Database of Geographic Range of Species

Is there a database of organisms which would contain their queriable geographic location?

I would need to perform a rather simple query, such asAnimals of , where Location is some well defined geographic area such as Czech Republic or Europe.

So far I have found multiple lists on Wikipedia and other webpages, however they seem to be quite incomplete as their intersect is quite small. Moreover, I have found EOL (Encyclopedia of Life) collections but they appear to struggle the same way.

This is to a large extent a question of how reliable the data in the database needs to be. Reliability (and spatial scale) will differ between datasets and between species groups within datasets, and it is difficult to give a general recommendation. I doubt that you will find a single database with good coverage over all taxonomic groups, even if it is in the form of country checklists. For the most reliable information, curated country checklists for specific taxonomic groups will probably be best, but these have to be searched for individually for each taxonomic group of interest.

As a starting point, you might want to look at the occurence data that can be found in (The Global Biodiversity Facility). The data found there is certainly not complete, and it will be misleading for many species. However, for the current distribution of relatively well-known groups of species it will give you a good idea of their distribution. This has to be evaluated on a case-by-case basis though. You can access the data in gbif using external tools, for instance using R through rgbif (there is also tools for python or other languages). At the blog you can find a tutorial on how to get a species list for a particular country using rgbif (more specifically the functiondensity_spplist).

The GBIF database that was suggested can give you locations of occurrences given a specific animal. But you asked for lists of animals given some location. An excellent tool to give a list of animals given a location in the world is the Map of Life. Click on "Species by Location" which gives a map of the world, and click anywhere to get lists of species that could occur there.

Geographic location and phylogeny are the main determinants of the size of the geographical range in aquatic beetles

Why some species are widespread while others are very restricted geographically is one of the most basic questions in biology, although it remains largely unanswered. This is particularly the case for groups of closely related species, which often display large differences in the size of the geographical range despite sharing many other factors due to their common phylogenetic inheritance. We used ten lineages of aquatic Coleoptera from the western Palearctic to test in a comparative framework a broad set of possible determinants of range size: species' age, differences in ecological tolerance, dispersal ability and geographic location.


When all factors were combined in multiple regression models between 60-98% of the variance was explained by geographic location and phylogenetic signal. Maximum latitudinal and longitudinal limits were positively correlated with range size, with species at the most northern latitudes and eastern longitudes displaying the largest ranges. In lineages with lotic and lentic species, the lentic (better dispersers) display larger distributional ranges than the lotic species (worse dispersers). The size of the geographical range was also positively correlated with the extent of the biomes in which the species is found, but we did not find evidence of a clear relationship between range size and age of the species.


Our findings show that range size of a species is shaped by an interplay of geographic and ecological factors, with a phylogenetic component affecting both of them. The understanding of the factors that determine the size and geographical location of the distributional range of species is fundamental to the study of the origin and assemblage of the current biota. Our results show that for this purpose the most relevant data may be the phylogenetic history of the species and its geographical location.

What determines a species’ geographical range? Thermal biology and latitudinal range size relationships in European diving beetles (Coleoptera: Dytiscidae)

1. The geographical range sizes of individual species vary considerably in extent, although the factors underlying this variation remain poorly understood, and could include a number of ecological and evolutionary processes. A favoured explanation for range size variation is that this result from differences in fundamental niche breadths, suggesting a key role for physiology in determining range size, although to date empirical tests of these ideas remain limited.

2. Here we explore relationships between thermal physiology and biogeography, whilst controlling for possible differences in dispersal ability and phylogenetic relatedness, across 14 ecologically similar congeners which differ in geographical range extent European diving beetles of the genus Deronectes Sharp (Coleoptera, Dytiscidae). Absolute upper and lower temperature tolerance and acclimatory abilities are determined for populations of each species, following acclimation in the laboratory.

3. Absolute thermal tolerance range is the best predictor of both species’ latitudinal range extent and position, differences in dispersal ability (based on wing size) apparently being less important in this group. In addition, species’ northern and southern range limits are related to their tolerance of low and high temperatures respectively. In all cases, absolute temperature tolerances, rather than acclimatory abilities are the best predictors of range parameters, whilst the use of independent contrasts suggested that species’ thermal acclimation abilities may also relate to biogeography, although increased acclimatory ability does not appear to be associated with increased range size.

4. Our study is the first to provide empirical support for a relationship between thermal physiology and range size variation in widespread and restricted species, conducted using the same experimental design, within a phylogenetically and ecologically controlled framework.

Appendix S1. Collection localities, southern and northern range limits, latitudinal range extents (LRE) and latitudinal range central points (LRCP) for Deronectes species studied.

Appendix S2. Body mass, wing size and thermal performance of Deronectes species.

