Abstract
Biotic homogenization—increasing similarity of species composition among ecological communities—has been linked to anthropogenic processes operating over the last century. Fossil evidence, however, suggests that humans have had impacts on ecosystems for millennia. We quantify biotic homogenization of North American mammalian assemblages during the late Pleistocene through Holocene (~30,000 ybp to recent), a timespan encompassing increased evidence of humans on the landscape (~20,000–14,000 ybp). From ~10,000 ybp to recent, assemblages became significantly more homogenous (>100% increase in Jaccard similarity), a pattern that cannot be explained by changes in fossil record sampling. Homogenization was most pronounced among mammals larger than 1 kg and occurred in two phases. The first followed the megafaunal extinction at ~10,000 ybp. The second, more rapid phase began during human population growth and early agricultural intensification (~2,000–1,000 ybp). We show that North American ecosystems were homogenizing for millennia, extending human impacts back ~10,000 years.
Introduction
The global-scale ecological impacts of humans have accelerated over the last several thousand years1,2,3. Human-driven climate changes and exploitation of natural resources, as well as increasing globalization and urbanization linked to rapid population growth, are now altering landscapes to such a degree that few pristine ecosystems remain1,4,5. Consequences of these anthropogenic stressors include widespread extirpation of species6,7, population declines4, geographic range shifts and expansions8, significant ecological downgrading9, and an impending wave of global extinctions10. At the local scale, however, meta-analyses reveal no net decreases in species richness11,12,13, while regional studies document biotic homogenization (i.e., increased similarity in the composition of species among ecological communities, also referred to as reduced β diversity) for several extant groups including birds, fishes14, mammals7,15,16,17, and plants13,18,19, in terrestrial and aquatic settings over the past one hundred years13,15,17,20,21,22.
Biotic homogenization can result from increased species coexistenc21, driven by factors including, but not limited to, range expansions, human-mediated species translocation, and agricultural landscape modification (linked to decreased landscape heterogeneity, increased patch size, and the intentional or unintentional spread of generalist, competitively dominant native or non-native species)6,8,13,19,20,23. It can also result from the extinction or extirpation of endemics7,24,25, which similarly reduces the uniqueness of spatially separated species assemblages15,21 (Supplementary Fig. 1). Ongoing biotic homogenization is a conservation concern because range-expanding and introduced species, as well as those that are displaced or at risk of extinction, may be clustered in particular functional groups (i.e., groupings based on the roles they play in ecosystems)21,26,27. For example, extant mammal species at risk of extinction are commonly large-bodied carnivores28. Such non-random biodiversity loss could lead to profound modifications in the nature of biotic interactions (e.g., trophic interactions), altering the ways in which materials and energy flow through ecosystems26,29, reducing their resilience to ongoing and future perturbation9. In particular, the replacement of keystone species—those whose impacts on interactions and the environment are outsized with respect to their relative abundance—by species whose impacts are proportional to their relative abundance, has broad implications for associated biota (e.g., through the loss of certain ecosystem engineering processes)30 that can include reduction in ecosystem functioning and production of ecosystem services31,32.
Until now, however, biotic homogenization has been studied almost exclusively on relatively short time scales that encompass the last few decades to the past century (e.g.,7,13,15,19). Although historical biodiversity data are valuable for understanding the effects of accelerating anthropogenic climate and landscape change (e.g.,13), these short-term data provide only a partial, time-limited picture of biodiversity change and may incorrectly identify drivers33,34,35. In particular, they do not provide ecological scenarios in which human influence is minor or negligible (often termed baselines) against which observed changes can be compared36, making it difficult to fully understand the magnitude and timing of large-scale human effects on biodiversity. Understanding the deeper-time history of biotic homogenization is therefore critical to better define the contributions of anthropogenic and non-anthropogenic processes.
Evidence is mounting that humans have had landscape-scale effects on ecosystems for thousands of years6,37,38. The dispersal of humans into North America (~16,000 –14,000 ybp and possibly prior to 20,000 ybp)39,40, while coincident with significant climatic perturbation39,41, was also associated with the loss of 72% of large-bodied (>44 kg) mammal genera42,43,44,45. Much later, humans undertook the development of geographically extensive agriculture in North America (starting 2,000–1,000 ybp)37. The net result has been significant ecological perturbation, leading to shifts in species distributions46, major changes in mammal community structure47,48,49,50,51,52,53, and changes to fundamental biotic interactions54,55,56,57. To our knowledge, biotic homogenization has yet to be studied for terrestrial North American mammals at the continental scale and over timescales incorporating the late Pleistocene and entire Holocene (~30,000 ybp to present), a critical period in North American pre-history that encompasses times of both little human influence and strongly anthropogenic scenarios.
We test for biotic homogenization of North American mammalian assemblages during the late Pleistocene and Holocene (~30 ka to modern), including records from before and after the arrival of humans in the Americas (see Methods). Here, we define an assemblage as the co-occurrence of species as recorded by specimens at paleontological or archeological sites, and modern observations records. Our data are taxonomically well-resolved with 8,831 occurrences of 365 species at 366 localities. The data include all extant North American mammalian orders spanning the last 30,000 years divided among six time bins, i.e., 5,000 year intervals between 30,000 ybp and 500 ybp, with the seventh (modern) interval being the 1970–2000s. To minimize bias due to differences in sampling between the modern and fossil records, all included sites were required to have 20 or more species and at least one occurrence each from Artiodactyla, Carnivora, and Rodentia. Our vetting process ensures sampling across a variety of body sizes and representation from clades comprising the majority of non-volant North American mammal diversity (Supplementary Fig. 2).
Biotic homogenization refers to increasing compositional similarity among ecological communities (i.e., declining β diversity), which is frequently quantified as the average of a specified pairwise similarity metric58. We quantify change in the mean species compositional similarity for North American mammalian assemblages using the pairwise Jaccard similarity metric [i.e., J/(A + B-J), where A and B are the number of species present at Site 1 and Site 2, respectively, and J is the number of species shared by the two sites; the metric, varies from zero and one, with zero indicating 100% compositionally distinct assemblages and one indicating compositionally identical assemblages59]. We also compare Jaccard similarity to other less commonly-used metrics (e.g., Forbes Similarity60), and provide a series of null model expectations designed to account for the effects of spatiotemporal changes in sampling. The null model shuffles sites among time bins, thus providing a measure of the significance of change in mean taxonomic similarity among time bins (see Methods)61. We perform the same analyses on subsets of the data to address potential differences among mammalian size classes (species > 1 kg and > 5kg62,63,64) as well extinct and extant species (excluding extinct megafauna15,21).
We perform a series of sensitivity analyses to evaluate the effects of varying intensity of fossil record sampling (i.e., number of sites), topographic complexity, and spatiotemporal changes in the distribution of fossil data (Supplementary Fig. 2)65. To do so, we perform our analyses on subsets of the data, i.e., equalizing the number of sites among time bins and re-sampling, excluding areas of high topographic complexity, and excluding high latitude samples (see Methods). Topographically complex regions such as the Rocky Mountains show comparatively low mean taxonomic similarity among sites, a phenomenon that is typically attributed to high environmental turnover66. Taxonomic similarity among sites also tends to increase with latitude66,67. Thus, changes in the intensity of sampling across topographically variable regions or across latitudes may lead to anomalous changes in mean taxonomic similarity. Our sensitivity analyses are designed to tease apart sampling effects from ecological signal. Graham, et al.47 performed a broadly similar set of analyses, which we extend to create a comparison to modern mammal assemblages, while considering heterogeneity in spatiotemporal patterns of sampling and addressing differences between mammals from different body size classes.
Finally, we explore potential drivers of biotic homogenization by comparing to temporal changes in climate heterogeneity (i.e., differences in climate among sites measured within each time bin)68, species geographic range sizes53, human presence on the North American landscape (~20,000–14,000 ybp)39, extinction of the mammalian megafauna (beginning ~15,000 ybp and culminating by ~11,700 ybp)45, and the development of extensive agriculture (~2,000–1,000 ybp)37. We hypothesize that there were two periods of significant biotic homogenization, the first following the extinction of the mammalian megafauna (~12,000 ybp–10,000 ybp), and second the development of widespread agricultural activities (2,000–1,000 ybp). Support for our hypothesis would constitute strong evidence for an ancient origin of anthropogenic biotic homogenization and amplify calls for deep time perspectives in the study of human impacts on ecosystems.
