-ranging mammals and spore-bearing plants, will be somewhat ameliorated from these impacts (Normand et al. 2011), low range filling associated with dispersal lags has been shown in animals (rodents:

Munguía et al., 2008; and amphibians: Munguía et al., 2012) meaning these results are likely not limited only to trees.

Conclusion Over the last 21,000 years, eastern North American tree ranges have exhibited an individualistic response to climate change, but overall have trended towards climatic disequilibrium in recent millennia, despite their capacity to keep pace with several abrupt events following deglaciation. This is consistent with dispersal limitation hypotheses. However, much of the contemporary climate disequilibrium has accumulated since the mid-Holocene, despite

53 most taxa showing an initial recovery from abrupt events during deglaciation (15,000 BP to

12,000 BP). Together, these results paint a conflicting message; while climate change can outpace species during rapid events, species show remarkable abilities to rebound after these events. However, considering modern ranges appear in disequilibrium relative to their late- glacial counterparts means that responses to future climate change could behave differently. The fact that range filling is as low today as it was during the rapid climatic events following deglaciation offers a sobering perspective on the capacity of trees to track their climates into the

21st century and beyond. As many attempts to model species distributions assume contemporary ranges are in equilibrium with climate, we may be overestimating future range areas and poorly predicting their locations. Thus these disequilibrial responses suggest future climate tracking might be considerably limited.

CHAPTER IV: NO EVIDENCE FOR EXTINCT MEGAFAUNA DISPERSAL LIMITATIONS AMONG EASTERN UNITED STATES’ TREES; DID NATIVE AMERICANS DISPERSE THE FAVORED FRUITS OF PLEISTOCENE MEGAFAUNA?

Chapter Abstract The fruits of several species of North American trees have traits associated with adaptations to dispersal by now-extinct megaherbivores. Trees with megafaunal dispersal syndrome are thus predicted to have had more difficulty tracking postglacial warming during the

Holocene, resulting in narrower ranges and lower abundances than trees without megafauna dispersal adaptations. Decades after Janzen and Martin first proposed these evolutionary anachronisms, the impact of these vanished interactions remains untested. Here, we analyzed

FIA and USGS range data for differences in range filling between megafauna and non- megafauna species. Contrary to our predictions, we found no significant differences between megafauna and non-megafauna species across several biogeographic indices. We propose that humans and smaller animal dispersers may have compensated for the loss of ice age megafauna.

As future dispersal capacity is uncertain in a warming and fragmented world, managed relocation may continue to be necessary to ensure the survival of these and other at-risk trees.

Introduction Janzen and Martin (1982) introduced the concept of “megafaunal dispersal syndrome” to explain an ecological puzzle in the Neotropics: trees like jicaro (Crescentia alata) and

Guanacaste (Enterolobium cyclocarpum) produced copious amounts of large, energy-rich fruits which rotted largely untouched beneath their parent trees. Upon observing that these fruits were browsed by non-native livestock, Janzen and Martin proposed that domestic animals were

55 partially restoring the ecological role of seed dispersal by extinct Pleistocene megaherbivores

(1982). While Janzen and Martin’s theory is often cited as an example of coevolution (Janzen &

Martin 1982; Tiffney 2004), megafaunal dispersal syndrome has largely remained a ‘just-so’ story in ecology, with few explicit tests (Gill 2014). Most of the research on the dispersal syndrome has focused on the Neotropics (Guimarães et al. 2008; Jansen et al. 2012; Doughty et al. 2016b; Galetti et al. 2017; Onstein et al. 2018), though Janzen and Martin (1982) proposed five temperate North American trees are also adapted for dispersal by now-extinct megafauna

(animals >44 kg): pawpaw (Asimina triloba), persimmon (Diospyros virginiana), honey locust

(Gleditisia triacanthos), Kentucky coffeetree (Gymnocladus dioica), and Osage orange (Maclura pomifera). Janzen and Martin predicted that “when there was megafauna available, such genera may have been denser and had much wider ranges (1982).”

Testing Janzen and Martin’s hypotheses is difficult (Howe 1985). While fossil occurrences can be a valuable tool in assessing dispersal limitations (Webb 1986; Davis 1989), this information is uncommon for many megafauna-adapted plants. Much of their diversity is in the tropics, where paleobotanical records are limited. Among temperate species, there are few records in the Neotoma paleoecological database (Williams et al., 2018; www.neotomadb.org) because these trees produce pollen that either is undistinguishable from their congenerics or animal-dispersed and in low numbers. Thus, reconstructing their ranges from sediment cores is not possible. Further, sediment records from the last interglacial period are rare in North

America, which precludes comparisons of ranges during warm periods with and without megafaunal dispersers.

