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Title
The ecology of cancer prevalence across species: Cancer prevalence is highest in
desert species and high trophic levels
Authors
Stefania E. Kapsetaki1,2,+, Zachary Compton1,2,3, Shawn M. Rupp1,2, Michael M.
Garner4, Elizabeth G. Duke1,5,8, Amy M. Boddy1,6,8, Tara M. Harrison1,5,8, Athena Aktipis*1,7,
Carlo C. Maley*1,2,3
+ corresponding author
* co-senior authors
Author affiliations
¹Arizona Cancer Evolution Center, Arizona State University, Tempe, AZ, USA
² Center for Biocomputing, Security and Society, Biodesign Institute, Arizona State
University, Tempe, AZ, USA
3School of Life Sciences, Arizona State University, Tempe, AZ, USA
4Northwest ZooPath, Monroe, WA 98272, USA
5Department of Clinical Sciences, North Carolina State University, Raleigh, NC, 27607 USA
6Department of Anthropology, University of California Santa Barbara, CA, USA
7Department of Psychology, Arizona State University, Tempe, AZ, USA
8Exotic Species Cancer Research Alliance, North Carolina State University, Raleigh, NC,
27607 USA
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Abstract
The ecology in which species live and evolve likely affects their health and
vulnerability to diseases including cancer. Using 14,267 necropsy records across 244
vertebrate species, we tested if animals in low productivity habitats, with large habitat range,
high body temperature and weight-inferred estimates of metabolic rates, and in high trophic
levels (from lowest to highest: herbivores, invertivores, primary carnivores, and secondary
carnivores) are linked with having increased prevalence of neoplasia. This study found that:
(1) habitat productivity negatively correlated with the prevalence of malignancy and
neoplasia across tissues, and malignancy and neoplasia in gastrointestinal tissues; (2) inferred
metabolic rates negatively correlated with the prevalence of neoplasia; and (3) trophic levels
positively correlated with malignancy and neoplasia prevalence in both mammals and
non-mammals. However, only the correlations with trophic levels remained significant after
Bonferroni corrections for multiple testing. There are several mechanisms that might explain
these findings, including the biomagnification of carcinogens in higher trophic levels, as well
as tradeoffs between cancer suppression versus reproduction and survival in low productivity
environments.
Keywords: biome, cancer prevalence, comparative oncology, habitat, metabolism,
trophic level
Introduction
Very little is currently understood about why some organisms evolve to be more
susceptible to cancer than others, and what tradeoffs constrain the evolution of cancer
suppression. Previous work has looked at the association between cancer prevalence and
intrinsic organismal characteristics such as body size, longevity, placental invasiveness, and
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litter size1–3. Because the ecology of a species determines the selective pressures on that
species, we hypothesised that aspects of a species ecology may force tradeoffs between
cancer suppression and adaptations to the environment. This study investigates if there are
associations between cancer prevalence and habitat productivity, habitat range, temperature-
and weight-inferred estimates of metabolic rates, and trophic levels. For trophic levels, we
classified species by their primary diet as herbivores (plant eaters), invertivores (invertebrate
eaters), primary carnivores (herbivore and invertivore eaters), and secondary carnivores
(eaters of primary carnivores).
The selective pressures in the ecology of a species shape all aspects of that species’
life history, including its susceptibility to cancer. Life history theory predicts that larger and
longer-lived animals may have invested more energy in cancer defence mechanisms, e.g.
more protection from mutations, and less energy in reproduction than smaller and
shorter-lived animals4, and thus have lower cancer prevalence. Cancer researchers, however,
predict that larger, longer-lived organisms should have more cancer, because they have more
cells that could generate cancer over longer lifespans5–8. The observation that larger,
longer-lived species do not seem to get more cancer is called Peto’s Paradox1,2,9. However,
there is a life history trait, litter size, that is positively correlated with cancer prevalence
across 29 mammals, indicating a possible tradeoff between offspring quantity and quality of
somatic maintenance1.
Cancer vulnerability should be associated with ecological
variables
Low productivity habitats may select for species that are more susceptible to cancer
through a variety of mechanisms including tradeoffs between investment in somatic
maintenance versus reproduction and survival in resource limited environments, selection for
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lower metabolisms and meat-eating. Habitat productivity can be quantified by the grams of
carbon in the form of plant glucose produced per square metre per year10,11 (Fig. 1), in other
words the amount of plant materials in the environment. Low productivity may limit an
organism’s ability to withstand physiological stresses. Birds from habitats with fewer plants
have lower resistance to oxidative and non-oxidative stress in response to cadmium,
hydrogen peroxide, paraquat, tunicamycin, and methane methylsulfonate12–15. Additionally, a
comparison of 59 temperate bird species from 17 families versus 69 tropical bird species
from 29 families shows that species living in lower productivity environments, e.g. temperate
birds, have higher metabolic rates than species in higher productivity environments, e.g.
tropical birds12–15. Higher metabolic rates are linked with increased oxidative stress,
immunological stress16, and increased cell proliferation, which can lead to cancer17,18 . Still, a
recent study suggests that high metabolic rates might be cancer protective19. Birds are known
to have higher metabolic rates than mammals20–24, and lower cancer prevalence than
mammals25–28.
[Figure 1]
Lower habitat productivity is also linked with the evolution of meat-eating29. Across
vertebrates, mammals have the highest cancer prevalence, followed by reptiles, birds, and
amphibians25–28. Across different orders of mammals, Carnivora have higher neoplasia
prevalence than Rodentia, Primates, Artiodactyla, and Diprotodontia3,26,30, with the latter
orders consisting mostly of herbivorous species. Carcinogens are known to concentrate as
they move up the food chain, a phenomenon called biomagnification31–39 and there is good
epidemiological evidence in humans that animal-based diets are more carcinogenic40,41.
Higher metabolic rates and larger home ranges (km2) have been also observed in species,
within the order Carnivora, eating a higher percentage of meat42. Therefore, it would be
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expected that higher trophic levels and species with a larger habitat range would have higher
cancer prevalence across vertebrates.
The hypotheses were that: (1) lower habitat productivity; (2) higher trophic levels; (3)
larger habitat range; and (4) higher metabolic rates, are associated with higher malignancy
and neoplasia prevalence across tissues across species. It was also predicted that the effect of
higher trophic levels might be particularly evident in neoplasms of the gastrointestinal tract.
These hypotheses were tested on data collected from 244 vertebrate species from zoos,
aquariums, veterinary hospitals, and online resources.
Methods
Neoplasia Data Collection
We obtained neoplasia prevalence and malignancy prevalence data from 39
institutions on 14,267 individual necropsies across 244 species. Specifically, Mammalia (89
species), Aves (66 species), Reptilia (50 species), Amphibia (24 species), Actinopterygii (13
species), Chondrichthyes (1 species), and Elasmobranchii (1 species) (supplementary data).