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Materials and methods

Specimen collection, maintenance and preparation

Adult Deronectes were collected during spring and summer 2006 (see Appendix S1, Supporting Information) standardizing as much as possible for season of collection, working a D-Framed pond net (1 mm mesh, dimensions 20 × 25 cm) along stream banks. We studied adults as these are the life-history stage of longest duration (≥1 year), readily collected from the field, whilst larvae are short lived (c. 1–2 months), rarely collected due to their interstitial habits, and morphologically intractable. In addition, it is the adult stage which overwinters, and survives periodic droughts ( Nilsson & Holmen 1995 ), making adult thermal tolerance most relevant here. All individuals collected were early post-teneral adults, minimizing any possible confounding effects due to age variation ( Bowler & Terblanche 2008 ). All species were collected as close as possible to the central point of their latitudinal ranges, to avoid the possible confounding effects of local adaptation in range edge populations, and to ensure data were comparable for each species ( Thompson et al. 1999 ). Given the largely allopatric occurrence of many species, and differences in the latitudinal position of their ranges, it is impossible to sample all taxa from the same latitude. Data on species’ geographical distributions were taken from Fery & Brancucci (1997) and Fery & Hosseinie (1998) . Latitudinal range extents were calculated as the difference (in degrees latitude) between northern and southern distributional limits ( Gaston 1994 ), and latitudinal range central points determined as the mid-point of each species’ latitudinal range extent (see Appendix S1, Supporting Information).

After collection individuals were transported to the laboratory in plastic containers (vol. = 1 L) filled with damp, aquatic vegetation, kept within thermally insulated bags (Thermos ® , Rolling Meadows, IL, USA) to reduce temperature variation in the containers. In the laboratory, specimens were maintained in aerated artificial pond water [APW, pH 7·5, acidified using HCl ( ASTM 1980 )], distributed between a number of aquaria (vol. = 5 L, maximum 20 individuals per aquarium) in a 12 : 12 h L/D regime, and fed chironomid larvae ad libitum. Individuals fed, and mated in our treatments, suggesting they were functioning in a normal manner. Aquaria were sealed with Cling-film ® to reduce evaporation and prevent individuals escaping. All the work was conducted in computer-controlled constant temperature rooms. The maximum water temperature fluctuation amongst all aquaria over the acclimation period was 0·6 °C, measured with a maximum–minimum thermometer (Jumbo Thermometer Oregon Scientific© model EM899 ± 0·1 °C Oregon Scientific©, Portland, OR, USA). In an attempt to avoid possible confounding effects of individuals’ recent thermal history, specimens were maintained under identical, constant conditions in the laboratory prior to experiments (e.g. Sokolova & Pörtner 2003 ), which is likely to minimize prior acclimatization effects on individuals. Each species was divided haphazardly into two equal groups, acclimated at 14·5 or 20·5 °C respectively and specimens were maintained in the laboratory for 7 days at these two temperatures before experiments were conducted ( Hoffmann & Watson 1993 Klok & Chown 2003 Terblanche & Chown 2006 ). Temperatures were chosen as being within the range experienced by Deronectes adults in the field (D. T. Bilton, personal observation S. Fenoglio, A. Millán, P. Abellán & D. Sánchez-Fernández, personal communication), and were the same for all species studied to compare relative acclimation abilities of taxa. During acclimation the use of extreme temperatures was avoided, as these could have potentially acted (at least for certain species) as deleterious (pejus) temperatures (see Pörtner 2002 Woods & Harrison 2002 ), suggesting acclimation was probably not stressful, and indeed no mortality occurred during acclimation. After acclimation, individuals from each acclimation-temperature group were further haphazardly assigned to two equal subgroups: used to measure tolerance to heat and cold respectively for individuals of each species kept at 14·5 or 20·5 °C.

Thermal limits and acclimatory ability of Deronectes species

To define species’ thermal biology we employed upper and lower lethal thermal limits [defined as upper thermal limit (UTL) and lower thermal limit (LTL) subsequently], as these proved the most reliable, repeatable measure of thermal tolerance in diving beetles. Lethal limits were favoured amongst the various end-points which could be identified in thermal tolerance experiments, as they showed the lowest variance (see Lutterschmidt & Hutchison 1997a,b Calosi et al. 2008a ) however, the use of sublethal end-points (e.g. paralysis) did not change results.

Experiments commenced at the temperature to which individuals of a given subgroup had been acclimated. Thermal tolerance tests relied on a dynamic method, and were carried out in air in generic, 24-well (diameter = 12 mm, depth = 18 mm) plastic culture plates (Corning Ltd, Sunderland, UK), placed in a computer-controlled water bath (Grant LTC 6–30), heated and cooled, via a ramping program (±1 °C min −1 ) using the Grant Coolwise Software [Grant Instruments (Cambridge) Ltd, Herts, UK]. Experimental ramping rate and equilibration temperature can influence the outcome of thermal tolerance tests ( Terblanche et al. 2007 Chown et al. 2009 ), and selecting an ecologically relevant ramping rate is difficult when comprehensive environmental data are lacking. Consequently, we employed an identical ramping rate, to allow comparisons amongst taxa, and with previous studies ( Lutterschmidt & Hutchison 1997a,b ).