Results
Mean taxonomic similarity
During the late Quaternary (30,000 ybp to modern), the average taxonomic similarity of mammalian assemblages was relatively stable and within null expectations (null model generated by shuffling sites among time bins; see Methods) until the Holocene (Fig. 1; Table 1). Mean assemblage similarity increased by 0.15 (Jaccard similarity) from the 10,000–5,000 ybp time bin through to the modern (Fig. 1; black line), occurring at the fastest rate between the final two time bins (5,000–500 ybp and 500 ybp–modern; Fig. 1). Assemblages composed of mammals larger than 1 kg and 5 kg showed the greatest degree of homogenization. They increased in similarity by 0.25 (Jaccard similarity) from the ~15,000–10,000 ybp time bin onward (Fig. 1; dashed lines), becoming more homogenous than null expectations from the 10,000 ybp–5,000 ybp time bin onward (Table 1). Large mammal (>1 kg) assemblages experienced two periods of rapid homogenization, from the 15,000–10,000 ybp bin to the 10,000–5,000 ybp bin and from the 5,000–0.5 ybp bin to the modern (Fig. 1; dashed lines). The same patterns are evident when aligning the time bins with the onset of deglaciation at the beginning of Heinrich Stadial 1 and the Pleistocene-Holocene transition (Supplementary Fig. 3a), suggesting that the pattern is not an artefact of how we grouped sites into time bins.
Mammals larger than 1 kg commence homogenizing during the 15,000–10,000 ybp time bin, but the onset of homogenization is delayed until after 10,000 ybp for assemblages including all mammals. Mammals larger than 1 kg are more homogenous than null expectations by the 10,000–5,000 ybp time bin while assemblages of all mammals are more homogenous than null expectations by the 5,000–500 ybp time bin. Change in mean taxonomic similarity (Jaccard similarity index) among sites ± the standard error of the mean. Gray ribbon shows the mean of the null model runs (sites shuffled among time bins) with 95% confidence intervals. Dates of the mammal sites are based on calibrated radiocarbon dates (See Material and Methods). Extinction of the mammal megafauna in North America (ME). The modern time bin (1980’s–2010’s) is portrayed as larger to enhance readability.
Fossil record sampling
Re-sampling (i.e., randomly drawing the same number of sites for each time bin to homogenize sampling intensity) did not change the overall pattern of Holocene biotic homogenization (Supplementary Fig. 4, S5). Furthermore, mean taxonomic similarity as calculated using Jaccard similarity (1 – Jaccard dissimilarity) is uncorrelated with total within-time-bin species richness (i.e., size of the regional species pool; p > 0.05, R2 = 4.0 × 10−4; Supplementary Fig. 6). Exclusion of sites from the Rocky Mountains westward (to reduce the effects of topographic heterogeneity; see Methods; Fig. 1; lighter brown line) and north of the Canadian border (to address trends in sampling area and density) does not alter our results (Fig. 1; gray dotted line). Thus, changes in spatial and taxonomic sampling are not likely to be responsible for the pattern of Holocene biotic homogenization.
Additional similarity metrics
The pattern of Holocene biotic homogenization is apparent for most of the similarity metrics employed herein (Supplementary Fig. 7). The relative stability of nestedness through time suggests that much of the change in mean taxonomic similarity during the Holocene is a result of declining turnover (Supplementary Fig. 7A). The divergent patterns observed for distance decay of similarity and the corrected Forbes Index appear to reflect correlations with the number of sites (p < 0.05, R2 = 0.79; Supplementary Fig. 8B) and regional species richness (p < 0.05, R2 = 0.80; Supplementary Fig. 8B), respectively.
Palaeoclimate turnover
To test for an association between climate heterogeneity and mean mammal taxonomic similarity, we performed a PCA of annual average minimum temperature, annual average maximum temperature, annual actual evapotranspiration (AET), and annual precipitation. We then calculated climate turnover as the mean pairwise climate dissimilarity in PCA scores amongst sites in each time slice. The loadings for both PC1 and PC2 showed that both axes are primarily correlated with annual evapotranspiration (AET) and total annual precipitation (AP) for all time slices. Sampling the climate rasters using the distribution of fossil sites (Fig. 2, solid brown line) shows a more pronounced effect of deglaciation on climate heterogeneity than sampling evenly across the continental USA and southern Canada at 500-year intervals (Fig. 2, solid orange line), simply owing to changes in fossil site distributions among time bins. Both sets of curves, however, show that climate heterogeneity declined between 20,000 ybp and 15,000 ybp with relatively little change thereafter.
Change in climate turnover (mean climate difference) ± the standard error of the mean. Climate estimates are based on de-biased and downscaled earth system model (ESM) climate simulations from recent and paleoclimate models at 0.5 degree resolution68. The orange star indicates the extinction of the mammal megafauna in North America (ME). The purple and blue stars indicate points at which assemblages of large mammals (LM) and all mammals (AM) were more homogenous that null expectations. The lines in brown tone indicate climate rasters sampled at the same locations as the fossil and archeological sites included in the study. The lines in orange tone represent sampling of the climate rasters are equal intervals across the landscape between 35 and 80˚N based on the fact that the majority of the fossil and archeological sites included in the present study occur between these latitudes.
Geographic range size change
After 10,000 ybp, we observe ~50% increases in mean geographic range size among mammals larger than 1 kg (Fig. 3A) but not increases in range fill (occupancy; Fig. 3B). Assemblages including smaller mammals (<1 kg), however, showed much more moderate increases in geographic range size and biotic homogenization that were delayed until after ~5,000 ybp (Fig. 3A).
Mean range size for mammals larger than 1 kg were significantly larger than null expectations by the 5,000-500 ybp time bin. Neither are associated with increases in range occupancy. A mean geographic range size ± standard error of the mean and B mean occupancy (proportion of 1˚ by 1˚ grid cells occupied) ± standard deviation. Gray ribbons show the mean of the null model runs (sites shuffled among time bins) with 95% confidence intervals.
Discussion
While historical biodiversity data are invaluable for understanding recent patterns of biotic homogenization (e.g.,13,15), they do not provide pre- or low human impact scenarios and therefore cannot address when humans began to have large-scale ecological impacts33. We used the Pleistocene through Holocene (30,000 ybp to present) records of North American mammals to assess the onset of biotic homogenization in North America (Supplementary Fig. 2) and address the potential roles of human dispersal (~20,000–14,000 ybp), extinction of the mammal megafauna (~12,000–10,000 ybp), and acceleration of human impacts (e.g., development of extensive agriculture; ~2,000–1,000 ybp). The present study provides, to the best of our knowledge, the most temporally and taxonomically inclusive as well as spatially extensive study of mammalian biotic homogenization to date. We observe mammal assemblages that are homogenized, i.e., more similar than null expectations, as early as 10,000–5,000 ybp, with biotic homogenization commencing between 15,000 and 10,000 ybp for mammals larger than 1 kg and 10,000–5,000 ybp for all mammals (Fig. 1; Table 1). Our various sensitivity and re-sampling scenarios do not change the overall pattern (Fig. 1; Supplementary Figs. 3–6), indicating that this is not an artefact of spatiotemporal changes in fossil record sampling, changes in the intensity of sampling through time, or radiocarbon dating inaccuracies.
Biotic homogenization has been observed for a number of modern assemblages. For instance, heavily invaded aquatic plant communities have increased in similarity by only ~0.02–0.05 (Jaccard’s Index)19, while island bird assemblages, which are more vulnerable to anthropogenic activities, have increased in mean similarity by ~0.04 (Jaccard’s Index)15,69. The most dramatically homogenized modern floras and faunas include Brazilian forests and island mammal assemblages, which have increased in similarity by ~0.16–0.24 (median; Jaccard’s Index)70 and ~0.10 (median; Jaccard’s Index; ranging from 0.00 to 0.40 with only a very small proportion of islands showing an increase greater than 0.2)15. Though similar to some island mammal assemblages, the magnitude of Pleistocene through Holocene biotic homogenization reported here (an increase of 0.15–0.25; >100% change in Jaccard similarity) exceeds that of modern and historical continental vertebrate faunas (typically < 0.05 on the Jaccard Index scale)71,72. These direct comparisons do not account for differences in time bin size (e.g., 5,000 years vs. years to decades), but do highlight the fact that more biotic homogenization has occurred among terrestrial North American mammal assemblages over the last 15,000 years than is captured by modern and historical studies alone.