All animals in the study had been held in human care, including zoos, research facilities,
rehabilitation centers or were pets that had been submitted to veterinary clinics. The neoplasia
prevalence (including both benign or malignant tumors) or malignancy prevalence was
calculated by dividing the number of necropsies that reported neoplasms, or only
malignancies respectively, by the total number of necropsies for that species. We calculated
tissue-specific neoplasia prevalence or malignancy prevalence by dividing the number of
necropsies showing a neoplasm or only malignancy in that tissue by the total number of
necropsies for that species. Species were analysed for which there were ≥20 necropsies, as
has been done in previous analyses3.
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Data Filtering
All infant records were excluded. Infancy was determined if a record's age was
smaller than or equal to that species’ age of infancy (either the weaning age in the life history
table, or the average of male and female maturity if there was no weaning age). When there
was no recorded age of infancy, the record was considered an infant if it contained any of the
following key words: "infant", "fetus", "juvenile", "immature", "adolescent", "hatchling",
"subadult", "neonate", "placenta", "newborn", "offspring", "fledgling", "snakelet", "brood",
"fry", "fingerling". Records for which there was no information about the location of the
neoplasia and free-ranging animal records were excluded from this study.
For reference, worldwide, adult humans have a cancer rate of approximately 0.18
(https://ourworldindata.org/grapher/share-of-deaths-by-cause), but there is not an estimate of
benign neoplasia prevalence in humans, so humans were not included in our database.
Tissue specific diagnoses
Gastrointestinal malignancy or neoplasia prevalence of each species consists of the
malignancy or neoplasia prevalence, respectively, of the oral cavity, esophagus, stomach,
gallbladder, bile duct, liver, pancreas, duodenum, small intestine, and colon. A case of a
midabdominal mass adhered to a ferret’s stomach and small bowel, and a mass in the
pancreatic region of another ferret were included in the gastrointestinal data. The remaining
abdominal neoplasms that did not classify as neoplasms of the esophagus, stomach,
gallbladder, bile duct, liver, pancreas, duodenum, small intestine, or colon, were included in
the non-gastrointestinal neoplasia data.
Specifically, non-gastrointestinal malignancy (or neoplasia) prevalence of each
species consists of the malignancy (or neoplasia) prevalence in non-gastrointestinal locations
or cell lineage. These locations or cell lineages are the adrenal cortex, adrenal medulla, blood,
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bone, bone marrow, brain, carotid body, cartilage, dendritic cell, fat, fibrous connective
tissue, glandular tissue, glial cell, hair follicle, heart, iris, abdomen, kidney, larynx, lung,
lymph nodes, mammary, mast cell, meninges, mesothelium, myxomatous tissue, nerve cell,
neuroendocrine tissue, neuroepithelial tissue, nose, notochord, ovary, oviduct, parathyroid
gland, peripheral nerve sheath, pigment cell, pituitary gland, neural crest, prostate, pupil,
skin, smooth muscle, spinal cord, spleen, striated muscle, synovium, testis, thyroid, trachea,
transitional epithelium, uterus, vulva, and when the cancer was considered widespread
without an indication of where the primary tumor originated.
Habitat data collection
To determine the habitat of the 244 species for which there were cancer records, a
search was performed predominantly using the Animal Diversity Web
(https://animaldiversity.org/) and the International Union for Conservation of Nature (IUCN)
red list of threatened species (https://www.iucnredlist.org/). References for the habitat(s) of
each species are included in the supplementary data.
Values of habitat productivity for freshwater, tropical, marine, temperate, and desert
habitats from previous studies were found43,44 (Table 1A). The productivity of freshwater
habitats was classified as the average productivity of swamps, marshes, and river estuaries,
whereas the productivity of marine habitats was classified as the average productivity of coral
reefs, algal beds, and open oceans (Table 1A). The average habitat productivity of a species
was the productivity of its habitat if that species lived in a single habitat, and the average
productivity of its habitats if it lived in two or more habitats (Table 1B). Habitats were
ordered from lowest to highest productivity (Table 1).
To find how many habitats each species lives in, we used the habitat information
(supplementary data; Table 1), and categorised each species by their number of habitats (one,
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two or three or more habitats). Few species lived in four or five different habitats, so these
these species were classified as living in three or more habitats.
Diet data collection
When considering the effect of diet on neoplasia prevalence, there are potentially two
different effects: the diet that the species has evolved to eat and the diet those animals are
actually fed under human care as pets or in zoological institutions. The dietary data for the
animals in human care are not available for this study.
To find the natural diet of each of the 244 species in our database in the wild, a search
was performed in the Animal Diversity Web (https://animaldiversity.org/), the IUCN red list
of threatened species (https://www.iucnredlist.org/), and other resources45–50. Each ecosystem
can have many trophic levels. For example, marine ecosystems usually have more trophic
levels than terrestrial ecosystems51–53. Each organism may eat many types of food creating
complex interactions that appear as a food web54. In order to test hypotheses in all
ecosystems, based on the trophic pyramid55, and the food pyramid of low to high cancer
incidence in humans40,41, each species was classified according to their primary diet into four
trophic levels: herbivores, invertivores, primary carnivores, and secondary carnivores.
Decomposers, such as certain bacteria and fungi, were not included in these analyses, because
there are not malignancy or neoplasia prevalence data for these species. In these
classifications, herbivores include species eating primarily plants (fruit, seeds, bark, leaves,
flowers, roots) and/or fungi. Invertivores include species eating primarily invertebrate
animals such as arthropods, cnidarians, echinoderms, comb jellies, annelids, and/or molluscs.
Eating primarily vertebrates automatically assigned a species as a primary carnivore. To
verify this animals were evaluated if their prey were primary carnivores. If the prey were also
primary carnivores, these species (i.e. the predators of primary carnivores) were classified as
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secondary carnivores (Supplementary Data). The diet and habitat of a species may change
over time. In this paper, species were analysed according to their primary diet in the wild as
reported in recent years. If data on the diet of juveniles in the wild were available, we
classified these species according to the diet of adults in those species.
Inferred metabolic rates
To find the metabolic rate of each species we used the following formula M –1/4 • e
–E/kT. This formula is part of a more extended formula for metabolic rates “B = bo • M –1/4 • e
–E/kT” (where B = mass-specific metabolic rate; bo = a coefficient independent of body size
and temperature; M = adult mass in grams, E = 0.65 eV, k = 8.62 • 10–5 eVK–1, T = body
temperature in Kelvin)56–59. Since there is not a known value for bo in most species, we
measured M –1/4 • e –E/kT for every species. Adult mass data was obtained for the species where
their weight was known60,61 (AnAge Database
https://genomics.senescence.info/species/index.html). The average body temperatures were
found for 81 out of the 244 species in our study. Most of these average body temperatures
were from Moreira et al.’s recent study62 on 1,721 species of land vertebrates, including
amphibians, mammals, reptiles, and birds collected from the lab, field, and an ‘unclear’
location. Body temperature for the 15 fish species was unknown and not included in any
analyses. Average body temperature of marine mammals was generated from van
Wijngaarden et al, Mense et al., and McCafferty et al.63–65. The mass-specific metabolic rate
was estimated as the unweighted average of body temperature. In order to use these body
temperature values in “M –1/4 • e –E/kT”, the body temperatures was converted from Celsius to
Kelvin.