Despite this, there are compelling signs of dispersal limitation among Janzen and

Martin’s temperate anachronistic species (1982). Fossil evidence suggests the ranges of Osage

56 orange and pawpaw were once much larger; both were found near present-day Toronto during the last interglacial (Coleman 1895; Penhallow 1900) and Osage orange seeds appear in mastodon (Mammut americanum) dung in Florida from the last glacial maximum (Newsom &

Mihlbachler 2006), all of which are far beyond their current ranges. Population genetics of Osage orange and honey locust show each have significant local differentiation, which the authors argued resulted from limited long distance dispersal (Schnabel et al. 1991, 1998). Indeed, Osage orange has essentially no known dispersers in its narrow native range (Collingwood 1939;

Murphy et al. 2018). Similarly the only known method of long-distance dispersal (>100m) of

Kentucky coffeetree pods is water (Zaya & Howe 2009), and rivers would have likely been an ineffective strategy for the northward migration required after deglaciation. Kentucky coffeetree’s limited regeneration and low local abundances are attributed to a lack of animal dispersers (Zaya & Howe 2009; Choudhury et al. 2014; Carstens & Schmitz 2017).

Janzen and Martin’s anachronistic fruits (1982) have modern analogues around the world, some of which may be dispersed by extant megafauna. Osage orange’s closest relative (Maclura brasiliensis) is found in the Brazilian Cerrado, an area with many other megafauna-adapted fruits

(Donatti et al. 2007; Guimarães et al. 2008) and where tapirs (Tapirus terrestris) play a role in dispersal (Galetti et al. 2001; Tobler et al. 2010; O’Farrill et al. 2013). Kentucky coffeetree has a congeneric in India (Gymnocladus assamicus) which is critically endangered due to a lack of regeneration and dispersal (Choudhury et al. 2007, 2014; Menon et al. 2010), reductions in native ruminant dispersers may have contributed to its decline (Choudhury et al. 2009).

Persimmon (Diospyros) species are found globally throughout the tropics where they are consumed by a variety of frugivores (Lieberman et al. 1979; Chapman & Chapman 1994).

Locusts of the genus Gleditisia, although not found in the range of any extant megaherbivores,

57 are similar to other legumes readily dispersed by African elephants (Loxodonta africana), like

Mesquites (Prosopis) and Acacia (Razanamandranto et al. 2004; Spanbauer & Adler 2015;

Bunney et al. 2017).

While globally widespread until the late Quaternary, presently only Africa and Asia support sizeable populations of megaherbivores. Throughout the last 65 million years, North

America supported a megafauna assemblage as diverse as modern Africa (Koch & Barnosky

2006), which contained numerous animals proposed to disperse the now-anachronistic fruits

(Janzen & Martin 1982; Barlow 2000, 2002). Among these species, the most notable frugivore dispersers were proboscideans (elephants and their relatives, especially fruit-eating mastodons), xenarthans (ground sloths, which have no extant species), and equids (horses and their relatives)

(Barlow 2000; Boone et al. 2015; Gardner et al. 2017). A phylogeny of Maclura (Osage orange) revealed it evolved around the same time (40 million years ago) and ecological conditions as the diversification of proboscideans and xenarthans (Gardner et al. 2017), which supports the interpretation they co-evolved.

By 10,000 years ago, 34 genera and 2 orders of mammals had gone extinct in North

America alone, including the largest terrestrial mammals on Earth (Koch & Barnosky 2006).

Their extinction is associated with continental-scale changes in vegetation communities and fire regimes (Gill et al. 2009b, 2012; Gill 2014), and long distance transport of nutrients (Doughty et al. 2013, 2016a) and seeds (Campos-Arceiz & Blake 2011; Doughty et al. 2016b; Malhi et al.

2016; Bunney et al. 2017; Corlett 2017). The evolution of large, heavy, energy-rich seeds and fleshy fruits are thought to allow plants to circumvent the resource trade-off between seed size and dispersal distance by targeting megaherbivore dispersers (Guimarães 2008). In comparison to smaller animals, larger dispersers can consume larger seeds while being less likely to damage

58 them and due to increased gut retention times and mobility can travel longer distances before depositing seeds (Sridhara et al. 2016).

Modern experiments involving dispersal and regeneration of the megafauna adapted trees have yielded mixed results. Scarification, a process which mimics passing through a herbivore’s digestive tract, resulted in significantly higher (95.8% vs 5.6%) germination success for honey locust (Warren 2016). In contrast, fruits of Osage orange, pawpaw, and persimmon, have been fed to Asian elephants (Elephas maximus) and domesticated horses (Equus ferus caballus) to determine if the viability of their seeds was enhanced by digestion, and only persimmon showed a benefit from elephant consumption (Boone et al. 2015). Neither elephants or horses would eat pawpaw, and Osage orange showed no increase germination success from either of the animals’ consumption (Boone et al. 2015). Boone et al. conclude that neither Pleistocene proboscideans

(elephants and their relatives) or equids were likely effective dispersers of Osage orange, pawpaw, or persimmon (2015), leaving their potential coevolved dispersers a mystery.