To further contextualize our findings, we compared them to spatial patterns of modern Western Hemisphere mammal assemblage similarity using a space-for-time comparison73. Modern mammal assemblages are most homogenous at high latitudes and more heterogeneous at low latitudes, such as the Canadian Arctic versus central Mexico (Fig. 4)66, a pattern that also has been documented elsewhere66,74. An increase in Jaccard similarity of ~0.15–0.25 (Fig. 1), as reported herein, is equivalent to the difference in mean assemblage similarity between the Arctic and subtropical faunas of the Western Hemisphere (~30˚ of latitude) (Fig. 4). In other words, the change we observe over the past ~15,000–10,000 years is equivalent to what one would observe if the subtropical mammalian faunas of central Mexico became homogenized to the same extent as Arctic Alaskan faunas, which are currently more than twice as similar across comparable spatial distances (Fig. 4). Based on the above considerations, we are confident that we document biologically significant biotic homogenization of mammalian assemblages during the late Pleistocene and Holocene.
An increase of 0.15-0.25 in assemblage similarity between ~15,000 ybp and Modern for North American mammals is equivalent to the difference in mean similarity between assemblages in Alaska and the same-sized regions in the subtropics at ~30˚ of latitude. The value of each cell is the mean Jaccard similarity of all surrounding cells within a 1000 km radius. Mean similarity between cells is 2.5× higher for Alaskan than for Mexican communities. Circles represent hypothetical assemblages of mammals from Arctic Alaska and Mexico. Silhouettes represent the occurrences of different species in adjacent grid cells. Silhouette credits from phylopic.org: Sarah Werning (Bradypus, Cebinae), Rebecca Groom (Nasua), Lukasiniho (Panthera). Silhouettes are distributed under Creative Commons Attribution 3.0 Unported license.
There are several potential drivers of late Quaternary biotic homogenization. Today, the comparative homogeneity of high latitude mammal faunas (Fig. 4) is attributed to lower climate heterogeneity (i.e., lower dissimilarity of climate among regions)66,67. Over millions of years, climate has also played a central role in structuring terrestrial mammalian faunas75,76,77; North American mammalian faunas over the last ~40 million years were more homogenous when climates were cooler and drier78,79, suggesting the same may have been true for the late Quaternary. The ~30,000 year period of the present study encompasses the Last Glacial Maximum (LGM; ~21,000 ybp) and subsequent long-term warming and deglaciation of the Northern Hemisphere (19,000–11,000 ybp)80, which is likely associated with weakening of latitudinal climate gradients80,81 that could have affected the compositional similarity of mammal assemblages. The last ~19,000 years were also punctuated by several shorter periods of significant climate warming and cooling81, including Heinrich Stadial 1 (17,000–14,700 ybp) and the Bølling-Allerød (14,700–12,900 ybp), which both coincided with glacial retreat82,83,84. During these periods, we expect decreased spatial heterogeneity of climate. The Younger Dryas (12,900–11,700 ybp) was a brief reversal of global warming and return to glacial-like conditions83,85, during which we expect increased climate heterogeneity. Graham, et al.47 implicated environmental changes in declining distance decay of similarity for North American mammal faunas between the Pleistocene and Holocene81. We show that climate heterogeneity declined primarily between 20,000 and 15,000 ybp, prior to the onset of biotic homogenization for mammals larger than 1 kg (Fig. 1). Further, mean similarity of mammalian assemblages could not be differentiated from null expectations until the 10,000–5,000 ybp time bin (Fig. 1), when there was comparatively little change in climate heterogeneity (Fig. 2), suggesting non-climatic drivers of biotic homogenization from late Pleistocene through Holocene (Supplementary Figure 1).
Biotic homogenization can also be driven by the extinction of species with narrow geographic ranges (Supplementary Fig. 1). The Pleistocene-Holocene transition (~11,700 ybp) was the culmination of the extinction of 72% of North American mammal genera larger than 44 kg, which began as early as 15,000 ybp but occurred primarily between 12,000 and 10,000 ybp42,43,45. The results included widespread range shifts86, re-assembly of mammal communities52,54, loss of functional diversity87, and weakening of biotic interactions among surviving species88. Exclusion of the now extinct mammalian megafauna from our analysis, however, did not alter the observed pattern (Fig. 1; solid brown line), likely because the mammalian megafauna possessed geographic ranges that were similar in size to surviving taxa during the late Glacial and immediately prior to their extinction (Supplementary Fig. 9). All else being constant, the loss of the mammal megafauna by ~10,000 ybp should therefore not have favored homogenization or heterogenization (decreasing mean similarity among species assemblages; Supplementary Fig. 1). Excluding possible indirect ecological effects (e.g., trophic cascades), our analysis shows that the extinction of the mammalian megafauna did not directly lead to biotic homogenization.
Nonetheless, North American mammal assemblages were significantly homogenized (i.e., outside the confidence intervals for the null model) by the 10,000 ybp–5,000 ybp (mammals larger than 1 kg) and 5,000–500 ybp time bins (all mammals) (Fig. 1; Table 1). We suggest a connection to the extinction of the majority of the megafauna that occurred between 12,000 and 10,000 ybp45 (Fig. 1; star symbol). Although the loss of much of the North American megafauna did not directly drive Holocene biotic homogenization (Fig. 1; solid brown line), it may have done so via indirect ecological effects. Because most of the now extinct megafauna became extinct near the 10,000 ybp boundary45, we suggest that biotic homogenization began late in the 15,000 ybp–10,000 ybp interval (mammals larger than 1 kg) and do not posit a long delay between the extinctions and their ecological effects.
Large mammals perform a variety of ecological functions, including facilitation of seed dispersal over long distances89,90, maintenance of vegetation structure at the landscape-scale91, moderation of small mammal populations through competition and predation, and, perhaps most importantly, lateral transfer of nutrients92. The causes for the global megafaunal extinctions are still debated and there is conflicting evidence regarding the direct or indirect role played by humans42,44,93,94. Regardless of causal mechanisms, removal of ecosystem engineering megafauna and the resulting dominance of smaller-bodied mammals with different functional roles42,87 had continental-scale ecological consequences for North American terrestrial ecosystems95,96,97, including geographic range expansions and shifts among surviving species53,88.
Today, geographic range expansion, whether climate-mediated or via translocation, is one of the primary drivers of biotic homogenization14,19,71 (Supplementary Fig. 1). After the 10,000–5,000 ybp time bin, we observe ~50% increases in average geographic range size among mammals larger than 1 kg (Fig. 3A) but not increases in range fill (Fig. 3B), consistent with previous studies of mammal range dynamics86,88. These range expansions exceeded the increase in newly available space and equitable climate resulting from glacial retreat alone88, suggesting additional, possibly ecological, drivers. One possible ecological driver, landscape-scale ecosystem changes resulting from the loss of the megafauna, may have driven biotic homogenization for assemblages of mammals larger than 1 kg, given their close temporal association (Fig. 1). Assemblages including smaller mammals (<1 kg), however, showed much more moderate and delayed increases in geographic range size and biotic homogenization until after ~5,000 ybp (Figs. 1 and 3A), possibly reflecting differences in dispersal abilities98.
Regardless of the taxonomic or spatial filters we applied to the data, the fastest rate of biotic homogenization occurred between the 5,000–500 ybp and modern time bins (Fig. 1), coincident with geographic range expansions of ~25% for assemblages including all mammals (Fig. 3A). This second phase of biotic homogenization began during enhanced fire regimes, considerable human population growth, and the development of extensive agriculture (i.e., non-continuous but widespread cultivation) in North America3,37. Human populations may have increased by as much as 10-fold in North America during the penultimate time bin (5,000–500 ybp)3. Habitat alteration by human activities (e.g., clearing of forests, construction of villages) favors population growth among synanthropic species (i.e., those dependent on human-dominated habitats). Such species are favored due to their reliance on resources provided by human habitation (e.g., refuse) and the elimination of their natural predators (e.g., through hunting), among other factors. As human populations and habitation become more widespread, so do synanthropic species, leading to biotic homogenization99. Though it is likely such processes were operating during the mid to late Holocene (the 5,000–500 ybp time bin), they are very unlikely to have been as spatially extensive or intensive as is observed for modern urban environments. The incidence of fires also increased throughout the Holocene in North America, though, in most cases, this has been linked to climate change rather than anthropogenic activities3. Furthermore, fires may in fact produce less homogenous biotas100.