Statistical Analyses
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All were performed analyses in R version 4.0.566 using the R packages CAPER67,
phytools68, geiger69, tidyverse70, and powerAnalysis (https://github.com/cran/powerAnalysis),
and performed Phylogenetic Generalised Least Squares (PGLS) regressions to take into
account the phylogenetic non-independence among species. To perform a PGLS regression, a
phylogenetic tree (phyl file) was made using the NCBI Tree creator
(https://www.ncbi.nlm.nih.gov/Taxonomy/CommonTree/wwwcmt.cgi). PGLS analyses have
also been previously used to compare neoplasia prevalence and life history variables across
mammals1. In the analyses where the dependent variable was malignancy or neoplasia
prevalence, we weighted the analyses by 1/(square root of the number of necropsies per
species) (from Revell 68). A Bonferroni corrections was performed to adjust for multiple
testing.
The trophic level, average habitat, minimum habitat productivity, and number of
habitats were used as categorical variables. Whereas the malignancy prevalence, neoplasia
prevalence, average body temperature, “M –1/4 • e –E/kT”, and average habitat productivity
(Table 1), were used as numerical variables. The average body temperatures were
transformed to the power of 2, and the value of “M –1/4 • e –E/kT” to the power of 0.1 to
normalise the distribution of data.
Results
This study found that species from lower productivity habitats have more
malignancies, more tumors, more gastrointestinal cancer and gastrointestinal neoplasms in
general (Table 2). These associations were examined in two ways. First, using the minimum
productivity habitat occupied by a species and controlling for the species position in the
trophic pyramid (Fig. 2). Second, using the average productivity of the habitat(s) a species
occupies and controlling for the higher levels of neoplasms in mammals25 (Fig. 3). These
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correlations, however, between habitat productivity and malignancy or neoplasia prevalence
were not significant after applying Bonferroni corrections for multiple testing (Table 2).
[Figure 2]
[Figure 3]
There were no significant associations overall between habitat range and malignancy
prevalence across tissues or neoplasia prevalence across tissues without controlling for
increased cancer in mammals, and after applying a Bonferroni correction (Supp. Fig. 2).
There is evidence of a trend (P-value = 0.01), however, in species living in three or more
habitats having higher neoplasia prevalence than species living in one habitat (Supp. Fig. 2B).
Higher trophic levels tend to have higher malignancy and neoplasia prevalence across
tissues (Fig. 4), both within mammals and within non-mammals (Fig. 5 & Supp. Fig. 1; Table
2). Also, in mammals, higher trophic levels have higher non-gastrointestinal malignancy and
neoplasia prevalence. Gastrointestinal neoplasia prevalence in mammals seems to largely be
determined by phylogenetic closeness (Lambda = 0.99), potentially between the species in
the order Carnivora relative to the other mammalian orders, and there was little evidence that
gastrointestinal neoplasia prevalence was influenced by trophic level (Fig. 5 & Supp. Fig. 1;
Table 2). In contrast, in non-mammals, higher trophic levels had higher non-gastrointestinal
neoplasia prevalence as well as higher gastrointestinal malignancy and neoplasia prevalence
(Fig. 5 & Supp. Fig. 1; Table 2). Most of these relationships remained statistically significant
after a Bonferonni multiple testing correction (Table 2).
[Figure 4]
[Figure 5]
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Species with lower inferred metabolic rates [measured as (M –1/4 • e –E/kT )0.1, where M
= adult weight, and T = average body temperature] had higher neoplasia prevalence (but not
higher malignancy prevalence) across tissues when controlling for increased cancer in
mammals (Fig. 6; Table 2, and Supp. Fig. 3), however, this association was not statistically
significant after applying Bonferroni corrections for multiple testing (Table 2).
[Figure 6]
Average body temperature was not significantly correlated with malignancy or
neoplasia prevalence across tissues without controlling for increased cancer in mammals
(Supp. Fig. 4). Also, inferred metabolic rates (M –1/4 • e –E/kT )0.1 were lower in primary
carnivores than invertivores (Supp. Fig. 5).
The majority of species in lower trophic levels in our database lived in higher
productivity habitats, whereas the majority of species in higher trophic levels lived in lower
productivity habitats (Supp. Fig. 7). When controlled for trophic level, there was still
evidence that low productivity habitats are still associated with more neoplasia (P=0.02) and
malignancy (P=0.04), though neither was significant after Bonferroni correction for multiple
testing.
Discussion
Cancer prevalence varies across species, for mostly unknown reasons3,25,26,30. This
study hypothesised that ecological variables would explain some of this variance in cancer
prevalence. Evidence was found that malignancy and neoplasia prevalence are higher in
species from lower productivity habitats (Table 2). There was also evidence that neoplasia
prevalence is higher in species with lower inferred metabolic rates (Table 2).
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The strongest effect found was that malignancy and neoplasia prevalence are higher in
higher trophic levels, particularly in secondary carnivores (Figs. 4 & 5, Table 2). A recent
study in mammals on a different dataset showed an association between cancer mortality risk
and animal content in a species’ diet3. Here this association was expanded across vertebrates,
and discovered that this was partly driven by higher levels of tumors, both benign and
malignant, in vertebrate species that are secondary carnivores. It is possible that some of the
association between trophic level and cancer prevalence was due to biomagnification of
non-degradable toxic chemicals, such as heavy metals, pollutants, microplastics, and
pesticides71, causing detrimental health effects as their concentration increases in animal
tissue at every step higher in the trophic pyramid31–37,39. This study also discovered strikingly
low rates of neoplasia prevalence in species that mainly feed on insects or other invertebrates
(Figs. 4 & 5, Supp. Fig. 1). Trophic levels explain much of the variation in malignancy and
neoplasia prevalence across tissues across species in our database (R² as high as 0.31; Table
2).
Caution should be used when applying observations across species to within species;
however, these observations of increased cancer prevalence at higher trophic levels were
consistent with many studies in humans showing an association between cancer and
consumption of animal products72–77. In humans, consuming products low in photosynthates,
such as red meat, animal fat, and/or processed food, leads to the production of N-nitroso
compounds, secondary bile acids, hydrogen sulfide, and reactive oxygen species41,78. These
chemicals increase DNA damage and substitution rates, reduce mucus production, degrade
the extracellular matrix, cause inflammation and tumor proliferation40,41,78–82. Products
abundant in photosynthates, such as fruit and vegetables83–90, produce more vitamins41,
antioxidants41, flavonoids41,91, and glucosinolates41, which reduce DNA damage and
inflammation41, degrade toxins, limit pathogen growth41, tighten cellular junctions41, maintain
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the barrier between intestinal cells41, and inhibit tumor growth40,41,79,92. Perhaps it is no
coincidence that the majority of already known cancer-resistant mammals, such as
naked-mole rats, blind mole rats, and elephants, are herbivores93–95. Despite these
observations, the story is likely to be more complex. It is likely that species with meat-based
diets have evolved specialised adaptations to consuming meat that may mitigate the negative
effects of carnivory.