Individuals were introduced, one per well, to a maximum of 12 individuals at any one time, with two investigators working together, for accurate determination of thermal tolerance limits. The actual temperature within each well was measured directly using a calibrated digital thermometer (Omega ® HH11 Omega Engineering Inc., Stamford, CT, USA) equipped with an Omega ® ‘precision fine wire thermocouple’ (type K – dia./ga. 0·010 Teflon). Individuals were removed from their acclimation aquaria, quickly but carefully dried on absorbent paper and placed into a clean, dry, well. To avoid escape, well plates were covered with their plastic lids between addition of individuals. Once the experiment started, the lid was removed to allow full aeration and avoid the build-up of water vapour.

Thermal range (TR) was calculated as the difference between mean UTL at 20·5 °C and LTL at 14·5 °C, as these are likely to represent the most ecologically realistic measures of a species’ tolerance of high and low temperatures (as they follow high and low acclimation temperatures, respectively – see Calosi et al. 2008b – and, overall, species showed higher tolerance to heat and cold when acclimated at these temperatures). Upper and lower thermal tolerance acclimatory abilities (ΔUTL and ΔLTL) were estimated as the absolute difference between the thermal limits (for high or low temperatures respectively) measured at the two acclimation temperatures ( Stillman 2003 ). A positive value for either ΔUTL or ΔLTL indicates a positive ability of a species to increase its mean UTL or mean LTL, following acclimation at a higher or lower temperature. After the experiments individuals were weighed (to ±0·001 g) using a Sartorius 1204 MP2 balance (Sartorius Ltd, Epsom, UK).

Dispersal ability

Obtaining accurate estimates of species’ relative dispersal ability (DA) is difficult (see Bilton, Freeland & Okamura 2001 ), as dispersal itself is an emergent trait, influenced by numerous morphological, physiological and ecological factors ( Rundle, Bilton & Foggo 2007b ). For aerial dispersers such as Deronectes species, however, wing size is an obvious feature which has been suggested to correlate with relative DA (e.g. Rundle et al. 2007a ). This is particularly likely to hold in comparisons of closely related, otherwise similar, taxa, and the use of wing size as a surrogate of relative DA was adopted here. We specifically use wing length/body length ratio, as this is likely to provide a good comparative measure of the relative DA of diving beetle species (see Rundle et al. 2007a ): other possible surrogates of DA (wing length, wing area/body mass ratio) were also explored, and gave the same results as those presented here. Individuals whose wings were to be examined were first photographed intact under a Leica MZ8 stereomicroscope (×50 mag) using a Nikon Coolpix 4500. The right wing was removed from 10 individuals of each species, digested in 10% potassium hydroxide for 15 min to increase flexibility, before being teased open and mounted in lactic acid solution (DL-lactic acid 85% w/w syrup – Sigma Chemical Co., St Louis, MO, USA) on a microscope slide. Disarticulated wings were examined and photographed as described above, and wing length estimated using UTHSCA Image Tool Version 3.0. Body length of each individual was measured from the front of the pronotum to the tip of the elytra (to avoid measurement error due to contraction of head into pronotum) using the same photomicroscopic approach.

Statistical analyses

Species’ body mass differed significantly amongst Deronectes (F13, 712 = 90·282 n = 726 P < 0·0001), and was therefore considered as a covariate in subsequent analyses. The number of individuals studied ranged from 26 in Deronectes angusi Fery and Brancucci to 92 in D. hispanicus Rosenhauer. No significant correlation was found between the number of individuals of each species examined and any physiological, ecological or biogeographical trait (Pearson correlation minimum Z12 = 1·148 P = 0·251), indicating that interspecific differences in sample size did not influence results. Mean body masses (±SE) and the number of individuals tested for each species are given in Appendix S2, Supporting Information.

Differences in mean UTL and mean LTL among species were first analysed separately using an ancova , including body mass as a covariate, whilst differences in mean DA among species were analysed using an ANOVA. We investigated factors influencing variation in latitudinal range extent and position, and both northern and southern range limits using a series of multiple regression models. Akaike’s Information Criteria (AIC) was used to select the best supported models, an approach which reduces problems associated with multiple testing and co-linearity of explanatory variables ( Burnham & Anderson 2002 ). In each analysis, models were constructed using all combinations of experimental variables, and the five best models presented in the results. The single best supported model for each analysis was selected on the basis of the AIC weights, calculated to evaluate the relative likelihood of a model, given the data and the fitted model, scaled to one ( Burnham & Anderson 2002 ). Model selection was performed using both raw data and independent contrasts ( Felsenstein 1985 ) derived from mtDNA based phylogenies ( Ribera et al. 2001 Ribera & Vogler 2004 ). Contrasts were produced using the CRUNCH algorithm of the CAIC software package ( Purvis & Rambaut 1995 ), and regressions of contrast scores forced through the origin ( Garland, Harvey & Ives 1992 ).

Species’ southern range limits were normally distributed, whilst latitudinal range extent and central point were normalized following log10 transformation. In the case of northern range limits, data were normalized following double log10 transformation. Normality in all cases was assessed via Shapiro–Wilks test P > 0·05. All statistical analyses were conducted using JMP IN ® version 5.1, except for multiple regression models, which were run in R v.2.5.1 ( R Development Core Team, 2007 ) and SPSS v.15.0.