Archeological evidence, however, suggests that, by ~2,000–1,000 ybp (within the penultimate time bin of this study), extensive agriculture was practiced throughout much of the central and eastern United States3,37. Modern agriculture results in spatially-extensive monocultures101, favoring agricultural pests (e.g., voles)102 and generalist species103,104. Today, agricultural intensification is one of the major drivers of biotic homogenization, largely due to reduced landscape heterogeneity and increased patch size23. Similarly, agriculture reduces the capacity of ecosystems to support large pools of species, resulting in spatially homogenous, depauperate floras and faunas105. Early farming practices did not produce the spatially extensive, single-species monocultures of today106,107. Our findings suggest, however, that some combination of anthropogenic processes, which occurred 2,000–1,000 years ago (e.g., habitation, agriculture), may still have had a homogenizing effects on North America’s land mammals (Fig. 1)101.
Modern North America is characterized by the most homogenous mammalian faunas of the last 30,000 years (Fig. 1; Table 1). The 20th century saw the fastest and most spatially extensive human landscape modification, including the development of true agricultural monocultures, further transformation of the landscape into farmland, expansion of human transportation networks, and rapid growth of urban environments2,6. We suggest that modern human niche construction (e.g., agriculture) resulted in a dramatic intensification of ongoing biotic homogenization, producing a further increase of ~0.10 in faunal similarity (67% increase; Jaccard similarity; Fig. 1). Because much of our modern data is based on surveys from state and national parks, our results further suggest that the homogenizing effects of landscape modification have percolated through protected ecosystems. Assuming current rates of invasion and human landscape modification are sustained, North American mammals are projected to homogenize by a further 0.05–0.12 (Jaccard’s Index) during the 21st century, given current human population densities and rates of species endangerment108. If these projections are borne out, continental North American mammalian assemblages will have become as much as 0.37 (Jaccard similarity) more homogenous over the last 15,000 years, an increase in similarity of greater than 300%. For context, such an increase would exceed the difference between modern Arctic and subtropical faunas (Fig. 4).
The scale of projected future biotic homogenization is of concern to wildlife managers, conservation biologists, and human communities relying on a variety of ecosystem services. Such extreme homogenization signals the replacement of complex and spatially variable ecosystems by a few, widespread, simpler ones22, potentially driving the loss of resilience to perturbation109. Reduced resilience may result from the loss of functional redundancy, reduction in “response diversity”, and, thus, the probability of species surviving future perturbations110. The ultimate consequence may be the loss of important ecosystem functions (defined as the transfer of energy and matter among individual species in a community)26. Continuing biotic homogenization into the future may have further consequences for the trajectories of biodiversity, as species-environment relationships are uncoupled, including our ability to forecast ecosystem-scale biodiversity changes, and, more fundamentally, to understand the drivers of biodiversity patterns.
We report biotic homogenization for modern continental North American mammal faunas and incorporate timescales allowing comparison of pre- or low human impact faunas with strongly anthropogenically-impacted ones. We show that biotic homogenization is not just a recent historical phenomenon, it preceded the modern era by as much as 10,000 years. We suggest that Holocene biotic homogenization proceeded in two phases, the first following the extinction of the mammalian megafauna, primarily impacting assemblages of mammals larger than 1 kg, and the second, coinciding with the development of extensive agricultural practices in North America and the rapid spread of anthropogenic biomes that now characterize much of our planet (Fig. 1)111. Our findings therefore contribute to ongoing discussion regarding the long-term environmental impacts of humans and the beginning of the Anthropocene37,112 by showing that biotic homogenization began thousands of years before present.
Methods
A diagram summarizing the various methods is included in Supplementary Fig. 2. All R code is available in the Github Repository113.
Fossil and modern data
North American fossil mammal records are well resolved taxonomically and chronologically, making mammals ideal for understanding the effects of late Quaternary anthropogenic and climatic perturbation. Lists of species at individual sites are taken from a dataset containing 67 extant communities114,115, as well as 298 Late Pleistocene and Holocene communities. Extant species occurrences are derived from a variety of literature and web sources and reflect occurrences during the 1970s to 2000s (operationally modern for the purposes of the present study)114,115. The Late Pleistocene and Holocene occurrences are derived from the FAUNMAP II database (2003), currently the most comprehensive compilation of North American Quaternary mammal occurrences that includes information about taxonomy, geochronology, geology, and taphonomy116. We required each assemblage of species to meet specific criteria for inclusion into the data set. First, assemblages were included only if they comprised a minimum of 20 species, based on sites with the lowest species richness included in ref. 114. Second, assemblages were required to include at least one occurrence of species belonging to each of Rodentia, Artiodactyla, and Carnivora, assuring representation from the clades comprising the majority of non-volant North American mammal diversity.
Delineating and dating fossil sites
We defined sites from the FAUNMAP II database as spatially and/or temporally separated occurrences of mammal species meeting the richness and taxonomic criteria listed above. We included fossil sites that have been dated using both radiocarbon and biostratigraphy. Radiocarbon dates were calibrated using the BchronCalibrate function in the Bchron R package (v. 4.7.5)117 using the IntCal13 calibration curve. We considered two or more stratigraphic layers within the same deposit (sharing the same latitude and longitude), but with calibrated radiocarbon ages that differed by more than 500 years, as separate sites. Layers within that same deposit that were dated to be within 500 years of each other were combined for the purpose of increasing the number of usable sites. Combining dated layers increases time-averaging but is unlikely to influence temporal trends, given that sites were then apportioned to longer time bins (as below). Similarly, two layers with different latitudes and longitudes but similar or identical radiocarbon ages were considered separate sites. We plotted the sites on maps for each time bin using the sp (v. 1.4.5)118, raster (v. 3.4.10)119, mapdata (v. 2.3.0)120, maps (v. 3.4.0)121, and ggplot2 (v. 3.3.5)122 R packages (Supplementary Fig. 10).
Inaccuracies in bulk radiocarbon dates (those dating samples that have been performed prior to the use of Accelerator Mass Spectrometry for radiocarbon dating) are commonly as large as 2,000 years123. Furthermore, a number of sites in the dataset have no associated radiocarbon dates and are dated based on biostratigraphic methods. Thus, to be conservative and avoid false precision, we use 5,000-year time bins. Fossil sites were therefore divided into six 5,000 year time intervals (larger than the typical bulk radiocarbon dating error) based on their associated median radiocarbon or biostratigraphic dates: 30,000–25,000 cal. BP, 25,000–20,000 cal. BP, 20,000–15,000 cal. BP, 15,000–10,000 cal. BP, 10,000–5,000 cal. BP, and 5,000–500 cal. BP. As a test of sensitivity of 5,000 year bins with the above cut off times, we also used a series of bins that aligned with the end of the beginning of Heinrich Stadial 1 (17,000–14,700 ybp), end of the Younger Dryas (12,900–11,700 ybp), and the end of the mid Holocene (8,326–4,200 ybp). The resolution of the mammal data is such that it cannot be divided more finely without adding false precision.
We created the following subsets of the data: (1) only sites occurring east of the Rocky Mountains, thus excluding the most topographically complex regions of North America, which are associated with high rates of community turnover66, (2) only sites occurring south of the Canada/USA border, given that few sites exist north of the border until the most recent time bins due to glacial coverage (Supplementary Fig. 10), (3) all sites excluding extinct megafauna, (4) all sites including species >1 kg, and (5) all sites including species > 5 kg, because the preservation potential, fieldwork collection practices, and dispersal abilities of small and large mammals differ63,98.