Invertivores in most of these analyses had the lowest median cancer prevalence and
neoplasia prevalence of all trophic levels (Fig. 4 & 5; Supp. Fig. 1). Previous studies have
shown that insectivorous fish have higher mean concentrations of total mercury in their
tissues than herbivorous fish96. The majority of herbivores in our database, however, were
endotherms, whereas the majority of invertivores in our database were ectotherms.
Ectotherms are known to have lower concentrations of persistent organic pollutants in their
body than endotherms, because endotherms of the same size consume relatively more food as
a means of maintaining body temperature. Therefore, this lower concentration of persistent
organic pollutants in the body of ectotherms97,98, in combination with the fact that there are
more ectotherms than endotherms in our invertivore trophic level, may explain the lower
cancer prevalence in invertivores than herbivores (Fig. 4 & 5; Supp. Fig. 1). This study
found, however, that cancer prevalence was not significantly different between endotherms
and ectotherms when controlling for trophic levels (multivariate PGLS, P-value = 0.46;
Lambda: 0.42; R²: 0.05). This lower cancer prevalence in invertivores may also be due to the
presence of insect chemicals, such as chitin, in their diet. Chitin is a polymer of
N-acetylglucosamine, a derivative of glucose, and is the primary substance of the exoskeleton
of almost all invertebrates and most fungi. There is evidence that chitin and many other
insect-derived compounds have antioxidant, anti-inflammatory, and anti-cancerous
properties99–101.
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Species at high trophic levels tend to live in many habitats42,102,103 (Supp. Fig. 6) and in
low productivity habitats in particular (Supp. Fig. 7). This larger habitat range of carnivores
versus herbivores, may be explained by unpredictability in the abundance and wide dispersal
of their prey. Carnivores have relatively more mobile prey than herbivores, and this possibly
led to adaptations related to movement in a broader habitat range in carnivores than
herbivores 104. Species living in larger habitat ranges, however, do not have higher
malignancy or neoplasia prevalence across tissues than species living in one habitat (Table 2).
DNA base pair substitution (mutation where a pair of bases changes in the DNA) rate
is sometimes modelled as a function of the inferred metabolic rate56–59,105, and so metabolic
rate may be associated with cancer due to its relationship to mutation rate. Additionally,
higher metabolic rates are characteristic of higher trophic levels42,106,107. Still, birds have
higher metabolic rates than mammals20–24, and lower cancer prevalence than mammals25–28.
This study found that inferred metabolic rates did not differ significantly between trophic
levels (Supp. Fig. 5), and inferred metabolic rates showed a negative correlation with
neoplasia prevalence across tissues (Figure 6; P-value = 0.006) which was not significant
after applying Bonferroni corrections (Table 2). This discrepancy with Muñoz-Garcia et al.’s
findings may be due to the fact that this study measured inferred metabolic rates (M –1/4 • e
–E/kT from the metabolic theory of ecology, where M is adult weight and T is their body
temperature)56,57 across the tree of life, whereas Muñoz-Garcia et al.42,106,107 measured the
basal metabolic rate or rate of oxygen consumption per species only within the order
Carnivora.
Why haven’t species at lower productivity habitats, higher trophic levels, or with
lower metabolic rates evolved better cancer defences than species in higher productivity
habitats, lower trophic levels, or higher metabolic rates? Resource scarcity in lower
productivity habitats and bioaccumulated toxins in higher trophic levels may limit adaptive
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responses to cancer defences. Imagine a damaged piece of DNA in two different
environments: tropics and deserts. In the tropics, the resources for fixing/maintaining108,109 the
broken DNA are more likely to be available than in the deserts. Some tropical birds with
slower life histories are more resistant to oxidative stress than temperate birds with faster life
histories12–14,110. Tropical predators may also have more resources for detoxifying the
bioaccumulated toxins. In other words, tropical birds may invest more resources in cellular
maintenance, such as cancer protection, than temperate species12,111. Bioaccumulated natural
(and artificial) toxins in high trophic levels, and loss of heat, waste, and dead matter in every
trophic level, can reduce population size in high trophic levels and thus limit adaptive
responses to future environmental stressors112. Therefore, even though damages in the DNA
can potentially happen in many different environments and trophic levels, their chance of
repair may be higher in species of higher productivity environments and lower trophic levels.
Limitations & future directions
There were 244 species in this database, however, some data were missing or were
not precise for many of those species. For example, the adult weight and body temperatures
of 188 and 81 species, respectively, were available. This limits statistical power for
identifying relationships between variables and cancer prevalence. Some taxa, such as birds,
have too few species per habitat or trophic level (e.g. only 8 primary carnivores and only 2
secondary carnivores within birds) to identify associations between habitat or trophic level
and cancer prevalence just within that taxon. Another caveat in our study is that precise data
on the productivity of the specific habitats inhabited by each species does not exist, nor are
there data on the amount of a species range that is covered by each habitat. The number of
grams of carbon produced per square metre per year is an average over the whole year based
on the habitat of each species43,44 (Table 1A & 1B; supplementary data). Furthermore,
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although the outcomes of malignancy and neoplasia prevalence in this study are based on a
minimum of 20 necropsies per species, future studies with additional individuals may find
other relationships than are currently available in this dataset.
The estimates of metabolism are inferred rather than directly measured (Methods).
The bo value, a coefficient independent of body size and temperature in the metabolic rate
equation, is unknown for most of these species (Supplementary Data). Ideally, future studies
will provide more accurate measurements of each species’ average metabolic rate and the
geographical coordinates of wild animals.
This database is composed of animal populations housed under human care in zoos,
aquariums, and as pets submitted to veterinary practices (Supplementary Data). In a study
across over 50 mammalian species, all Carnivora lived longer in managed populations than in
free-ranging wildlife populations, whereas not all species from mostly herbivorous orders had
an extension of lifespan in human managed populations113. Cancer is a disease of aging, and
this enhanced longevity in species at higher trophic levels may expose them to developing
more cancer. So the fact that high trophic levels have higher cancer prevalence than low
trophic levels in these data suggests that this may not be as strong an effect in wild
populations. There may be mismatches in the food given to each animal in different zoos due
to the different location and availability of food in each zoo; however, some of the institutions
in this study that belong to the Association of Zoos and Aquariums (AZA) accredited
institutions follow similar feeding guidelines. There may also be mismatches in the habitat
and diet of animals from various institutions in our database, compared to animals living in
the wild, that may affect their susceptibility to cancer. For example, in AZA accredited
institutions (feeding guidelines: https://www.aza.org/animal-care-manuals and
https://nagonline.net/tag/animal-care-manuals/), lions eat predominantly raw meat or cat
food; European polecats eat primarily cat food supplemented with fruit and vegetables or
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Mazuri® Ferret Diet; meerkats eat primarily cat food, Royal Canine® Vet Diet, Natural
Balance® Carnivore 10% food, or fruit and vegetables; and North American river otters eat
primarily cat food, horsemeat, or beef-based diets supplemented with vegetables, minerals,
vitamins, and raw fish. Whereas in the wild, meerkats are invertivores and North American
river otters are secondary carnivores (Supplementary Data). The diets that were fed to the
specific individuals in our database are not available. Also, species in human care may
experience different climate conditions than their environment in the wild. For example,
species may live in a desert-like environment under human care, but in a temperate
environment in the wild. Future studies that track the diet of individuals worldwide in zoos,
aquariums and veterinary hospitals, may determine if diets and habitats are better or worse
predictors of cancer prevalence compared to the experienced habitats and diets of the
individual animals being studied. This study was based on the primary diet and habitat the
species occupied in the wild which may have shaped the evolution of each species, its cancer
prevalence and cancer defences over thousands of years.