I nference A lgorithm for A ncestral S pecies L ocations U sing a P hylogenetic T ree

First, we establish some notation to ease exposition. Consider a fixed, binary phylogenetic tree with n tips and hence n − 1 internal nodes corresponding to ancestral taxa as the one represented in Figure 3. Let d ( i ) be the trait values of the daughter nodes of a particular internal node i ⁠ , and let a ( i ) denote the trait value of the ancestor of i ⁠ . If i is the most recent common ancestor (MRCA) of all taxa, let a ( i ) be the empty set ∅ ⁠ . We observe domains R 1 , … , R n at the tips of the tree and we wish to estimate the ancestral values and variance terms under a bivariate BM process with covariance matrix V ⁠ . Let π ( x ) be a prior distribution for the location of the MRCA in the tree. In the next section, we present a MCMC algorithm for sampling from the joint posterior distribution of the ancestral node value a and the Brownian variance parameters V ⁠ .

Illustration of the four different conditional likelihoods used by the MCMC sampling of the rase algorithm following the notation in the main text. Conditional likelihood for BM using points is represented as f (•) while conditional likelihood that requires integration over a bounded domain is represented by g (R | •). On each panel, the posterior density is being evaluated at ancestor x (marked with a black dot). a) When x is connected by internal branches only ( ⁠ s ⁠ , t 1 and t 2 ⁠ ) and is not the MRCA, its conditional distribution depends on regular point BM with its ancestor a and daughters d1 and d2. b) When x is connected with two terminal branches t 1 and t 2 and one internal branch s ⁠ , its conditional distribution depends on the integration of the bounded domains R 1 and R 2 ⁠ , and regular BM with its ancestor a. c) When x is connected with two internal branches s and t 2 and one terminal branch t 1 ⁠ , its conditional distribution depends on the integration of the bounded domain R 1 and regular BM process with d2 and a. d) When x is the MRCA, its conditional distribution depends on its prior π (x), and regular BM process with d1 and d2.

Illustration of the four different conditional likelihoods used by the MCMC sampling of the rase algorithm following the notation in the main text. Conditional likelihood for BM using points is represented as f (•) while conditional likelihood that requires integration over a bounded domain is represented by g (R | •). On each panel, the posterior density is being evaluated at ancestor x (marked with a black dot). a) When x is connected by internal branches only ( ⁠ s ⁠ , t 1 and t 2 ⁠ ) and is not the MRCA, its conditional distribution depends on regular point BM with its ancestor a and daughters d1 and d2. b) When x is connected with two terminal branches t 1 and t 2 and one internal branch s ⁠ , its conditional distribution depends on the integration of the bounded domains R 1 and R 2 ⁠ , and regular BM with its ancestor a. c) When x is connected with two internal branches s and t 2 and one terminal branch t 1 ⁠ , its conditional distribution depends on the integration of the bounded domain R 1 and regular BM process with d2 and a. d) When x is the MRCA, its conditional distribution depends on its prior π (x), and regular BM process with d1 and d2.


Species-range-size distributions have received remarkably little attention in contrast to species-abundance distributions. However, recognition of the importance of regional scale phenomena for local assemblage structure, and the emergence of ‘macroecology’, have begun to change this situation. A growing number of studies suggests that these distributions are, in general, approximately lognormal, although interpretation is complicated by a variety of factors. Assuming the distribution pattern to be real, it can be viewed in terms of evolutionary and ecological determinants of species occurrences, although their relative significance remains unclear. The form of the distribution has a variety of important consequences, particularly for inventories of faunas and floras and for conservation.


Using a combination of morphology, genetics, ecological niche modelling of current and paleoecological data and physiological experiments we have reconstructed the surprisingly complex evolutionary history of this diving beetle clade in the western Mediterranean. The A. brunneus complex diversified ca. 0.6-0.25 Ma, most likely in the south of the Iberian peninsula after the colonization of A. ramblae from north Morocco. Whilst insular populations (A. ramblae in the Balearic Islands and A. rufulus in Corsica and Sardinia) did not apparently differentiate substantially in either morphology or ecology, continental A. brunneus evolved the most distinctive morphology within the complex, as well as wider tolerance to cold habitats, something that seems to have facilitated range expansion.

From a reduced potential distribution during the LIG, A. brunneus and A. ramblae appear to have expanded their ranges during the last glacial (0.03-0.01 Ma) (A. brunneus to a much wider area), covering most of their LGM potential rages in the western Mediterranean. This expansion was accompanied by a population expansion, as identified through demographic models. However, despite much wider current potential distributions, both species have not occupied areas beyond their LGM potential distribution except for some isolated populations of A. brunneus in France and England. In Sardinia, the Balearic Islands and possibly Tunisia, secondary contact between species of the complex has resulted in introgression, with some specimens showing discordance between mitochondrial haplotypes typical of A. brunneus and nuclear sequences and morphology typical of A. rufulus or A. ramblae respectively.