We examined the evidence for the biogeographic consequences proposed by Janzen and

Martin (1982) on Eastern United States’ megafauna tree species with several tests: restricted distributions, reduced abundances, and decreased range filling of their potential climatic ranges

(Chapter I), all relative to other tree species within the Eastern United States. We also compared ranges derived from datasets that reflect pre- and post-European colonization to partially assess the role of humans have play in dispersal limitation in release.

Methods We compiled species’ geographic ranges from two sources; the Forest Inventory Analysis

(FIA) dataset (U.S. Forest Service 2015; see Figure 1) and the United States Geological Survey

(USGS) Atlas of United States Trees (U.S. Dept. of the Interior 2006), a digitized version of the expert-informed ranges created by E.L. Little (Little 1971). We updated and standardized their nomenclature for each database using The Plant List (2013), and only analyzed species shared by both datasets. The USGS ranges were constrained to the United States, since FIA coverage does not include Mexico or Canada. This should not influence our results, as amongst the megafauna adapted species we analyzed, only pawpaw, Kentucky coffeetree, and honey locust extend slightly into Southern Ontario (representing 0.63%, 0.18%, 0.03% of their ranges, respectively).

We further limited the extent to east of 102° W to focus on eastern species, since range filling is correlated with geography owing to differences in accessibility to post-glacial refugia and dispersal barriers between regions (Chapters I and II). Western species with proposed megafauna-adapted fruits (like Joshua tree, Yucca brevifolia) are largely absent in the FIA database, and among the species we analyzed only Osage orange extended beyond this extent

(accounting for just 0.52% of its range). We then rasterized occurrence data at a resolution of 20 km by 20 km for further analysis. We calculated range overlap as the proportion of a range which was occupied by the other occurrence dataset (for instance FIA overlap for Osage orange was just 9%).

We calculated the following four measures of abundance from the FIA data: frequency, relative dominance, relative basal area, and an importance value index. Frequency is the proportion of FIA plots within each grid cell that species was present (see Nguyen et al. 2014).

We calculated relative dominance by taking the proportion of all stems in each cell which belonged to a species following Iverson and Prasad (1998) and Iverson et al. (2008). Similarly, we measured species’ relative basal area as their proportion of sum diameter at breast height

Figure 15. Megafauna-Adapted Geographic Ranges. Janzen and Martin’s five species from the USGS tree atlas ranges by E.L. Little (marked with crosshatching) and FIA-derived importance values (mean of frequency, dominance, and basal area) are shown by color. within each cell (Iverson & Prasad 1998; Iverson et al. 2008). Lastly, we calculated importance values by taking an average the abundance metrics (Iverson and Prasad 1998; Iverson et al.

2008), also including frequency (Nguyen et al. 2014).

Range filling was calculated by taking the proportion of potential climatic ranges realized by each species (Chapter I, Figure 16). Potential climatic ranges were calculated using

Bioclimatic Envelope (Busby 1991; Booth et al. 2014), Maximum Entropy (Phillips et al. 2004a,

2006), and Generalized Additive Modeling (Guisan, Edwards Jr, and Hastie 2002; Hastie and

Tibshirani 1990) approaches and four climate variables: coldest minimum temperature, growing degree days, precipitation balance, and precipitation timing. These climate variables were chosen for their direct links to plant performance and survival. Cold temperatures are common mortality thresholds for plants (Sakai & Weiser 1973). Growing degree days are an index of growing season intensity which is useful for capturing phenological requirements (Prentice et al. 1992;

Chuine & Beaubien 2001). Precipitation balance, which was calculated as the monthly sum of 61

Figure 16. Megafauna-Adapted Potential Climatic Ranges. The five megafauna adapted species’ potential ranges generated using Maximum Entropy modeling. Yellow colors reflect areas of higher relative suitability. The black crosshatched area is the USGS range. precipitation divided by potential evapotranspiration, is a metric of moisture availability more direct than precipitation alone (Thornthwaite 1948; Thornthwaite & Mather 1955; Stephenson

1990). Lastly, precipitation timing (summer precip. minus winter precip.) is a proxy for the predominant season of precipitation. Proportional range filling values were then converted to residuals from the range size/range filling relationship, which reveals a range-size independent metric of range filling which can be used to detect dispersal limitations (Chapters I, II, and III).

We tested whether the temperate megafauna adapted species named by Janzen and

Martin (1982; Honey locust, Kentucky coffeetree, Osage orange, pawpaw and persimmon) were differentiable from the other 159 species in our dataset across biogeographic indices using two sampled t-tests. To obtain a single value for each species, we took an average of their range-wide values for the FIA metrics (frequency, dominance, basal area, and importance). We then log- transformed these values as we did with range size, as they spanned multiple orders of magnitude. All analyses were performed in R.