Measures of taxonomic similarity
We use the term biotic homogenization to refer specifically to the increasing average compositional similarity of samples (i.e., a decrease in β diversity) through time124. All analyses were performed in R version 4.03125 using the R packages betapart (v. 1.5.4)126, vegan (v. 2.5.7)127, fossil (v. 0.4.0)128, and glm2 (v. 1.2.1)129. The Forbes similarity index was calculated using code by J. Alroy (http://bio.mq.edu.au/~jalroy/Forbes.R)60. To test for biotic homogenization, we used several metrics for compositional similarity and turnover, including the average of each of two measures of pairwise site similarity, the Jaccard and corrected Forbes similarity indices60. We also partition β diversity into its turnover and nestedness components using Simpson similarity (for turnover) and the difference between Sørensen and Simpson similarities (for nestedness) in their pairwise and multi-site implementations130. We also employed two additional methods, Distance Decay of Similarity and Multivariate Dispersion131,132. We include these various metrics for completeness. However, studies of biotic homogenization typically employ the Jaccard index58.
Among the multitude of available similarity metrics, the Jaccard Index is preferred for calculating compositional similarity among samples, given that it excludes joint absences133. The corrected Forbes Index, also excludes joint absences but is intended to better compensate for the known relationship between within site richness and values of the Simpson, Sørensen, and Jaccard Indices60. We also employ Baselga’s method of partitioning nestedness (i.e., when smaller samples are subsets of larger samples) and turnover (i.e., replacement of species among samples), which are both components of β diversity. Because the Simpson Index measures turnover without the influence of richness, the difference between the Sørensen and Simpson indices can be taken as a measure of nestedness130. We also employ the multisite versions of these indices here but note that they are not easily comparable among time bins with different numbers of sites, and they produce biased estimates when the number of communities is unknown (i.e., when the number of sampling units may not equal the number of sites)130,134. Fortunately, pairwise metrics, as employed herein, circumvent these issues134.
Distance decay of similarity is a measure of the steepness of the slope of a regression of similarity calculated using a pairwise dissimilarity metric, in this case the Jaccard Index, as a function of distance132. Multivariate dispersion measures differences in compositional dissimilarity among areas, using mean distances from the centroid of a principal coordinates analysis of dissimilarity matrices calculated using metrics such as Jaccard. Multivariate dispersion facilitates comparison among areas or groups131. It has not, however, been widely applied to study biotic homogenization so is not easily comparable to existing studies.
Range size and occupancy
Range sizes and occupancy were calculated from the fossil occurrence data. To delineate the proximate drivers of biotic homogenization, we calculated geographic range size (km2) for each mammal species occurring at five or more sites in each time bin with multiple 95% convex hulls using a resampling approach implemented in the adehabitatHR R package (v. 0.4.19)135. The method involves estimating the geographic range using single-linkage cluster analysis (clusthr function) followed by size estimation at the 95% level (MCHu2hrsize function). We then calculated the mean geographic range size for each time bin.
To calculate mean proportional occupancy, we divided North America into 100 km by 100 km grid cells under a Behrmann equal area projection (9196 cells). We then assigned sites to a grid cell based on their proximity to its center using the R function spDistsN1in the sp R package (v. 1.4.5)118, thus creating a species by grid cell occurrence matrix. We used this method because sites clustered in space are not treated individually and, thus, occupancy is not inflated. For each species occurring in each time bin, we calculated the proportion of grid cells occupied as a proportion of the total number of cells. We then calculated the mean proportional occupancy for each time bin.
Null model comparison
We test whether mean taxonomic similarity, range size, and occupancy vary across the seven time bins. However, simple regression analyses of similarity versus time is problematic for at least two reasons. First, the number of time bins (n = 7) is too small to provide credible results for a simple parametric correlation analysis, particularly if there are influential points that dominate the regression. The second problem is that biodiversity metrics, including species similarity, can be sensitive to various measures of sampling intensity, including the number of samples, the number of species, the occupancy or fill of the matrix, area samples, and the distance among samples (an issue due to spatial autocorrelation)136. We therefore used null model analyses to address these problems. As an alternative to the simple regression analysis, we used a null model that randomizes the assignment of each site to a particular time bin. This null model was first introduced for analyses of species co-occurrence137. It preserves the species association within each site, the total number of species occurrences across the time span, and the number of sites per time bin. However, it reshuffles patterns of site associations that change through time (shuffling among bins). Operationally, the null model involves drawing sites with their observed complement of species randomly from the entire pool (i.e., all sites across all time bins) without replacement until the number of observed sites for each time bin is achieved. Mean Jaccard similarity, geographic range size, and occupancy were then calculated for each time bin. The procedure was repeated 1,000 times. The null model employed herein allowed us to test for changes in species similarity through time and to test for changes in geographic range size and occupancy.
To assess the significance of observed changes in compositional similarity among sites, we calculated effect sizes as: (mean observed–mean null)/standard deviation of null. We interpreted effect sizes greater than zero as representing biotic homogenization. As an additional control, we used a regression analysis of Jaccard similarity and richness to ensure that there was no statistical correlation with the number of species recorded in each site, which often affects similarity measures. Radiocarbon ages
As mentioned, errors in bulk radiocarbon dates pre-dating the application of Accelerator Mass Spectrometry methods are commonly as large as 2,000 years123. Therefore, site ages determined using bulk radiocarbon dating may be assigned incorrectly to a time bin. To test for the effects of radiocarbon dating inaccuracies, we used a randomization approach. Error was added to dates using random draws from a normal distribution with a mean of zero and standard deviation of 2,000 years. Mean taxonomic similarity was then re-calculated for each time bin. The procedure was repeated 1,000 times. For the radiocarbon error analyses, we calculated mean taxonomic similarity using the Jaccard index.
Re-sampling
The intensity with which the fossil record is sampled is inconsistent through time or space138,139,140,141. Such changes in sampling (e.g., the number of sites and the distances among them) can affect measures of taxonomic similarity and quantification of other macroecological phenomena. To assess the effects of sampling intensity and area on our analyses of taxonomic similarity, we first used a re-sampling approach in which we iteratively selected 15 sites per time bin (the number of sites in the poorest sampled time bin) and re-calculated taxonomic similarity 1,000 times for each dataset. We then determined the minimum longitudinal extent (i.e., the maximum longitudinal distance between sites for the time period with the smallest longitudinal spatial coverage) for the entire dataset (all sites in all time bins) and used the same approach while simultaneously limiting the longitudinal extent of the data for each of the 1,000 iterations. For the sampling intensity analyses, we calculated mean taxonomic similarity using the Jaccard index. This approach therefore accounts for changes in the number of sites as well as their density (mean distance among sites) during the sampling period.
Space-for-time comparison
The mean pairwise taxonomic similarity of mammal assemblages is extensively studied in modern contexts on a variety of spatial scales, particularly in the Western Hemisphere66,142. To contextualize the patterns observed in the fossil record and as a means of assessing whether observed changes may be biologically significant, we make a space-for-time comparison73. We downloaded spatially referenced geographic range data for modern non-volant Western Hemisphere mammals143, a dataset that uses the taxonomy of ref. 144 and includes 1,366 species. The Western Hemisphere mammal dataset has been used in other recent studies of community structure145. We sampled the ranges of extant Western Hemisphere mammals using a Behrmann equal area projection and 100 km by 100 km grid cells using the R packages raster (v. 3.4.10)119 and maptools (v. 1.1.1)146 because smaller grid cell sizes are more subject to false positives (more likely to record a species as present when it is not)147. We considered grid cells to be occupied by a species if the center of the cell intersected with its geographic range145. The result was a species by grid cell occurrence matrix, which we used for further analyses.
We used the R function spDistsN1 in the sp R package148 to calculate the great circle distances amongst grid cells. Using a spatial window of 1000 km, we subsampled grid cells surrounding each focal grid cell using the inverse of the great circle distance as the probability of selection66. For each subsampled group of grid cells, we calculated taxonomic similarity using the Jaccard Index. We then plotted the values back onto projected maps of the Western Hemisphere under a Behrmann equal-area projection.
Paleoclimate data
To test for an association between climate heterogeneity and mean mammal taxonomic similarity, we downloaded North American paleoclimate data from de-biased and downscaled earth system model (ESM) climate simulations based on recent paleoclimate models at 0.5 degree resolution68. We downloaded four climate variables from the Dryad digital database (http://datadryad.org/resource/doi:10.5061/dryad.1597g): annual average minimum temperature, annual average maximum temperature, annual actual evapotranspiration (AET), and total annual precipitation. These climate variables are known to correlate with mammal diversity67,149. Climate variables of 500-year intervals were a) sampled evenly across the continental USA and southern Canada, where the majority of mammal fossil sites are located and b) averaged into 5,000-year time intervals for comparison to fossil mammal data. Climate and mammal data were projected into a WGS-84 global coordinate system for analysis.