Our work highlights the complex dynamics of speciation and range expansions within refugia during the last glacial cycle, and the fact that the biota of southern Europe, in addition to being a source of colonisers of formerly glaciated areas in the north, experienced much evolutionary change during this time period. It also highlights the fundamental but often neglected role of North Africa as source of biodiversity in Europe [70�].

Database of Geographic Range of Species - Biology

Unit Eight. The Living Environment

Organisms live as members of populations, groups of individuals that occur together at one place and time. Whether the population is a group of birds, insects, plants, or humans, ecologists can study several key elements of populations and learn more about them.

The term “population” can be defined narrowly or broadly. This flexibility allows us to speak in similar terms of the world’s human population, the population of protists in the gut of an individual termite, or the population of deer that inhabit a forest. Sometimes the boundaries defining a population are sharp, such as the edge of an isolated mountain lake for trout, and sometimes they are fuzzier, such as when individual deer readily move back and forth between two forests separated by a cornfield.

Five aspects of populations are particularly important: population range, which is the area throughout which a population occurs population distribution, which is the pattern of spacing of individuals within that range population size, which is the number of individuals a population contains population density, which is how many individuals share an area and population growth, which describes whether a population is growing or shrinking, and at what rate. We will consider each in turn.

No population, not even one composed of humans, occurs in all habitats throughout the world. Most species, in fact, have relatively limited geographic ranges, and the range of some species is minuscule. The Devil’s Hole pupfish, for example, lives in a single hot water spring in southern Nevada, and the Socorro isopod is known from a single spring system in New Mexico. Figure 35.4 shows a collection of other species that are found in a single population in an isolated habitat. At the other extreme, some species are widely distributed. The common dolphin (Delphinus delphis), for example, is found throughout all of the world’s oceans.

Figure 35.4. Species that occur in only one place.

These species, and many others, are only found in a single population. All are endangered species, and should anything happen to their single habitat, the population, and the species, would go extinct.

Organisms must be adapted for the environment in which they occur. Polar bears are exquisitely adapted to survive the cold of the Arctic, but you won’t find them in the tropical rain forest. Certain prokaryotes can live in the near-boiling waters of Yellowstone’s geysers, but they do not occur in cooler streams nearby. Each population has its own requirements—temperature, humidity, certain types of food, and a host of other factors—that determine where it can live and reproduce and where it can’t. In addition, in places that are otherwise suitable, the presence of predators, competitors, or parasites may prevent a population from occupying an area, a topic we will take up later in this chapter.

Range Expansions and Contractions

Population ranges are not static rather, they change through time. These changes occur for two reasons. In some cases, the environment changes. For example, as the glaciers retreated at the end of the last Ice Age, approximately 10,000 years ago, many North American plant and animal populations expanded northward. At the same time, as climates warmed, species experienced shifts in the elevation at which they could live. The temperatures at higher elevations are cooler than at lower elevations. For example, the range for trees that survive better in colder temperatures shifts farther up a mountain when temperatures increase in an area as shown in figure 35.5.

Figure 35.5. Altitudinal shifts in population ranges in the mountains of southwestern North America.

During the glacial period 15,000 years ago, conditions were cooler than they are now. As the climate has warmed, tree species that require colder temperatures have shifted their range upward in altitude so that they live in the climatic conditions to which they are adapted.

In addition, populations can expand their ranges when they are able to move from inhospitable habitats to suitable, previously unoccupied areas. For example, cattle egrets native to Africa appeared in northern South America some time in the late 1800s. These birds made the nearly 2,000-mile transatlantic crossing, perhaps aided by strong winds. Since then, they have steadily expanded their range and now can be found throughout most of the United States (figure 35.6).

Figure 35.6. Range expansion of the cattle egret.

The cattle egret—so-named because it follows cattle and other hoofed animals, catching any insects or small vertebrates that they disturb—first arrived in South America in the late 1800s.

Since the 1930s, the range expansion of this species has been well- documented, as it has moved westward and up into much of North America, as well as down the western side of the Andes to near the southern tip of South America.

Key Learning Outcome 35.2. A population is a group of individuals of the same species existing together in an area. Its range, the area a population occupies, changes over time.

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Best and Worst Methods for Range Reconstruction

Results from both the range-contraction and extinction simulations suggest macroevolutionary and macroecological patterns, at least in the relatively recent past, can be studied reliably using only a few fossil occurrence sites. Range contraction in both dry and wet biomes can be preserved and detected using a variety of commonly used paleobiological methods. Within the wet biome, we find that great-circle distance and convex-hull methods perform best and give statistically consistent results using only 3+ sites. Latitudinal range also performs well above a threshold of 5+ sites, whereas alpha hull performs relatively poorly, requiring a threshold of 100+ sites.

We find that range contraction is easier to detect in dry biomes than in wet biomes, with three sites sufficient for all four methods. This result at first appears counterintuitive: more preservable area should logically produce a more reliable fossil record, and thus a more reconstructable range. However, when comparing the sizes of the two ranges, the patchiness of the preservable areas in the dry biome may allow for detection of a statistical difference between the larger and smaller range (although this may hold only when fossil “localities” are randomly distributed, as they are here). We make a preliminary attempt to test this hypothesis by sampling 0.5° by 0.5° grid cells from within each of the four ranges shown in Figure 1 (see Supplementary Figs. 6, 7), and comparing mean pairwise distances between the centroids of preservable areas within each grid cell. The results, however, reveal that mean distances are equivalent in both wet and dry biomes (see Supplementary Fig. 8), and thus patchiness in preservable areas may not be driving the observed pattern and some other explanation may be required.