Figure 17. Megafauna-Adapted Range Differences. Megafauna adapted species (purple) and non (gray) across range size (a), range filling (b), FIA abundance metrics (c), and range overlap (d). Megafauna species are labeled with the following two letter acronyms; Honey locust (HL), Kentucky coffeetree (KC), Osage orange (OO), Pawpaw (PP), and Persimmon (PS).

Results Megafauna-adapted species did not have smaller ranges than non-megafauna species in either the USGS or FIA datasets (Figure 17a). In fact, only one megafauna adapted species had a below average range size in either range dataset. Osage orange and Kentucky coffeetree exhibited low range filling, although overall we found no significant range filling differences amongst megafauna-adapted species and non-megafauna species (Figure 17b). Megafauna- adapted species also did not show lower FIA-derived abundances (across all metrics, Figure

17c). Finally, we did not detect significantly different overlaps between the USGS and FIA ranges among megafauna adapted species and the rest of our dataset (Figure 17d), although

Osage orange shows very low FIA overlap (9%).

Discussion Janzen and Martin (1982) proposed that megafauna dispersed trees should be dispersal limited compared to non-megafauna species following the end-Pleistocene extinctions. We

63 would expect this to be especially true in North America, which has experienced the highest climate velocities in the world over the last 21,000 years (Sandel et al. 2011); we expected that the five temperate species proposed by Janzen and Martin (1982) would show a strong signal of dispersal limitation. Surprisingly, we found no significant differences between the five megafauna-adapted trees predicted by Janzen and Martin (1982) and the other 159 Eastern

United States’ woody species we analyzed (Figure 3). To explain this, we propose that the degree to which megafauna-adapted trees are truly disperser-absent varies across species, with

Kentucky coffeetree and Osage orange being the most anachronistic, and persimmon and honey locust being the least anachronistic, as supported by the range filling results. We argue the strength of the relationship among megafauna adapted trees and their megaherbivore dispersers is not significant enough to be detected considering these other factors. Further, we suggest that the lack of evidence for biogeographic consequences of the megafauna extinction is due to extant animal dispersers and humans compensating for missing megaherbivores.

Despite the lack of evidence that megafauna-adapted species are dispersal limited, we do not take this as evidence that these species were not dispersed by megafauna, nor that the extinction of megafauna was without ecological consequences. New World palms with megafauna adaptations show increased extinction rates relative to similar palms in the Old World

(Onstein et al. 2018). Other Amazonian megafauna adapted trees show reduced range sizes on par with expectations of no dispersal since the Pleistocene (Doughty et al. 2016a). Given we only had five species to analyze within our extent, our inference and statistical power is limited.

Further, postglacial range shifts were likely disproportionately driven by long distance jumps that can create satellite populations (Clark 1998). Due to the rarity of such jumps and the large spatial scales required to study them, these events are effectively unobservable and remain

64 poorly understood (Pitelka 1997; Clark 1998), although previous studies have demonstrated that species’ morphological seed traits and the mechanisms of long-distance dispersal events are not strongly linked (Higgins et al. 2003). This is because long-distance dispersal events appear to primarily occur through ‘non-standard’ means, i.e., not via the dispersal vector implied by seeds traits (Higgins et al. 2003). Given this, species with megafaunal dispersal syndrome may not have the dispersal limitations expected if their fruits had been successfully dispersed by rare events (e.g. wind storms, floods, or tornados) and via animal or human dispersal, which we discuss below.

After the extinction of megaherbivores, smaller native species like deer, rodents, and birds likely became more important seed dispersers for megafauna-adapted species (Pires et al.

2014), which have since been augmented by the introduction of livestock (Janzen & Martin

1982). Camera-trapping of elephant dispersed fruits has shown that when not consumed by megaherbivores, fruits become consumed by successively smaller dispersers (Sekar & Sukumar

2013). Indeed, scatter-hoarding rodents are the only known dispersers of Joshua tree (Vander

Wall et al. 2006; Waitman et al. 2012; Borchert & DeFalco 2016), despite its large fruits with megafauna-adapted traits (Janzen 1986; Lenz 2001; Cole et al. 2011) that have appeared in the fossilized dung of Shasta ground sloths (Nothrotheriops shastensis) (Harrington 1933;

Laudermilk & Munz 1934) when it had a larger range (Cole et al. 2011). Body size may reduce the effectiveness of a seed disperser; as smaller animals are more likely to consume seeds entirely, or only move them short distances (Hoshizaki et al. 1997, 1999; Vander Wall & Beck

2012). However, the abundance of scatter-hoarding rodents may make them capable backup dispersers, and there is increasing evidence that their seed caches are important germination sources (Vander Wall et al. 2005; Vander Wall & Beck 2012). Scatter-hoarding rodents have

65 even been shown to be capable of long-distance (>100 m) dispersal of large neotropical fruits

(Jansen et al. 2012). While Osage orange fruits are consumed by squirrels (Sciurus sp., Figure

18, Korschgen 1981; Keeler 2000), some contend this is unnatural and occurs primarily in human-modified settings (Murphy et al. 2018). More work is needed to evaluate the trade-offs among dispersal agents (Vander Wall & Beck 2012; Pires et al. 2014) and the natural history of megafauna-adapted trees.