We summarized the paleoclimate data using principal components analysis (PCA). We subjected the paleoclimate data for all sites in each time slice to a single PCA and extracted values for the first and second principal components. We then calculated climate turnover as the mean pairwise climate dissimilarity in PCA scores amongst sites in each time slice.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability
Pleistocene and Holocene mammal data were collected from the published Faunmap 2.0 database (https://ucmp.berkeley.edu/faunmap/). Data from individual fossil and archeological sites were not collected by the authors of the present study. Faunmap was assembled from published occurrence data (described here: https://ucmp.berkeley.edu/faunmap/). Modern mammal occurrence data were collected from the following sources, Brown & Nicoletto (1991) and Lyons & Smith (2013). Brown & Nicoletto (1991) and Lyons & Smith (2013) compiled mammal occurrence data from various sources, including within national parks, as described therein. Software used in data collection included an internet browser (Google Chrome) and Microsoft excel. The data used in this study have been deposited in the Github repository https://doi.org/10.5281/zenodo.6518845. Source data are provided in this paper.
Code availability
The R code used during the current study is available in the Github repository, https://doi.org/10.5281/zenodo.6518845.
References
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
Ellis, E. C. et al. Used planet: a global history. Proc. Natl Acad. Sci. USA 110, 7978–7985 (2013).
Marlon, J. R. et al. Global biomass burning: a synthesis and review of Holocene paleofire records and their controls. Quat. Sci. Rev. 65, 5–25 (2013).
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 52, 52–58 (2012).
Boivin, N. L. et al. Ecological consequences of human niche construction: examining long-term anthropogenic shaping of global species distributions. Proc. Natl Acad. Sci. USA 113, 6388–6396 (2016).
Petrozzi, F. et al. Surveys of mammal communities in a system of five forest reserves suggest an ongoing biotic homogenization process for the Niger Delta (Nigeria). Trop. Zool. 28, 95–113 (2015).
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).
Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).
Ceballos, G. et al. Accelerated modern human–induced species losses: entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).
Sax, D. F. & Gaines, S. D. Species diversity: from global decreases to local increases. Trends Ecol. Evol. 18, 561–566 (2003).
Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proc. Natl Acad. Sci. USA 110, 19456–19459 (2013).
Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).
Baiser, B., Olden, J. D., Record, S., Lockwood, J. L. & McKinney, M. L. Pattern and process of biotic homogenization in the New Pangaea. Proc. R. Soc. Lond. B: Biol. Sci. 279, 4772–4777 (2012).
Longman, E. K., Rosenblad, K. & Sax, D. F. Extreme homogenization: the past, present and future of mammal assemblages on islands. Glob. Ecol. Biogeogr. 27, 77–95 (2018).
Spear, D. & Chown, S. L. Taxonomic homogenization in ungulates: patterns and mechanisms at local and global scales. J. Biogeogr. 35, 1962–1975 (2008).
Tóth, A. B., Lyons, S. K. & Behrensmeyer, A. K. A century of change in Kenya’s mammal communities: increased richness and decreased uniqueness in six protected areas. PLoS ONE 9, e93092 (2014).
Qian, H. & Ricklefs, R. E. The role of exotic species in homogenizing the North American flora. Ecol. Lett. 9, 1293–1298 (2006).
Muthukrishnan, R. & Larkin, D. J. Invasive species and biotic homogenization in temperate aquatic plant communities. Glob. Ecol. Biogeogr. 29, 656–667 (2020).
Early, R. et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun. 7 1–9 (2016).
Olden, J. D. & Poff, N. L. Toward a mechanistic understanding and prediction of biotic homogenization. Am. Naturalist 162, 442–460 (2003).
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).
Vellend, M. et al. Homogenization of forest plant communities and weakening of species–environment relationships via agricultural land use. J. Ecol. 95, 565–573 (2007).
Byers, J. E., Wright, J. T. & Gribben, P. E. Variable direct and indirect effects of a habitat‐modifying invasive species on mortality of native fauna. Ecology 91, 1787–1798 (2010).
Didham, R. K., Tylianakis, J. M., Gemmell, N. J., Rand, T. A. & Ewers, R. M. Interactive effects of habitat modification and species invasion on native species decline. Trends Ecol. Evol. 22, 489–496 (2007).
Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).
Lavery, T. H., Posala, C. K., Tasker, E. M. & Fisher, D. O. Ecological generalism and resilience of tropical island mammals to logging: a 23 year test. Glob. Change Biol. 26, 3285–3293 (2020).
Smith, F. A., Smith, R. E. E., Lyons, S. K. & Payne, J. L. Body size downgrading of mammals over the late Quaternary. Science 360, 310–313 (2018).
Sinclair, A. Mammal population regulation, keystone processes and ecosystem dynamics. Philos. Trans. R. Soc. B: Biol. Sci. 358, 1729–1740 (2003).
Ellison, A. M. et al. Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. Front. Ecol. Environ. 3, 479–486 (2005).
O’Connor, N. E. & Crowe, T. P. Biodiversity loss and ecosystem functioning: distinguishing between number and identity of species. Ecology 86, 1783–1796 (2005).
Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).
Barnosky, A. D. et al. Merging paleobiology with conservation biology to guide the future of terrestrial ecosystems. Science 355, eaah4787 (2017).
Mihoub, J.-B. et al. Setting temporal baselines for biodiversity: the limits of available monitoring data for capturing the full impact of anthropogenic pressures. Sci. Rep. 7, 1–13 (2017).
Beller, E. et al. Toward principles of historical ecology. Am. J. Bot. 104, 645–648 (2017).
Dietl, G. P. et al. Conservation paleobiology: leveraging knowledge of the past to inform conservation and restoration. Annu. Rev. Earth Planet. Sci. 43, 79–103 (2015).
Stephens, L. et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).
Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl Acad. Sci. USA 118 e2023483118 (2021).
Waters, M. R. Late Pleistocene exploration and settlement of the Americas by modern humans. Science 365 https://doi.org/10.1126/science.aat5447 (2019).
Bennett, M. R. et al. Evidence of humans in North America during the last glacial maximum. Science 373, 1528–1531 (2021).
Lisiecki, L. E. & Raymo, M. E. Plio–Pleistocene climate evolution: trends and transitions in glacial cycle dynamics. Quat. Sci. Rev. 26, 56–69 (2007).
Lyons, S. K., Smith, F. A. & Brown, J. H. Of mice, mastodons and men: human-mediated extinctions on four continents. Evol. Ecol. Res. 6, 339–358 (2004).
Barnosky, A. D. Megafauna biomass tradeoff as a driver of Quaternary and future extinctions. Proc. Natl Acad. Sci. USA 105, 11543–11548 (2008).
Koch, P. L. & Barnosky, A. D. Late Quaternary extinctions: state of the debate. Ann. Rev. Ecol. Evol. Syst. 37 215–250 (2006).
Faith, J. T. & Surovell, T. A. Synchronous extinction of North America’s Pleistocene mammals. Proc. Natl Acad. Sci. USA 106, 20641–20645 (2009).
Pineda-Munoz, S., Wang, Y., Lyons, S. K., Tóth, A. B. & McGuire, J. L. Mammal species occupy different climates following the expansion of human impacts. Proc. Natl Acad. Sci. USA 118, e1922859118 (2021).
Graham, R. W. et al. Spatial response of mammals to late quaternary environmental fluctuations. Science 272, 1601–1606 (1996).
Blois, J. L., McGuire, J. L. & Hadly, E. A. Small mammal diversity loss in response to late-Pleistocene climatic change. Nature 465, 771–775 (2010).
Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: individualistic responses of species in space and time. Proc. R. Soc. B: Biol. Sci. 277, 661 (2010).
Lyons, S. K., Wagner, P. J. & Dzikiewicz, K. Ecological correlates of range shifts of Late Pleistocene mammals. Philos. Trans. R. Soc. B: Biol. Sci. 365, 3681–3693 (2010).