We tested the effect of environmental factors such as habitat productivity, habitat
range, and diet, on malignancy and neoplasia prevalence across the tree of life. There may be
additional factors, however, within these or neighboring habitats that affect malignancy and
neoplasia prevalence across species, that were not tested. The use of hormonal contraceptives
in human managed populations may contribute to higher cancer prevalence in some of our
species, particularly in females. When testing for a sex bias in cancer risk in the order
Carnivora, however, Vincze et al.3did not find any significant sex bias in cancer risk,
indicating that hormonal contraceptives do not explain the increased cancer prevalence in the
populations of Carnivora under human care that were examined in that study. A study from
1977 found that the majority of neoplasms in mammals were found in the lung25, whereas the
majority of neoplasms in birds and reptiles were lymphosarcomas25. Dogs exposed to passive
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smoking and air pollution may develop cancer114,115. Numerous other carcinogens in the
environment and diet are known to affect malignancy and neoplasia prevalence in several
species38,39,116–118, so it would be useful to know the impact of these carcinogens on
malignancy and neoplasia across species. More environmental toxins may be found in the
tissues of animals living in lower productivity habitats and/or in higher levels of the food
pyramid32,39,119, which could explain the observed higher malignancy and neoplasia
prevalence in lower productivity habitats (Fig. 2 & 3) as well as the higher cancer prevalence
across tissues in higher trophic levels (Fig. 4 & 5; Supp. Fig. 1; Table 2).
We expected what type of food different species would predominantly consume rather
than what they occasionally would consume to affect their evolution and development the
most. Therefore, we classified species in this database according to their primary diet.
Malignancy and neoplasia prevalence, however, may also depend on whether species are
monotrophs or polytrophs (e.g., generalists). Therefore, malignancy and neoplasia prevalence
could be compared with the diversity of food that an organism eats, although we do not have
data on the whole range of foods that every species eats or a clear biological hypothesis about
which group, monotrophs or polytrophs, may have higher cancer prevalence.
Future studies can test whether the correlations between higher trophic levels and
cancer prevalence (Fig. 4 & 5; Supp. Fig. 1; Table 2) are also present when comparing
carnivorous physiological traits and cancer prevalence. In humans, the risk of developing
breast and colorectal cancer is higher in people that mainly work at night49,120. Nocturnal and
crepuscular species may have higher cancer prevalence than diurnal species, and nocturnal
and crepuscular living is more typical of carnivores121–123. It may be that this association
between carnivory and nocturnal or crepuscular living is partially responsible for the
relationship we found between higher trophic levels and higher cancer prevalence (Fig. 4 &
5; Supp. Fig. 1; Table 2). The association between carnivores and cancer prevalence may also
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depend on the microbiome124, though little is currently known about the microbiome of most
of the species in this dataset.
Conclusion
To the extent that cancer has been an important selective factor on the survival and
reproduction of a species, it would be expected that natural selection blunts any associations
between ecological factors and cancer prevalence. Tradeoffs in responding to different
selective pressures in an organism’s ecology, however, may constrain the ability of a species
to effectively suppress the development of neoplasia. This study found that malignancy and
neoplasia prevalence across tissues was correlated with trophic levels across vertebrates, even
when applying Bonferroni corrections for multiple testing (Table 2). Future studies should
distinguish whether this pattern exists due to bioaccumulation of toxins in high trophic levels,
due to the consumption of meat per se, or both. An additional hypothesis for potential testing
would be the effect of insect-derived chemicals, such as chitin, on the low cancer incidence in
invertivores. Species with high inferred metabolic rates and in high productivity habitats have
lower neoplasia prevalence but these relationships are not significant after Bonferroni
corrections, and the reasons for those potential relationships remain unknown. These results
suggest that species with higher metabolic rates and in higher productivity habitats might
have more somatic resources with which to invest in cancer defences. A complete theory of
metabolic ecology would help distinguish between these possible mechanisms and this
knowledge could possibly be used to decrease the burden of cancer for animals managed in
human care, free ranging animals, and humans.
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Figure legends
Figure 1. Global distribution of photosynthesis (habitat productivity in grams of
carbon produced per square metre per year). The angle of sun rays hitting Earth, the
abundance of water, and carbon dioxide, determine the global distribution of photosynthates,
oxygen and glucose, through photosynthesis10,125,126. The green chromophore chlorophyll ais
involved in this chemical reaction inside the chloroplasts of plants and phytoplankton. We
show the global distribution of photosynthesis as concentration gradients of chlorophyll a.
Dark red and blue-green are areas of high photosynthetic activity in the ocean and on land,
respectively. Many grams of carbon in the form of glucose per square metre per year are
produced in the tropics [2000 gC/(m² • year)] and freshwaters [2150 gC/(m² • year)], where
the photosynthetic reactants sunlight, water, and carbon dioxide, are abundant127,128. Fewer
grams of carbon in the form of glucose per square metre per year are produced in deserts [3
gC/(m² • year)], where the photosynthetic reactant water is minimal10,125–128. Reproduced from
a satellite image of NASA SeaWiFS Project, Goddard Space Flight Center and ORBIMAGE
and https://images.slideplayer.com/25/8083427/slides/slide_27.jpg. Copyright: Wikimedia
Commons. We placed the NASA image on a sphere using www.maptoglobe.com to illustrate
the part of Earth’s curve closest to the sun, i.e. the tropics.
Figure 2. Malignancy and neoplasia prevalence across habitats. Malignancies (A)
and neoplasias (B) are more prevalent in species that live in deserts. Productivity is measured
in grams of carbon produced per square meter per year. Each species is classified according
to its minimum productivity habitat among the habitats in its range. Habitats are ordered from
left to right according to their productivity, from high to low productivity. Each dot is a
species, and each category on the x axis consists of ≥10 species. Colours show the trophic
levels of each species, and the shapes indicate the major clades. The number of species (N) in
a category is noted above each category. The horizontal black line in each category shows the
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median malignancy or neoplasia prevalence in that category. P-values between categories that
are significantly different are shown. All P-values are provided in the supplementary data and
Table 2.