Large seeds and fruits may be as much a general mammal adaptation as they are an adaptation to megafauna (Herrera 1989; Guimarães et al. 2008). Fleshy fruits appear especially attractive to omnivores (Herrera 1989; Chavez-Ramirez & Slack 1993; Willson 1993; Koike et al. 2008). Seeds of pawpaw, the largest fleshy fruits of any North American tree (Hormaza

2014), have been found intact in scat of red foxes (Vulpes vulpes), opossums (Didelphis virginiana), and racoons (Procyon lotor) (Willson 1993). Persimmon, another fleshy fruit, is consumed by a variety of similar mammals, like coyotes (Canis latrans), racoons, and opossums

(Chavez-Ramirez & Slack 1993; Cypher & Cypher 1999; Roehm & Moran 2013; Rebein et al.

2017). Because of the numerous animals that can successfully disperse persimmon, Rebein et al. conclude that their fruits exhibit a ‘generalist dispersal strategy’ and are not anachronistic (2017).

Indeed, persimmon appears the least dispersal limited of the megafauna adapted species in our study, with the largest range size and highest range filling.

The roles that several North American animals play as seed dispersers remain significant and underappreciated. Black bears (Ursus americanus) may be the largest extant North

American frugivore and can long distance disperse several large fruits and seeds (Willson 1993;

Kuhn & Vander Wall 2007; Enders & Vander Wall 2012). White tailed deer (Odocolieus virginanus) are important for dispersing many plants, even for those that deer are not commonly

66 thought of as dispersers (Vellend et al. 2003; Myers et al. 2004). Citizen naturalist Chris Jackson captured photographic evidence deer will eat Osage orange (Fig 4, 2014), although he noted that it was exclusively eaten by adult males, of which individuals would only ever eat a fruit or two at a time (dfwurbanwildlife.com). Blue jays (Cyanocitta cristata) also form seed caches, similar to rodents but less well-studied, which may have been a vector of long-distance dispersal for some

North American trees following deglaciation (Johnson & Webb 1989). Despite North America’s extant frugivores, the geographic association between plant dispersal adaptations and their apparent dispersers appears weak (Dittel et al. 2018), which may be due to the relatively recent extinction of megaherbivores.

Many megafauna adapted species have both traditional (Choudhury et al. 2007; Warren

2016) and contemporary human uses (Smith & Perino 1981), which have led to increases in their present distributions (Burton 1990; Barlow 2000) and may have in the past as well (Peterson

1991). There is evidence that extant populations of honey locust species were established by indigenous people (Lawson 1984; MacDougall 2003), and its present abundance is often more strongly associated with proximity to historic Cherokee settlements than ecological habitat

(Warren 2016), which decreases in areas with cattle grazing. Pawpaw appears to have been used intensely by indigenous Americans, who might have incidentally domesticated the species and saved it from extinction (Peterson 1991), although dispersal by mammals like opossums and racoons may have also been sufficient (Murphy 2001). Pawpaw populations in Southern Ontario and New York are thought to have been transplanted north by the Iroquois (Thwaites 1959;

Keener & Kuhns 1997; MacDougall 2003; Wykoff 2009).

Osage orange, named after the Osage people who occupied its range, was traded widely by Native Americans as it was prized for bow-making (Peacock et al. 2003; Austin 2004). When

Figure 18. Compensatory Dispersal Vectors of Megafauna Adapted Trees. While Osage orange (Maclura pomifera) is likely one of the most anachronistic megafauna fruits of North America, it still has several underappreciated dispersal vectors. Scatter-hoarding rodents like the gray squirrel (Sciurus carolinensis) are known to interact with the fruits, and likely bury some seeds intact (top left; photo by Chris Jackson). White Tailed Deer (Odocileus virginanus) have been proposed dispersers of Osage orange, but evidence was lacking until Trail cameras recently documented deer eating fruits. (top right; credit to Chris Jackson). Osage orange was a prized source of bow material widely used by the Osage people (bottom left; photo by Smithsonian), who may have influenced its distribution. Osage orange was also a popular hedge species (bottom right; photo by USDOT), likely contributing to populations beyond its native range.