Similar to the range-contraction experiments, our extinction simulations offer justification for many paleoecological and macroevolutionary studies that reconstruct paleo-range sizes and dynamics three of the four studied range-reconstruction methods (maximum great-circle distance, maximum latitudinal range, and convex hull) accurately predicted (>90% of p-values ≤ 0.05) patterns of species’ survival based on range size using only approximately 10 fossil sites in our high-sensitivity scenario. Although there were substantial differences between the low- and high-sensitivity scenarios, the results suggest site thresholds that could potentially guide future studies. That is to say, to achieve at least 90% accuracy assuming a low-sensitivity threshold, 10+ sites are needed for the convex-hull method, and 50+ sites are needed for maximum latitudinal range and maximum great-circle distance. Alpha hulls performed extremely poorly in both scenarios (see Supplementary Table 3). Although the method can perform well when sites are randomly placed anywhere within the range (e.g., see Supplementary Fig. 1), alpha shapes struggle to resolve real range geometries when clustered within linearly-oriented features such as streams, rivers, and lakes. Increasing the α value for hulls can help to reconstruct more realistic geometries, but the resulting polygons become less concave and thus equivalent to convex-hull methods.

Perhaps counterintuitively, all range-reconstruction methods performed better under the high-sensitivity (

50% extinction) scenario over the low-sensitivity (

90% extinction) scenario. One explanation for this may be a lack of statistical power, creating difficulty in recovering the correct extinction pattern under the low-sensitivity threshold. That is to say, few species “survive” in this scenario, making it difficult for the model to determine correctly the difference in range size between those species that go extinct and those that do not. Alternatively (or in addition), the better method performance in the high-sensitivity scenario may be an inadvertent result of the specific species that straddle the low- and high-sensitivity thresholds. At the low-sensitivity threshold, the methods are attempting to recover the “survival” of western species with ranges centered in more arid regions and the “extinction” of smaller-ranged species with ranges centered primarily in Florida and Louisiana. Given the limited distribution of water bodies (and therefore of preservable area) in the western United States, all methods will consistently underestimate range size of the western-distributed species. Conversely, the preservable parts of each range will more closely approximate the perimeter of species distributed in the wetter southeastern United States, and thus a random placement of sites will more closely reconstruct actual range sizes for these species. In other words, the simulations are trying to recover a result wherein a species prone to range underestimation survives and a species prone to range overestimation goes extinct. Although this arrangement of species and thresholds was entirely inadvertent, it illustrates the difficulty of determining the relative sizes of species’ ranges from fossil locality data if these species are distributed in radically different biomes.

Practical Comparisons with the Terrestrial Fossil Record

The number of localities needed for accurate paleo range-size analyses can be compared directly with the number available for various taxa in the fossil record. Such quantification provides a broad sense for how useful the fossil record is as a spatial, rather than a temporal, data set. Given that our range simulations are most applicable to terrestrial (non-volant) vertebrates, we downloaded all Cenozoic tetrapod (mammals, reptiles, and amphibians) occurrence data from the Paleobiology Database ( details given in Supplementary Material 1). We split occurrences by North American Land Mammal Ages (NALMAs) these time bins range in duration from 226,000 years (Rancholabrean:

0.24–0.014 Ma) to 10.2 Myr (Arikareean: 30.8–20.6 Ma) and are probably among the shortest intervals for which multispecies range-size or biogeographic analyses could be undertaken on continental scales (see, e.g., Fraser et al. Reference Fraser, Hassall, Gorelick and Rybczynski 2014). Because many paleobiogeographic analyses of terrestrial faunas have been performed at the genus rather than the species level (see, e.g., Hadly et al. Reference Hadly, Spaeth and Li 2009), we calculated the number of occurrences for both genera and species. The results (Fig. 6) illustrate the percentage of species and genera within NALMAs preserved at each number of sites treated in our simulations (details for individual NALMAs given in Supplementary Fig. 9).

Figure 6 Percentage of Cenozoic fossil terrestrial tetrapod (mammal, reptile, and amphibian) species (left) and genera (right) discovered in increasing numbers of fossil sites (defined as localities with unique paleolatitude and paleolongitude coordinates) in North America. Boxes illustrate the spread of values for all North American Land Mammal Ages (NALMAs), with mean values superimposed as points (see Supplementary Fig. 6 for details of individual NALMAs). Comparing these values with the trajectory of percentage successful simulations for different range-reconstruction methods shown in Figs. 4 and 5 provides a measure of the utility of the spatial fossil record of tetrapods for analysis of range-size dynamics in deep time.