Lyons, S. K. et al. The changing role of mammal life histories in Late Quaternary extinction vulnerability on continents and islands. Biol. Lett. 12, https://doi.org/10.1098/rsbl.2016.0342 (2016).
Pineda‐Munoz, S. et al. Body mass‐related changes in mammal community assembly patterns during the late Quaternary of North America. Ecography 44, 56–66 (2021).
Lyons, S. K. A quantitative model for assessing community dynamics of Pleistocene mammals. Am. Naturalist 165, E168–E185 (2005).
Lyons, S. K. et al. Holocene shifts in the assembly of plant and animal communities implicate human impacts. Nature 529, 80–83 (2016).
Pires, M. M. et al. Pleistocene megafaunal interaction networks became more vulnerable after human arrival. Proc. R. Soc. B: Biol. Sci. 282, 20151367 (2015).
Pires, M. M., Guimarães, P. R., Galetti, M. & Jordano, P. Pleistocene megafaunal extinctions and the functional loss of long‐distance seed‐dispersal services. Ecography 41, 153–163 (2018).
Galetti, M. et al. Ecological and evolutionary legacy of megafauna extinctions. Biol. Rev. 93, 845–862 (2018).
Olden, J. D., Lockwood, J. L. & Parr, C. L. In Conservation biogeography (eds. Ladle, R. & Whittaker, R. J.) Ch. 9, 224–243 (John Wiley & Songs, 2011).
Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence-absence data. J. Anim. Ecol. 72, 367–382 (2003).
Alroy, J. A new twist on a very old binary similarity coefficient. Ecology 96, 575–586 (2015).
Ulrich, W. & Gotelli, N. J. Null model analysis of species nestedness patterns. Ecology 88, 1824–1831 (2007).
Behrensmeyer, A. K., Western, D. & Boaz, D. E. D. New perspectives in vertebrate paleoecology from a recent bone assemblage. Paleobiology 5, 12–21 (1979).
Behrensmeyer, A. K. & Dechant Boaz, D. E. In Fossils in the Making (ed. Behrensmeyer, A.K.) 72–92 (University of Chicago Press, 1980).
Andrews, P. Owls, caves and fossils: predation, preservation and accumulation of small mammal bones in caves, with an analysis of the Pleistocene cave faunas from Westbury-sub-Mendip, Somerset, UK (University of Chicago Press, 1990).
Badgley, C. Tectonics, topography, and mammalian diversity. Ecography 33, 220–231 (2010).
Buckley, L. B. & Jetz, W. Linking global turnover of species and environments. Proc. Natl Acad. Sci. USA 105, 17836–17841 (2008).
Qian, H., Badgley, C. & Fox, D. L. The latitudinal gradient of beta diversity in relation to climate and topography for mammals in North America. Glob. Ecol. Biogeogr. 18, 111–122 (2009).
Lorenz, D. J., Nieto-Lugilde, D., Blois, J. L., Fitzpatrick, M. C. & Williams, J. W. J. S. d. Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD. 3, 160048 (2016).
Rosenblad, K. C. & Sax, D. F. A new framework for investigating biotic homogenization and exploring future trajectories: Oceanic island plant and bird assemblages as a case study. Ecography 40, 1040–1049 (2017).
Kortz, A. R. & Magurran, A. E. Increases in local richness (α-diversity) following invasion are offset by biotic homogenization in a biodiversity hotspot. Biol. Lett. 15, 20190133 (2019).
Castro, S. A. et al. Partitioning β-diversity reveals that invasions and extinctions promote the biotic homogenization of Chilean freshwater fish fauna. PLoS ONE 15, e0238767 (2020).
Peoples, B. K., Davis, A. J., Midway, S. R., Olden, J. D. & Stoczynski, L. Landscape-scale drivers of fish faunal homogenization and differentiation in the eastern United States. Hydrobiologia 847, 3727–3741 (2020).
Blois, J. L., Williams, J. W., Fitzpatrick, M. C., Jackson, S. T. & Ferrier, S. Space can substitute for time in predicting climate-change effects on biodiversity. Proc. Natl Acad. Sci. USA 110, 9374–9379 (2013).
Qian, H. & Xiao, M. Global patterns of the beta diversity energy relationship in terrestrial vertebrates. Acta Oecol 39, 67–71 (2012).
Fritz, S. A. et al. Twenty-million-year relationship between mammalian diversity and primary productivity. Proc. Natl Acad. Sci. USA 113, 10908–10913 (2016).
Figueirido, B., Janis, C. M., Pérez-Claros, J. A., Renzi, M. D. & Palmqvist, P. Cenozoic climate change influences mammalian evolutionary dynamics. Proc. Natl Acad. Sci. USA 109, 722–727 (2012).
Barnosky, A. D., Hadly, E. A. & Bell, C. J. Mammalian response to global warming on varied temporal scales. J. Mammal. 84, 354–368 (2003).
Fraser, D., Hassall, C., Gorelick, R. & Rybczynski, N. Mean annual precipitation explains spatiotemporal patterns of Cenozoic mammal beta diversity and latitudinal diversity gradients in North America. PloS ONE 9, e106499 (2014).
Darroch, S. A. F., Webb, A. E., Longrich, N. & Belmaker, J. Palaeocene–Eocene evolution of beta diversity among ungulate mammals in North America. Glob. Ecol. Biogeogr. 23, 757–768 (2014).
Clark, P. U. et al. Global climate evolution during the last deglaciation. Proc. Natl Acad. Sci. USA 109, E1134–E1142 (2012).
Andersen, K. K. et al. High-resolution record of Northern Hemisphere climate extending into the last interglacial period. Nature 431, 147 (2004).
Hodell, D. A. et al. Anatomy of Heinrich Layer 1 and its role in the last deglaciation. Paleoceanography 32, 284–303 (2017).
McManus, J. F., Francois, R., Gherardi, J.-M., Keigwin, L. D. & Brown-Leger, S. Collapse and rapid resumption of Atlantic meridional circulation linked to deglacial climate changes. Nature 428, 834–837 (2004).
Thiagarajan, N., Subhas, A. V., Southon, J. R., Eiler, J. M. & Adkins, J. F. Abrupt pre-Bølling–Allerød warming and circulation changes in the deep ocean. Nature 511, 75–78 (2014).
Alley, R. B. The Younger Dryas cold interval as viewed from central Greenland. Quat. Sci. Rev. 19, 213–226 (2000).
Lyons, S. K. A quantitative assessment of the range shifts of Pleistocene mammals. J. Mammal. 84, 385–402 (2003).
Davis, M. What North America’s skeleton crew of megafauna tells us about community disassembly. Proc. R. Soc. B. 284, 20162116 (2017).
Tóth, A. B. et al. Reorganization of surviving mammal communities after the end-Pleistocene megafaunal extinction. Science 365, 1305–1308 (2019).
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).
Ripple, W. J. et al. Collapse of the world’s largest herbivores. Sci. Adv. 1, e1400103 (2015).
Owen-Smith, R. N. Megaherbivores: the influence of very large body size on ecology (Cambridge university press, 1992).
Doughty, C. E. et al. Global nutrient transport in a world of giants. Proceedings of the National Academy of Sciences USA (2015).
Araujo, B. B., Oliveira-Santos, L. G. R., Lima-Ribeiro, M. S., Diniz-Filho, J. A. F. & Fernandez, F. A. Bigger kill than chill: the uneven roles of humans and climate on late Quaternary megafaunal extinctions. Quat. Int. 431, 216–222 (2017).
Stewart, M., Carleton, W. C. & Groucutt, H. S. Climate change, not human population growth, correlates with Late Quaternary megafauna declines in North America. Nat. Commun. 12, 1–15 (2021).
Gill, J. L., Williams, J. W., Jackson, S. T., Lininger, K. B. & Robinson, G. S. Pleistocene megafaunal collapse, novel plant communities, and enhanced fire regimes in North America. Science 326, 1100–1103 (2009).
Johnson, C. N. Ecological consequences of Late Quaternary extinctions of megafauna. Proc. R. Soc. Lond. B: Biol. Sci., rspb 2008, 1921 (2009).