Europeans arrived, human use of megafauna-adapted plants further intensified. Indeed, French explorers named Osage orange ‘Bois d’arc‘ (Bow-wood), and its agricultural use as fence posts and hedgerows (Figure 18) led to the expansion of Osage oranges’ range starting in the 1850s

(Winberry 1979; Smith & Perino 1981; Burton 1990; Ferro 2014). Today, most of Janzen and

Martin’s temperate anachronistic species (1982) are common horticultural plants, with honey locust being planted as a ‘city tree’ across the United States (Barlow 2000)

Conclusion We found surprisingly little evidence supporting the biogeographic limitations predicted by Janzen and Martin (1982) for Eastern United States’ trees with megafaunal dispersal syndrome. We propose that smaller animals and humans have provided sufficient dispersal following the megaherbivore extinctions to allow these plants to persist (Howe 1985), though there may be other consequences of the end-Pleistocene extinctions found in their genetics or among other anachronistic species in other regions. The same fruit and seed characteristics which were attractive to megafauna are also appealing to people, which has likely influenced the modern distributions of megafauna-adapted species and may have played an important role in their dispersal during the Holocene. Thus, Native Americans and their ancestors were likely the first to practice managed relocation. Given this, future managed relocation efforts should be considered for endangered, dispersal limited, and potentially anachronistic tree species.

SYNTHESIS

In this dissertation, I tested the degree of climatic disequilibria and the role of dispersal limitations in shaping the geographic ranges of North American tree species. I found significant climatic disequilibrium in realized ranges at continental scales in line with previous range filling findings (Svenning & Skov 2004; Munguía et al. 2008, 2012; Nogués-Bravo et al. 2014), lending support to this growing body of evidence. Most of the previous analyses which have quantified climatic equilibrium in this manner have concluded that these unfilled portions of potential climatic range are due to inadequate dispersal in response to post-glacial warming

(Svenning & Skov 2004, 2007; Munguía et al. 2008; Normand et al. 2011; Nogués-Bravo et al.

2014). I find consistent lines of evidence supporting this interpretation throughout my dissertation, and generally conclude that range filling appears useful for revealing the influences dispersal limitations at biogeographic scales. Despite this, each chapter also presented evidence which contradicted the interpretation that low apparent range filling is driven by dispersal limitations, which instead could be caused by unaccounted for biotic interactions, soil factors, or combinations of both.

A major caveat of this dissertation is that I found range filling as a raw percentage (as originally defined by Gaston (2003) and Svenning & Skov 2004) to be not especially useful as an ecologically-informative range metric and I subsequently used range filling as residuals from the range size range filling relationship (Chapter I) for further analyses. Nogués-Bravo et al. (2014) similarly found a measure of range filling residuals to better detect the biogeographic influences of dispersal than raw percentages. This was done because proportional range filling is highly correlated with range size (Figure 2, Chapter I). I find this relationship also holds through time

(Chapter III) and in is present even among spatially randomized ranges (Figure 2, Chapter II),

70 suggesting general causes for this trend. Based on the relative adjusted r2 of realized and randomized ranges, at least half of the variation in range filling is attributable to the role of geometry and statistical biases imbedded within our potential range modeling approach. Range filling is sensitive to the scale of the extent, where smaller extents cause higher filling. At the largest scales range filling decreases, and an analysis global scales found low range filling among amphibians (Munguía et al. 2012).

I find small ranged species have lower overall filling for their size, underfilled their potential ranges relative to ecologically null modeled ranges (Figure 2, Chapter I), and showed range shape ratios indicative of dispersal limitations (Figure 5, Chapter I). And yet high filling species for their range size were associated with reduced potential range sizes (Figure 4, Chapter

I), indicating these species had partially specialized to unique and spatially restrictive climates, and were not simply stuck in incoherent climatic combinations by happenstance.

Average range filling tended to decrease through time (Figure 13, Chapter III), consistent with the notion that contemporary disequilibrium is a result of post-glacial climate changes. Yet range filling responses across taxa showed tremendous individual variation (Figure 11, Chapter

III). Almost all taxa rebounded after notable high velocity periods (Figure 12, Chapter III), demonstrating that species overall have largely recovered from significant post-glacial events like the Bølling-Allerød and the Younger Dryas (Figure 13, Chapter III). This contradicts the idea that modern disequilibrium in tree species is the primarily a legacy of the accumulated climate changes following deglaciation that has been forwarded by previous range filling studies in Europe (Svenning & Skov 2004, 2007; Svenning et al. 2008; Nogués-Bravo et al. 2014;

Normand et al. 2017).

I did find support for the overall disequilibrium accumulating consistent with the dispersal limitation hypothesis, however this is a recent trend which began only 5000 years ago

(Figure 13, Chapter III). This trend of decreasing range filling which culminates in modern range filling being the greatest in the last 21000 years is unexpected and deserves further investigation.

The Holocene is often characterized as being climatically stable despite there being significant climate change (Mayewski et al. 2004), and so perhaps these are further evidence of underfilling being caused by climatically driven dispersal limitations. Potential range velocities were higher than realized velocities, evidence that climatic niches were moving faster than species could realize. However, the last 5000 years is a period of relatively low biotic and climatic velocities

(Figure 13, Chapter III) which suggests other possible drivers for this decrease in range filling.