At the species level, results range from poor coverage (only 10% of species in the Rancholabrean are preserved at 3 sites, decreasing to nearly 0% for 5+ sites) to remarkably good coverage (e.g., 30% of species are preserved at 5 sites and

10% of species are preserved at 20 sites in the Wasatchian). For the vast majority of NALMAs (with the exception of the Monroecreekian and Duchesnian), between 10% and 50% of species are preserved at 3+ sites, a number that our simulations suggest is sufficient for detecting changes in range-size dynamics using great-circle distance and convex-hull methods in either wet or dry biomes. At 10+ sites (the threshold suggested for two of our methods for detecting the correct split of victims and survivors in our extinction experiment), most time bins possess between 1% to 10% of species (and

17% in the Wasatchian), which still represents an encouraging return when 100–300 species are typically recorded in each time bin. Only in the Wasatchian are any species (0.23%) preserved at 100+ sites, rendering alpha hull effectively useless for this type of analysis. The results are even more encouraging at the genus level, with 11 out of 22 NALMAs possessing 30–40% of genera represented at 5+ sites. Cumulatively, these percentages suggest that many hundreds of Cenozoic species may be sufficiently well sampled to examine changes in their distribution and geographic range size and to test a broad swath of macroecological and macroevolutionary questions. Logically, these percentages will only increase if coarser time resolution is allowed (see, e.g., Desantis et al. Reference Desantis, Tracy, Koontz, Roseberry and Velasco 2012 Darroch et al. Reference Darroch, Webb, Longrich and Belmaker 2014), although the macroecological and environmental hypotheses invoked to explain any discovered patterns will also be correspondingly broader.

Caveats and Future Directions

Our methodological framework for these simulations makes a number of assumptions, all of which introduce significant caveats to the conclusions we reach concerning the utility and completeness of the spatial fossil record. One major assumption concerns our random placement of simulated fossil sites within ranges. In reality, fossil sites are aggregated and “patchy” on all scales (Plotnick Reference Plotnick 2017), which may have a significant effect on the accuracy of range reconstruction. The other assumptions we make can be organized loosely into “top-down” (climate and tectonic activity) versus “bottom-up” (necrolysis, biostratinomy, and diagenesis) effects that control the quality of the vertebrate fossil record (Noto Reference Noto 2010). With regard to the former, although we accounted for differential preservation and incompleteness of the fossil record by varying site numbers and by restricting occurrences to preservable areas, our most influential assumption is that the entirety of a species’ range is preserved and able to be interrogated by paleontologists. However, weathering, erosion, tectonism, and isostatic/eustatic sea-level change are responsible for constantly removing or burying large quantities of fossil-bearing sedimentary rocks, such that the exposed rock area for any given geological unit typically decreases as you go further back in Earth history (Raup Reference Raup 1976 note, however, this pattern is likely more apparent for terrestrial than marine sediments, see, e.g., Peters and Heim Reference Peters and Heim 2010). Consequently, it seems likely that the accuracy of many range-size reconstruction methods will covary with the surface expression of fossiliferous sediments through time.

In terms of bottom-up effects, another assumption inherent in these simulations is that all species are equally abundant (and thus equally likely to be found as fossils) and have equivalent taphonomic potentials. With regard to the first point (rarity), the relative abundance of a species may have a huge impact on the likelihood of it being discovered as a fossil. Many authors have argued that the preservation of any one species is potentially subject to an “abundance threshold,” such that rare taxa are less likely to be preserved (and/or subsequently discovered) than common species. Modern mammalian species exhibit a bimodal pattern of rarity, with an overabundance of species in both the rarest and most common categories (Yu and Dobson Reference Yu and Dobson 2000) a large proportion of fossil tetrapod species may therefore be underrepresented in the fossil record.

With regard to the second point (taphonomy), a suite of taphonomic processes favors the preservation of some taxa over others. The most obvious of these is size—the bones of animals under 100 kg tend to weather beyond recognition more rapidly than those of larger animals (Behrensmeyer Reference Behrensmeyer 1978 Janis et al. Reference Janis, Scott and Jacobs 1998 Plotnick et al. Reference Plotnick, Smith and Lyons 2016). Thus, in general, smaller species may require more atypical environmental conditions to be reliably preserved and, correspondingly, may tend to have smaller reconstructed ranges. In addition to overall body size, the robustness of skeletal elements (Behrensmeyer et al. Reference Behrensmeyer, Stayton and Chapman 2003, Reference Behrensmeyer, Fursich, Gastaldo, Kidwell, Kosnik, Kowalewski, Plotnick, Rogers and Alroy 2005), selective scavenging (Bickart Reference Bickart 1984 Livingston Reference Livingston 1989), and ambient environmental energy at the time of deposition (i.e., a lake margin vs. a fast-flowing river Kidwell and Flessa Reference Kidwell and Flessa 1996) are all processes that favor the preservation of some species over others, and almost certainly exert a taphonomic overprint on the reconstructed ranges of terrestrial species. With that said, the USGS Waterbodies Dataset likely represents a minimum estimate for the distribution of preservable area within a species’ range. Although tetrapod remains are most often fossilized in streams, rivers, and lakes, they can also be preserved in paleosols, aeolian sands, and within overbank deposits, none of which are incorporated here. Caves and karsted environments in particular make up a significant fraction of fossil sites in the Quarternary (Jass and George Reference Jass and George 2010 Plotnick et al. Reference Plotnick, Kenig and Scott 2015 although see Noto Reference Noto 2010 for a comprehensive list of preservable terrestrial subenvironments). Our modeled preservable area should thus be seen as conservative, and the accuracy of range-size reconstructions may be considerably better than indicated by our simulations (especially in arid environments).