Figure 3. Gastrointestinal malignancy (A) and neoplasia prevalence (B) are
higher in species living in habitats which on average have low productivity. Habitat
productivity is measured in grams of carbon produced per square metre per year. Each dot is
a species. See Table 2 for statistical analyses.
Figure 4. Malignancy (A) and neoplasia (B) prevalences are significantly higher
in higher trophic levels. Each dot is a species, and each trophic level consists of ≥10 species.
The number (N) of species in each trophic level is indicated above each trophic level. The
horizontal black line in each category shows the median malignancy or neoplasia prevalence
in that category. P-values are provided between categories that are significantly different, and
for all comparisons in the supplementary data and Table 2.
Figure 5. Malignancy and neoplasia prevalence in relation to trophic levels in
mammals versus non-mammals. Panels A-F show malignancy prevalence as a function of
trophic levels in mammals versus non-mammals, for all tissues (Panels A & B),
gastrointestinal malignancies (Panels C & D), and non-gastrointestinal malignancies (Panels
E & F). Malignancy prevalence is always higher in higher trophic levels except for
gastrointestinal malignancy prevalence in mammals (Panel C) and non-gastrointestinal
malignancy prevalence in non-mammals (Panel F). Each dot is a species, and each trophic
level consists of ≥10 species. The number (N) of species in each trophic level is indicated
above each trophic level. The horizontal black line in each category shows the median
malignancy prevalence in that category. P-values are provided between categories that are
significantly different, and for all comparisons in the supplementary data and Table 2.
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Figure 6. Neoplasia prevalence across tissues is higher in species with lower
inferred metabolic rates. The formula M –1/4 • e –E/kT multiplied by bo is a measure of
metabolic rate for each species. We do not know the bo value [bo = a coefficient independent
of body size and temperature from 58] for each species, so we measured the M –1/4 • e –E/kT
values for each species (where M = mass, E = 0.65 eV, k = 8.62 • 10–5 eVK–1, and T =
temperature in Kelvin)56. Each dot is a species.
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Tables
Table 1A. Net primary production (in grams of carbon produced per square meter per year) from previous measures of biomass
productivity 43,44.
Producers
Net biomass productivity
(gC/(m² • year)) = the mass of
photosynthate
Natural climate zones of the
animals
Average net biomass productivity
(gC/(m² • year)) in each climate
zone
Swamps and marshes
2500
Freshwater (average productivity of
swamps and marshes & river
estuaries)
2150
Tropical forests
2000
Tropical
2000
Coral reefs
2000
Marine (average productivity of coral
reefs, algal beds, and open oceans)
1375
Algal beds
2000
River estuaries
1800
Temperate forests
1250
Temperate
1250
Tundra
140
Open ocean
125
Deserts
3
Desert
3
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Table 1B. Average net biomass productivity, measured in grams of carbon produced per square meter per year, of the habitats in our
database. Rows with multiple habitats are averaged values of these habitats from Table 1A.
Habitats of the species
Average net biomass productivity
Freshwater
2150
Tropical & Freshwater
2075
Tropical
2000
Tropical & Freshwater & Marine
1841
Temperate & Tropical & Freshwater
1800
Marine & Freshwater
1762
Temperate & Freshwater
1700
Temperate & Tropical & Freshwater & Marine
1693.75
Tropical & Marine
1687
Temperate & Tropical
1625
Temperate & Freshwater & Marine
1591
Marine
1375
Temperate
1250
Temperate & Desert & Tropical
1084
Tropical & Desert
1001
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Temperate & Desert
626
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Table 2. Phylogenetic generalised least squares (PGLS) regression summary results of the figures in the main text and in the
supplementary materials. High values of lambda indicate that the signals can be mainly explained by common ancestry among species. With 26
tests, the most conservative Bonferroni correction requires a P-value < 0.0019 in order to be considered statistically significant, which are
highlighted with a * next to the P-value. In the 1st P-value column the P-value are reported for the first variable (which we call variable A in the
multivariate analysis), in the 2nd P-value column the P-value is reported for variable B, in the F-statistics column we report the F-statistics of
variable A, and in the “Type of Association” column we report whether there is a positive (+) or negative (-) association between the
independent variable A and neoplasm or malignancy prevalence. If the independent variable is categorical, the sign (+ or –) of the majority of
between-group comparisons is reported. †indicates that the R² value was not available given that the categorical independent variable only
included two groups (herbivores and primary carnivores).
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Indepe-
ndent
variable(s)
Tissues
Dependent
variable
R²
F-statistic and degrees
of freedom (DF)
Lambda
Type of
Association
P-value of
variable A
P-value
of
variable
B
Minimum
habitat
productivity
+ trophic
levels
(Figure 2)
all
Malignancy
prevalence
0.2
2.71 on 3 and 213 DF
0.33
+
0.04
0.0006*
Neoplasia
prevalence
0.19
3.05 on 3 and 213 DF
0.34
+
0.02
0.001*
Average
habitat
productivity
(as
numerical
variable) +
“mammals
versus non-
mammals”
all
Malignancy
prevalence
0.004
2.84 on 1 and 226 DF
0.47
–
0.09
0.12
Neoplasia
prevalence
0.007
2.82 on 1 and 226 DF
0.42
–
0.09
0.04
gastrointestinal
(Figure 3)
Malignancy
prevalence
0.02
7.12 on 1 and 149 DF
0.99
–
0.008
0.63
Neoplasia
prevalence
0.03
6.01 on 1 and 149 DF
0.99
–
0.01
0.34
non-gastrointestinal
Malignancy
prevalence
0.004
0.71 on 1 and 198 DF
0.40
–
0.39
0.17
Neoplasia
prevalence
0.006
1.27 on 1 and 198 DF
0.31
–
0.26
0.10
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Trophic
levels
(Figure 4)
all
Malignancy
prevalence
0.31
5.80 on 3 and 225 DF
0.44
+
0.0008*
NA
Neoplasia
prevalence
0.3
5.19 on 3 and 225 DF
0.43
+
0.001*
NA
Trophic
levels in
mammals
(Figure 5 &
Supp. Fig. 1)
all
Malignancy
prevalence
NA†
18.78 on 1 and 67 DF
0.30
+
0.0001*
NA
Neoplasia
prevalence
NA†
12.59 on 1 and 67 DF
0.34
+
0.0007*
NA
gastrointestinal
Malignancy
prevalence
NA†
2.27 on 1 and 46 DF
0.99
+
0.13
NA
Neoplasia
prevalence
NA†
1.65 on 1 and 46 DF
0.99
+
0.20
NA
non-gastrointestinal
Malignancy
prevalence
NA†
26.48 on 1 and 65 DF
0.01
+
< 0.0001*
NA
Neoplasia
prevalence
NA†
24.52 on 1 and 65 DF
0.00006
+
< 0.0001*
NA
Trophic
levels in
non-mamma
ls
(Figure 5 &
Supp. Fig. 1)
all
Malignancy
prevalence
0.4
5.27 on 3 and 141 DF
0.54
+
0.001*
NA
Neoplasia
prevalence
0.39
5.18 on 3 and 141 DF
0.32
+
0.002
NA
gastrointestinal
Malignancy
prevalence
0.36
4.82 on 3 and 89 DF
0.77
+
0.003
NA
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Neoplasia
prevalence
0.3
4.56 on 3 and 89 DF
0.09
+
0.005
NA
non-gastrointestinal
Malignancy
prevalence
0.49
2.10 on 3 and 116 DF
0.69
+
0.10
NA
Neoplasia
prevalence
0.47
3.51 on 3 and 116 DF
0.48
+
0.01
NA
Habitat
range
(Supp. Fig.