As our knowledge on the extent to which pre-European cultures to utilized and modify their environments continues to expand (Munoz et al. 2010), an intriguing alternative cause could be these underfilled ranges may be the result of human interference. If so, the fact that pre- industrialized societies could cause such significant biogeographic effects on long-lived organisms like trees would fundamentally change our understanding of contemporary species ranges and thus their management into the future.

Svenning and Skov (2007) showed that tree species richness correlated more strongly with accessibility to ice age refugia by modern climate. Species with refugia nearer to ice sheet margin had an advantage in colonizing new habitat that opened as climates warmed and ice retreated. Upon establishing, other species (particularly congenerics) would have had a harder time colonizing this area due to competitive exclusion. The leading species would then have been able to continue expanding its range relatively easily and competitor-free. This process would result in a bimodal pattern in species ranges; with large ranged, high filling species at

72 higher latitudes, and small ranged, low filling species at low latitudes as originally found by

Svenning & Skov (2004).

A bimodal pattern can also be seen in North American trees (Figure 1, Chapter I), although it is less pronounced than European trees (Svenning & Skov 2004). This may be because of the increased number of species in our analyses (447 versus 55). It also could be evidence that species are more dispersal limited in Europe than North America, shown by their lower average range filling values (38% vs 48%, respectively). Species in Europe may have had more difficulty with postglacial range filling due to east-west dispersal barriers like the dry

Mediterranean climates and numerous mountain ranges. The predominance of east-west dispersal barriers in Europe obfuscates range shape analyses because they co-correlate with climatic bands (Baselga et al. 2012). Post-glacial colonization paths are less clear in North

America than Europe, in part because Europe’s peninsulas help to channel potential migration routes. Thus accurately estimating accessibility in North America is more difficult than it is in

Europe, and the potential for cryptic refugia near the ice sheet margins (sensu McLachlan, 2005) may further complicate the actual accessibility of many species.

Testing hypotheses regarding how the relative controls on ranges vary across continents is challenging but will likely help further our understanding of species ranges broadly. As range data from Asian temperate forests are compiled and made available, even more potential hypotheses about the nature range fill will become possible to test. Presumably Asian tree species, particularly those in the mountainous regions of southwestern temperate China, should have even higher range filling than North American trees due to the decreased effects of the last glaciation and increased climatic and topographic heterogeneity.

Traits related to regeneration and dispersal (namely seed mass, dispersal syndrome, and wood density) showed the strongest correlations with range filling among the functional traits we tested (Chapter II). Traits also explained nearly the equal variation among range filling as climate and geography (Figure 7, Chapter II). However, I did not find the relationships I predicted between seed mass and range filling, where light seeded species with longer average dispersal distances would have higher filling. Instead, I found species with larger seeds (and slower growth rates) were associated with high range filling (Figure 8, Chapter II), which does not fit the expectation of poor range expansion as a culprit of low filling. To the contrary, I find more slow-growing, climax associated traits to be correlated with higher range filling. I found high density wood indicative of slow growing species to be correlated with high range filling.

These findings highlight the potential role of biotic interactions. Heavier seeds are associated with increased germination and seedling success, but also rely on animal dispersers. Heavier seeds may also be more beneficial for local dispersal since animal dispersers are ecologically selective, and thus allow for more effective range filling over time.

It is important to note the correlations among traits found in Chapter II were weak overall and do not imply causation. Because traits are phylogenetically correlated (Table 2, Chapter II), it is possible that much of the signal we found among traits and range filling could actually be attributable to certain groups of closely related species doing better than more distantly related species. While I cannot fully rule this out, range filling even when measured as residuals does not show nearly as strong correlations with phylogeny correlations as among species’ functional traits (Table 2, Chapter II). This is what would be expected if range filling is the result of phylogenetically-correlated functional traits but reveals variation beyond the phylogeny itself.

Most of the megafauna-adapted species also did not show low range filling, reduced range sizes, or lower abundances as would have been expected from missing vital dispersers over many millennia of climatic change (Figure 17, Chapter IV). This could be explained by unappreciated dispersal mechanisms still present among these species which have compensated for their missing megafauna dispersers, a point made in Howe’s (1985) original critique of

Janzen & Martin's 1982 theory. Only Osage orange and Kentucky coffeetree show low range filling (Figure 17, Chapter IV). Interestingly, these two species also appear to be the most anachronistic of the five species named by Janzen & Martin (1982), lending some validity to their predictions. The finding that Osage orange’s range was much larger in the FIA range, which can account for transplanting during the 1850s and onward, than the E.L Little’s attempt to drawn ranges without European influence adds further support that it is dispersal limited.