Barnosky, A. D. et al. Variable impact of late-Quaternary megafaunal extinction in causing ecological state shifts in North and South America. Proc. Natl Acad. Sci. USA 113, 856–861 (2016).
Kelt, D. A. & Van Vuren, D. Energetic constraints and the relationship between body size and home range area in mammals. Ecology 80, 337–340 (1999).
McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Arnan, X., Cerdá, X. & Rodrigo, A. Do Forest Fires Make Biotic Communities Homogeneous or Heterogeneous? Patterns of taxonomic, functional, and phylogenetic ant beta diversity at local and regional landscape scales. Front. Forests Glob. Change 3, https://doi.org/10.3389/ffgc.2020.00067 (2020).
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 1–8 (2015).
Luque-Larena, J. J. et al. Recent large-scale range expansion and outbreaks of the common vole (Microtus arvalis) in NW Spain. Basic Appl. Ecol. 14, 432–441 (2013).
Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: toward a global functional homogenization? Front. Ecol. Environ. 9, 222–228 (2011).
Rader, R., Bartomeus, I., Tylianakis, J. M. & Laliberté, E. The winners and losers of land use intensification: Pollinator community disassembly is non‐random and alters functional diversity. Divers. Distrib. 20, 908–917 (2014).
Tilman, D. et al. Forecasting agriculturally driven global environmental change. science 292, 281–284 (2001).
Price, T. D. Ancient farming in eastern North America. Proc. Natl Acad. Sci. USA 106, 6427–6428 (2009).
Smith, B. D. The origins of agriculture in the Americas. Evolut. Anthropol.: Issues, N., Rev. 3, 174–184 (1994).
Olden, J. D., Poff, N. L. & McKinney, M. L. Forecasting faunal and floral homogenization associated with human population geography in North America. Biol. Conserv. 127, 261–271 (2006).
Olden, J. D., LeRoy Poff, N., Douglas, M. R., Douglas, M. E. & Fausch, K. D. Ecological and evolutionary consequences of biotic homogenization. Trends Ecol. Evol. 19, 18–24 (2004).
Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364 (2013).
Ellis, E. C. & Ramankutty, N. Putting people in the map: anthropogenic biomes of the world. Front. Ecol. Environ. 6, 439–447 (2008).
Toivanen, T. et al. The many Anthropocenes: a transdisciplinary challenge for the Anthropocene research. Anthropocene Rev. 4, 183–198 (2017).
Biotic homogenization (Github, 2022).
Brown, J. H. & Nicoletto, P. F. Spatial scaling of species composition: body masses of North American Land Mammals. Am. Naturalist 138, 1478–1512 (1991).
Lyons, S. K. & Smith, F. A. In Animal body size: linking pattern and process across space, time, and taxonomic group (eds. Smith & S. Kathleen Lyons) (University of Chicago Press, 2013).
Graham, R. W. & E. L. Lundelius, J. FAUNMAP II: New data for North America with a temporal extension for the Blancan, Irvingtonian and early Rancholabrean. FAUNMAP II Database, version 1.0., 2010).
Haslett, J. & Parnell, A. A simple monotone process with application to radiocarbon-dated depth chronologies. J. Roy. Stat. Soc. Ser. C. (Appl. Stat.) 57, 399–418 (2008).
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5 (2005).
raster: Geographic data analysis and modeling version 3.4-10 (2021).
mapdata: Extra Map Database. R package version 2.3.0. (2018).
maps: Draw Geographical Maps version 3.4.0 (2021).
Wickham, H. ggplot2: elegant graphics for data analysis (Springer-Verlag, 2016).
Grimm, E. C., Maher, L. J. Jr & Nelson, D. M. The magnitude of error in conventional bulk-sediment radiocarbon dates from central North America. Quatern. Res 72, 301–308 (2009).
Whittaker, R. H. Vegetation of the Siskiyou mountains, Oregon and California. Ecol. Monogr. 30, 279–338 (1960).
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).
Baselga, A. & Orme, D. Package ‘betapart’. (2012).
Package vegan version 2.5-7 (2012).
Vavrek, M. J. fossil: palaeoecological and palaeogeographical analysis tools. Palaeontologia Electron. 14, 1T (2011).
Marschner, I. C. glm2: Fitting generalized linear models with convergence problems. R. J. 3, 12–15 (2011).
Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).
Nekola, J. C. & McGill, B. J. Scale dependency in the functional form of the distance decay relationship. Ecography 37, 309–320 (2014).
Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).
Marion, Z. H., Fordyce, J. A. & Fitzpatrick, B. M. Pairwise beta diversity resolves an underappreciated source of confusion in calculating species turnover. Ecology 98, 933–939 (2017).
Calenge, C. A collection of tools for the estimation of animals home range. (2017).
Ulrich, W. et al. Species richness correlates of raw and standardized co‐occurrence metrics. Glob. Ecol. Biogeogr. 27, 395–399 (2018).
Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).
Newell, N. D. Adequacy of the fossil record. J. Paleontol. 33, 488–499 (1959).
Raup, D. M. Biases in the fossil record of species and genera. Bull. Carnegie Mus. Nat. Hist. 13, 85–91 (1979).
Kidwell, S. M. & Holland, S. M. The quality of the fossil record: implications for evolutionary analyses. Annu. Rev. Ecol. Syst. 33, 561–588 (2002).
Benton, M. J., Dunhill, A. M., Lolyd, G. T. & Marx, F. G. In Comparing the geological and fossil records: implications for biodiversity studies Vol. 358 (eds. McGowan, A. J. & A. B. Smith, A. B.) 63–94 (Geological Society of London, 2011).
Graham, C. H. & Fine, P. V. A. Phylogenetic beta diversity: linking ecological and evolutionary processes across space in time. Ecol. Lett. 1265–1277 (2008).
Patterson, B. D. et al. Digital Distribution Maps of the Mammals of the Western Hemisphere, version 3.0. NatureServe, (Arlington, Virginia, USA, 2007).
Wilson, D. E. & Reeder, D. M. Mammal species of the world:ataxonomic and geographic reference. 3rd edition. (Johns Hopkins University Press,Baltimore, Maryland, 2,142 pp 2005).
Fraser, D. & Lyons, S. K. Biotic interchange has structured Western Hemisphere mammal communities. Glob. Ecol. Biogeogr. 26, 1408–1422 (2017).
Bivand, R. & Lewin-Koh, N. J. Maptools: Tools for Reading and Handling Spatial Objects R package (2021).
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007).
Bivand, R. S., Pebesma, E. J. & Gomez-Rubio, V. Applied spatial data analysis with R. (Springer, 2008).
Currie, D. J. et al. Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecol. Lett. 7, 1121–1134 (2004).
Acknowledgements
D.F. works on the traditional and unceded territories of the Algonquin-Anishnaabe Nation. This work was supported by the Smithsonian Institution’s National Museum of Natural History Program grant to the Evolution of Terrestrial Ecosystems Program (ETE) and by a NSF Research Coordination Network Grant DEB 1257625 to S.K.L., A.K.B., and N.J.G. This is ETE publication #352. DF was supported by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada, Research Activity Grants from the Canadian Museum of Nature, and a Peter Buck Postdoctoral Fellowship endowed by Dr. Peter Buck and awarded by the Smithsonian’s National Museum of Natural History.
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All authors (D.F., A.V., A.B.T., M.A.B., J.T.E., W.A.B., A.K.B., M.D., A.D., J.T.F., G.R.G., N.J.G., A.M.J., C.V.L., B.J.M., J.H.M., S.P.M., R.P., A.B.S., L.C.S., S.K.L.) contributed to the development and interpretation of the ideas in this manuscript and edited the writing. D.F. curated and analyzed the data, produced the figures, and wrote the paper. A.V. performed part of the data analysis, aided in the writing of early versions of the manuscript, and aided in figure design. A.B.T. provided R code for data analysis and analyzed early versions of the data. M.A.B. aided in figure design and in the writing of early versions of the manuscript. J.T.E. aided in the writing of early and recent versions of the manuscript.
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Fraser, D., Villaseñor, A., Tóth, A.B. et al. Late quaternary biotic homogenization of North American mammalian faunas. Nat Commun 13, 3940 (2022). https://doi.org/10.1038/s41467-022-31595-8
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DOI: https://doi.org/10.1038/s41467-022-31595-8
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