2)
all
Malignancy
prevalence
0.33
1.60 on 2 and 226 DF
0.53
+
0.20
NA
Neoplasia
prevalence
0.33
2.84 on 2 and 226 DF
0.50
+
0.06
NA
Inferred
metabolic
rate: (M –1/4 •
e–E/kT)0.1 +
“mammals
versus non-
mammals”
all
Malignancy
prevalence
(Supp. Fig.
3)
0.006
0.19 on 1 and 75 DF
0.77
–
0.66
0.60
Neoplasia
prevalence
(Figure 6)
0.08
7.99 on 1 and 75 DF
0.00006
–
0.006
0.04
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Acknowledgements
Thanks to all of the pathologists, veterinarians, and staff at the zoos, aquariums, and
veterinary hospitals for contributing to the data collection by diagnosing malignancy and
neoplasia prevalence. Specifically, we would like to acknowledge the following institutions:
Akron Zoo, Atlanta Zoo, Audubon Nature Institute, Bergen County Zoo, Birmingham Zoo,
Buffalo Zoo, Capron Park Zoo, Central Florida Zoo, Dallas Zoo, El Paso Zoo, Elmwood Park
Zoo, Fort Worth Zoo, Gladys Porter Zoo, Greensboro Science Center, Henry Doorly Zoo,
Utah’s Hogle Zoo, Jacksonville Zoo, John Ball Zoo, Los Angeles Zoo, Louisville Zoo,
Mesker Park Zoo, Miami Zoo, Oakland Zoo, Oklahoma City Zoo, Philadelphia Zoo, Phoenix
Zoo, Pueblo Zoo, San Antonio Zoo, Santa Ana Zoo, Santa Barbara Zoo, Sedgwick County
Zoo, Seneca Park Zoo, The Brevard Zoo, The Detroit Zoo, The Oregon Zoo, and Toledo Zoo.
Thanks to Diego Mallo, Walker Mellon, and Rebecca Belshe for help with the statistical
analyses. Thanks to Valerie Harris for contributing to the collection of life history data.
Thanks to Andrew Beckerman and Michael Angilletta for helpful discussions on ecological
relationships between species, food webs, and the metabolic theory of ecology. This work
was supported in part by NIH grants U54 CA217376, U2C CA233254, P01 CA91955, and
R01 CA140657 as well as CDMRP Breast Cancer Research Program Award BC132057 and
the Arizona Biomedical Research Commission grant ADHS18-198847. The findings,
opinions and recommendations expressed here are those of the authors and not necessarily
those of the universities where the research was performed or the National Institutes of
Health.
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Author Contributions
S.E.K. conceived and designed the study comparing cancer data with trophic levels,
habitats, metabolic rates, and species body temperatures in February 2020. S.E.K. collected
data on trophic levels, habitats, number of habitats, habitat productivity, average body
temperatures, inferred metabolic rates, analysed the data, and wrote the first draft of the
manuscript. T.M.H. helped in identifying the diet of animals in zoos. M.M.G., E.G.D., and
T.M.H. helped in the collection and coordination of cancer data across institutions. S.M.R.,
Z.C., and A.M.B., provided data on malignancy and neoplasia prevalence, necropsies for
each species, and species adult weight. A.A. and C.C.M. provided research supervision,
helpful discussions, comments, and guidance throughout the project. All authors edited and
approved the final version of the manuscript.
Competing interests
We declare we do not have any conflicts of interest.
Supplementary Figures
Supplementary Figure 1. Neoplasia prevalence in relation to trophic levels in
mammals versus non-mammals. Neoplasia prevalence is always higher in higher trophic
levels except for gastrointestinal neoplasia prevalence in mammals (Panel C). Each dot is a
species, and each trophic level consists of ≥10 species. The number (N) of species in each
trophic level is indicated above each trophic level. The horizontal black line in each category
shows the median neoplasia prevalence in that category. We provide P-values between
categories that are significantly different, and for all comparisons in the supplementary data
and Table 2.
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Supplementary Figure 2. Neoplasias (B) but not malignancies (A), are more
prevalent in species living in three or more habitats. The y axes show malignancy
prevalence across tissues (A), neoplasia prevalence across tissues (B), gastrointestinal
malignancy prevalence (C), gastrointestinal neoplasia prevalence (D). Each dot is a species,
and each category on the x axis consists of ≥10 species. The number of species (N) in a
category is noted above each category. The horizontal black line in each category shows the
median malignancy or neoplasia prevalence in that category. The x axis is classified as a
categorical variable. We provide P-values between categories that are significantly different,
and for all comparisons in the supplementary data and Table 2.
Supplementary Figure 3. Malignancy prevalence across tissues is not
significantly correlated with inferred metabolic rates. The formula M –1/4 • e –E/kT
multiplied by bo is a measure of metabolic rate for each species. The bo value [bo = a
coefficient independent of body size and temperature from Savage et al. 200458] is unknown
for each species, so we measured the M –1/4 • e –E/kT values for each species (where M = mass,
E = 0.65 eV, k = 8.62 • 10–5 eVK–1, and T = temperature in Kelvin)56. Each dot is a species.
Supplementary Figure 4. Malignancy and neoplasia prevalence are not
significantly associated with average body temperature (in Kelvin) across the tree of life.
The y axes show malignancy prevalence across tissues (A: F-statistic: 0.80 on 1 and 78 DF,
Lambda: 0.80) and neoplasia prevalence across tissues (B: F-statistic: 0.24 on 1 and 78 DF,
Lambda: 0.85). Each dot is a species.
Supplementary Figure 5. M –1/4 • e –E/kT (which is analogous to metabolic rate) to
the power of 0.1 is lower in higher trophic levels. Each dot is a species. This measure (M
–1/4 • e –E/kT) multiplied by bo is a measure of metabolic rate for each species. We do not know
the bo value [bo = a coefficient independent of body size and temperature from Savage et al.