Biotic factors are an alternative explanation to dispersal limitation in explaining unfilled portions of potential ranges. Biotic interactions may influence range filling if the presence of an interacting species has a significant enough impact on the species that it cannot coexist with the interacting species (or cannot exist without it in the case of facilitation; Wisz et al. 2013, Brown and Vellend 2014). Biotic interactions and edaphic preferences are thought to act primarily on more local scales and have been largely ignored and considered unimportant for determining ranges at biogeographic scales. However, seed predation has been shown to be a significant biotic control on some species ranges (Brown & Vellend 2014), and non-analogue vegetation communities which formed after deglaciation may be attributable to the extinction of megaherbivores and subsequently their unique functions on the landscape (Gill et al. 2009, 2012;

Gill 2014). If biotic interactions are important for determining the distributions of species, predicting how climate change will affect species ranges is much more difficult. As the abiotic

75 environment changes, novel assemblages of interacting species will form (Jackson and Overpeck

2000) and these novel interactions may cause species to behave unexpectedly and in ways that could invalidate predictions of species ranges which do not consider biotic interactions.

Soil is another plausible alternative limitation on tree species ranges. The use of soil maps has yet to be fully exploited in potential range modeling. Some of the utility of the commonly-used and well-maintained soil geographic databases for the United States (like

SSURGO and STATSGO2 (U.S. Dept. of Agriculture 2015, 2018) is limited as soil geographers often use categorical variables or ‘soil types’ to describe variation, which are not especially informative and difficult for species distribution models to use. In other instances when quantitative variables are mapped, they may be of variables which are not important to plants.

Despite this, Morin & Lechowicz (2013) used FAO’s ‘Digital Soil Map of the World’ (Sanchez et al. 2009) and found increased soil breadths (i.e. found in a wider range of conditions) among soil depth, water storage capacity, and pH to be positively correlated with range sizes in North

American trees (Morin & Lechowicz 2013). Future work could model potential ranges more accurately by including both climate and soil variables, which may finally help reveal the influence of soil as a range control at biogeographic scales.

Edaphic conditions are difficult to characterize, in part because they can further interact with the abiotic environment, and moreover because soil itself sustains a diverse assemblage of species each of capable of important biotic interactions. Soil can mediate the effects of some climate, for instance a seasonally dry climate may have enough precipitation for a mesic species so long as it is not on well-drained sandy soils. The soil microbiome further is increasingly being shown to be important even as large scale control on species ranges. Brown & Vellend (2014) found by transplanting soil along transects on a Quebec mountain that soil was a likely limiting

76 factor for sugar maple’s (Acer saccharum) elevational range limits, which they concluded may be caused by fungal pathogens. Species with increased receptivity to below-ground mycorrhizal fungal associations had faster responses in post-glacial range migrations (Pither et al. 2018).

Clearly further study into the macroscale effects of soil microbiomes is needed.

Disentangling the abiotic and biotic interactions in both the atmosphere and the pedosphere remains a goal at the frontiers of ecology and biogeography. I show significant progress can be made by further studying measures of range occupancy like range filling in addition to range sizes. Regardless of the drivers of disequilibria, it is likely that evidence of large scale range disequilibria will build. This will force ecologists to understand these patterns and integrate them into their species management, while also allowing us an important opportunity better forecast future species distributions.

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APPENDIX

Appendix Figure 1. Example Model Outputs. Potential range outputs across model algorithms and spatial resolution. Example shown is Honey Locust (Gleditsia triacanthos).

Appendix Figure 2. Range Filling by Resolution. Figures show effect of the resolution of analysis on range filling, which was relatively low.

Appendix Figure 3. Randomized Range Example. a) Randomized range for Honey Locust (Gleditsia triacanthos). Randomized range is equivalent in area to realized range but was generated via a spreading dye process. b) Potential range output (MaxEnt, 10 degrees) for randomized range above.

Appendix Figure 4.1 Range Examples (California buckeye). a) Realized range for Aesculus californica, a Mediterranean endemic with relatively high filling for its size. b) randomized range c) potential range from realized distribution d) potential range from randomized range.

Appendix Figure 4.2 Range Examples (big leaf maple). a) Realized range Acer macrophylum, a pacific northwest endemic which has relatively high filling for its size. b) randomized range c) potential range from realized distribution d) potential range from randomized distribution.

Appendix Figure 4.3 Range Examples (Yellowwood). a) Realized range of Cladrastis kentuckea, likely reflecting strong niche limitations. b) randomized range c) potential range from realized distribution d) potential range from randomized distribution. Note the increase in range filling.

Appendix Figure 4.4 Range Examples (Eastern hemlock) a) Realized range for Tsuga canadesis, a large-range high filling species. b) randomized range c) potential range from realized distribution d) potential range from randomized distribution. Note the decrease in range filling.

BIOGRAPHY Benjamin was born in the Twin Cities of Minnesota and graduated from Central Lakes

College in Brainerd, MN with his A.A. in 2010. From there, Benjamin went the University of

Wisconsin-Madison, where he graduated with a B.S. in 2013 with a double major in Geography and Biological Aspects of Conservation. He is a candidate for the Doctor of Philosophy degree in

Ecology and Environmental Sciences from the University of Maine in December 2018.

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