Another potential problem with reconstructing range sizes from fossil locality data involves the issue of postmortem transport. Many of the aquatic settings that favor fossil preservation are also characterized by ambient currents that can move vertebrate remains. Although estimates of the maximum distance skeletal remains can travel are relatively scarce, experimental work suggests bone material can move more than 10 km over approximately 10 years of continual transport without suffering levels of breakage and abrasion that might prevent them from eventually being identified (Hanson Reference Hanson 1980 Aslan and Behrensmeyer Reference Aslan and Behrensmeyer 1996). As a result, much of the terrestrial fossil record may be, to some extent, spatially averaged. In other words, fossils may have moved significant distances from their point of death, although it is not known whether material is commonly transported entirely outside the original range of the species.

We stress that although our simulations are designed to test whether chosen range-reconstruction methods can accurately capture a “snapshot” of a species’ distribution, the vast majority of the fossil record is not only spatially averaged but also time averaged, such that typical accumulations of bone material likely represent ages spanning 10 1 –10 4 years (e.g., Behrensmeyer et al. Reference Behrensmeyer, Kidwell and Gastaldo 2000). Although this property of the fossil record prevents range dynamics from realistically being investigated on timescales less than 10 5 years, time averaging can become advantageous when testing macroecological hypotheses on larger temporal scales (e.g., Darroch et al. Reference Darroch, Webb, Longrich and Belmaker 2014). The taphonomic processes leading to time averaging filter out short-term variations and high-frequency ecological variability (such as seasonal fluctuations), such that local accumulations of fossils represent long-term habitat conditions (Kowalewski et al. Reference Kowalewski, Goodfriend and Flessa 1998 Olszewski Reference Olszewski 1999 Tomašových and Kidwell Reference Tomašových and Kidwell 2010 Saupe et al. Reference Saupe, Hendricks, Portell, Dowsett, Haywood, Hunter and Lieberman 2014). With further refinement, however, our methodological approach could be modified to reproduce dynamic range shifts over a series of time steps and to combine simulated localities from each step. In this fashion, our method could be used to systematically investigate the effect of time averaging in masking (or highlighting) relative range-size changes over longer timescales.

Finally, we suggest that our simulation approach can be adapted to study range dynamics in marine taxa. The advantages of performing such analyses in the marine realm are: (1) More studies have analyzed paleo-range dynamics in the marine than the terrestrial realm (see, e.g., Payne and Finnegan Reference Payne and Finnegan 2007 Harnik et al. Reference Harnik, Simpson and Payne 2012 Saupe et al. Reference Saupe, Qiao, Hendricks, Portell, Hunter, Soberon and Lieberman 2015), and therefore simulations will have broader applicability and explanatory power. (2) In the marine realm, overall preservation potential will be higher in a greater proportion of the species’ range than it is for terrestrial species. This will likely affect the minimum number of occurrences required to accurately reconstruct ranges but will also remove some of the problems associated with the unusual geometries of terrestrial preservable areas (i.e., for alpha hulls). (3) Although preservation potential differs in marine settings, decades of research (e.g., Kidwell and Bosence Reference Kidwell and Bosence 1991 Kowalewski et al. Reference Kowalewski, Carroll, Casazza, Gupta, Hannis-Dal, Hendy, Krause, Labarbera, Lazo, Messina, Puchalski, Rothfus, Salgeback, Stempien, Terry and Tomašových 2003 Kidwell et al. Reference Kidwell, Best and Kaufman 2005 Kosnik et al. Reference Kosnik, Hua, Kaufman and Wust 2009 Darroch Reference Darroch 2012 Olszewski and Kaufman Reference Olszewski and Kaufman 2015) have worked toward calibrating the taphonomic biases associated with different taxa in many of these settings, potentially allowing taphonomic potential to be traced onto regional-scale maps of the world’s coastlines and ocean floor. (4) The number of occurrences for marine species is typically higher than it is for terrestrial species, and perhaps promises even better news for workers studying macroecological and macroevolutionary patterns in range-size dynamics through deep time.

The Center for Tree Science at the Morton Arboretum provided financial support for the lead author. Funding for various phases of the work was provided by the Smithsonian Institution and the National Science Foundation (US).


Morton Arboretum, 4100 Illinois Rte. 53, Lisle, 60532, IL, USA

Smithsonian Tropical Research Institute, Panama City, Panama

Salomón Aguilar & Rolando Pérez

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RC directed plot censuses and inventories, assembled and analyzed the data, and wrote the paper RP and SA did field work identifying and collecting species and supervising plot censuses, and they commented on the manuscript. The author(s) read and approved the final manuscript.

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