200458] for each species, so we simply measured the M –1/4 • e –E/kT values for each species (M
33
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= mass, E = 0.65 eV, k = 8.62 • 10–5 eVK–1, and T is temperature in Kelvin)56. The number
(N) of species in each trophic level is indicated above each trophic level. The horizontal black
line in each category shows the median. F-statistic: 1.84 on 2 and 67 DF, Lambda: 0.77, R²:
0.05. We only provide P-values between categories in the figures if the comparisons are
significantly different. All P-values for the category comparisons are available in the
supplementary data.
Supplementary Figure 6. The majority of carnivores in our database live in
many different habitats, whereas the majority of herbivores live in one or two habitats.
N shows the number of species in each level. The percentage shows the number of species in
a specific habitat range out of the total species in that trophic level • 100%. Percentages in
bold show the two highest percentages of species in each trophic level.
Supplementary Figure 7. The majority of herbivores and invertivores live in high
productivity habitats, whereas the majority of primary and secondary carnivores live in
low productivity habitats. Each dot is a species. We used minimal jitter to better visualise
individual dots. Numbers show the number of species in each level. Numbers are shown only
in the three columns, e.g. habitats where we have ≥10 species in our database. The percentage
shows the number of species in a specific habitat out of the total species in that trophic level •
100%. Percentages in bold show the two highest values in each habitat per trophic level.
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Te
Polar
Desert
(dry air is warmed as it
descends)
Desert
(dry air is warmed as it
descends)
Tem pe r a t e
(sun rays hit the Earth at an angle,
air cools and descends)
Tem pe r a t e
(sun rays hit the Earth at an angle,
air cools and descends)
Te
Polar
Desert
(dry air is warmed as it
descends)
Desert
(dry air is warmed as it
descends)
Tem pe r at e
(sun rays hit the Earth at an angle,
air cools and descends)
Tem pe r at e
(sun rays hit the Earth at an angle,
air cools and descends)
(less sunlight than temperate environments)
environments)
(less sunlight than temperate
(less sunlight than temperate
environments)
(less water than the tropics)
(less sunlight than temperate environments)
(less water than the tropics)
(less sunlight than temperate environments)
(less sunlight than temperate environments)
(sun rays hit the Earth at an angle,
less sunlight than the tropics)
(less sunlight than temperate environments)
(sun rays hit the Earth at an angle,
less sunlight than the tropics)
(sun rays hit the Earth directly, moisture
rises, cools, and is released as rain)
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Minimum productivity habitat of a species (gC produced per m²per year)
N = 54 N = 25 N = 100 N = 56
Malignancy prevalence
across tissues
A
PGLS
R² = 0.2
P-value = 0.04
(2000) (1375) (1250) (3)
P-value = 0.01
P-value = 0.03
0.0
0.1
0.2
0.3
0.4
0.5
Tropical Marine Temperate Desert
Herbivore
Invertivore
PrimaryCarnivore
SecondaryCarnivore
Secondary Carnivore
Primary Carnivore
Invertivore
Herbivore
Birds
Fish or Amphibia
Mammals
Reptiles
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Neoplasia prevalence
across tissues
B
PGLS
R² = 0.19
P-value = 0.02
Minimum productivity habitat of a species (gC produced per m²per year)
(2000) (1375) (1250) (3)
N = 54 N = 25 N = 100 N = 56
P-value= 0.01
P-value= 0.01
0.0
0.1
0.2
0.3
0.4
0.5
Tropical Marine Temperate Desert
Herbivore
Invertivore
PrimaryCarnivore
SecondaryCarnivore
Secondary Carnivore
Primary Carnivore
Invertivore
Herbivore
Birds
Fish or Amphibia
Mammals
Reptiles
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0.0
0.1
0.2
0.3
1000 1500 2000
Mammalia
NonMammal
Average habitat productivity (gC produced per m² per year)
Gastrointestinal
malignancy prevalence
PGLS
R² = 0.02
P-value = 0.008
Mammals
Non-mammals
A
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0.0
0.1
0.2
0.3
1000 1500 2000
Mammalia
NonMammal
Gastrointestinal
neoplasia prevalence
Average habitat productivity (gC produced per m² per year)
Mammals
Non-mammals
PGLS
R² = 0.03
P-value = 0.01
B
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0.0
0.1
0.2
0.3
0.4
0.5
Herbivore Invertivore Primary carnivore Secondary carnivore
P-value= 0.0002
N = 110 N = 63 N = 48 N = 23
Trophic level
P-value= 0.001
Malignancy prevalence
across tissues
A
PGLS
R² = 0.31
P-value = 0.0008
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0.0
0.2
0.4
0.6
Herbivore Invertivore Primary carnivore Secondary carnivore
Trophic level
Neoplasia prevalence
across tissues
P-value= 0.001
P-value= 0.0007
B
PGLS
R² = 0.3
P-value = 0.001
N = 110 N = 63 N = 48 N = 23
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0.0
0.1
0.2
0.3
0.4
Herbivore Primary carnivore
Mammals Non-Mammals
Malignancy prevalence
across tissues
0.00
0.05
0.10
0.15
0.20
Herbivore Primary carnivore
Gastrointestinal
malignancy prevalence
Non-gastrointestinal
malignancy prevalence
0.0
0.1
0.2
0.3
Herbivore Primary carnivore
0.0
0.1
0.2
0.3
Herbivore Invertivore Primary carnivore Secondary carnivore
0.0
0.1
0.2
0.3
Herbivore Invertivore Primary carnivore Secondary carnivore
0.0
0.1
0.2
0.3
Herbivore Invertivore Primar y carnivore Secondary carnivore
0.0
0.2
0.4
0.6
Herbivore Invertivore Primary carnivore Secondary carnivore
0.0
0.2
0.4
0.6
Herbivore Invertivore Primary carnivore Secondary carnivore
0.0
0.2
0.4
0.6
Herbivore Invertivore Primary carnivore Secondary carnivore
0.0
0.2
0.4
0.6
Herbivore Invertivore Primar y carnivore Secondary carnivore
0.0
0.2
0.4
0.6
Herbivore Invertivore Primary carnivore Secondary carnivore
0.0
0.2
0.4
0.6
Herbivore Invertivore Primary car nivore Secondary carnivore
PGLS
R² = NA
P-value = 0.0001
PGLS
R² = NA
P-value = 0.13
PGLS
R² = NA
P-value < 0.0001
PGLS
R² = 0.4
P-value = 0.001
PGLS
R² = 0.36
P-value = 0.003
PGLS
R² = 0.49
P-value = 0.10
P-value= 0.002
P-value= 0.001
P-value= 0.004
P-value= 0.003
P-value= 0.001
N = 63 N = 11
N = 40 N = 10
N = 61 N = 11
N = 47 N = 57 N = 37 N = 14
N = 28 N = 39 N = 20 N = 12
N = 37 N = 45 N = 33 N = 13
A
B
C
D
E
F
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0.0
0.2
0.4
0.6
0.06 0.07 0.08
Mammalia
NonMammal
Inferred metabolic rate: (M –1/4 • e –E/kT)0.1
Neoplasia prevalence
across tissues
Mammals
Non-mammals
PGLS
R² = 0.08
P-value = 0.006
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