1.8: Renovation of the Terrestrial Life - Biology

1.8: Renovation of the Terrestrial Life - Biology

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In the Triassic and early Jurassic, Pangea begins to disintegrate. The climate was warm at first but very dry, and by the end of the era, it gradually became more convenient to the terrestrial life.

Among the seed plants, there appeared more advanced groups like bennettites, which participate in making savanna type vegetation (without grasses, though, the role of grasses was likely played with ferns, mosses, and lichens). Seeds of many plants were protected by scales or were embedded in an almost closed cupula. Seed protection was the “answer” of seed plants to the appearance of numerous phytophagous insect groups. Some other groups of insects began to adapt to the pollination of seed plants; this was an additional factor to facilitate the growing of seed covers.

Reptilians were still dominated but gradually replaced with various groups of archosauromorphs, the most advanced reptiles by that time, able to move very quickly, typically using only two legs.

Simultaneously run there were processes of “mammalization” and “avification” of reptiles. Ancestors of mammals were now in a small dimensional class and became insectivorous; this is because small herbivorous reptiles were simply physiologically impossible. Plant food is not very nutritional, and reptile feeding apparatus was unable to extract enough calories to support small, presumably more active animal. Giant herbivorous reptiles have less relative surface and therefore need fewer calories. Only turtles are an exception because of their “super-protection”, which however has closed all further ways to improve the organization.

Ancestors of mammals were animals of the size of a hedgehog or less; they continued to improve their dental system, the thermal insulation system, and increase the size of the brain. The result was the emergence of first the first true mammals.

Among “true” reptiles, dinosaurs (birds’ancestors), crocodiles, and pterosaurs (which dominate the air for the next 70 million years) have appeared.

In the seas, there are first diatom algae, that stimulated the zooplankton, and in turn, cephalopods, which dominated throughout the Mesozoic. Also, to replace the extinct by this time mesosaurs, appeared new groups of marine reptiles, for example, notosaurs and molluscivorous placodonts.

How Many Species Are There on Earth and in the Ocean?

Affiliations Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada, United Nations Environment Programme World Conservation Monitoring Centre, Cambridge, United Kingdom, Microsoft Research, Cambridge, United Kingdom

Affiliation Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada

Affiliation Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada

Affiliation Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada

Background & Summary

Habitat loss is one of the primary causes of biodiversity decline 1,2,3,4 . There are many definitions of ‘habitat’, but they can broadly be described as the entirety of the physical conditions - including land cover and climate - that enable a species’ population to persist in space and time 5 . There is a strong positive relationship between the extent and intactness of a species’ habitat and its population persistence 6,7,8 , which may help species extinction risk assessments when information about other symptoms of risk is limited. Knowledge about species’ habitats is critical to design landscape management plans 9 , conservation planning 10,11 and analysis of past trends and future scenarios of species’ extinction risk 12,13,14 .

There are many ways to delimit species’ habitats types 15,16,17 , which can be represented as either continuous variables 17,18 or discrete classes 19 . The International Union for Conservation of Nature (IUCN) Red List of Threatened Species uses a global standard typology ( that aims to categorize all species-relevant habitats into a system of pre-defined habitat classes 16 . In this scheme 16 different broad habitat classes are listed at level 1 (e.g. forest, wetlands), with 119 more specific classes listed at level 2 (e.g. Forest – Subtropical/tropical moist lowland). Although detailed descriptions of the habitat classes in this classification scheme are unfinished - with the latest available documentation draft dating to December 2012 - it is used by IUCN Red List assessors to describe species’ habitats preferences 20 .

IUCN Red List assessments also involve compiling distribution maps showing the range boundaries for each species, typically based on point locality data, presence/absence data from atlases, published maps in field guides and monographs, remote sensing data on habitat extent, and expert inference (e.g. 20,21,22 ). Such maps are typically used to estimate Extent of Occurrence (the area of a minimum convex polygon that contains all occurrence records) in Red List assessments, and are also used in aggregate to quantify spatial biodiversity patterns at regional and global scales 23 . However, maps showing distributional boundaries often considerably overestimate the occurrence of a species at finer scales 11,24 , a type of error commonly known as commission error. To obviate these types of errors, one approach is to use the habitat preferences and elevational range documented in IUCN Red List assessments to exclude all land-cover classes and altitudes that are not considered suitable for a species in order to map its ‘Area of Habitat’ (AOH, 20 ). This requires a ‘crosswalk’ that establishes the relationships between each habitat and land-cover class in a particular land-cover product 13,25,26 . However establishing such relationships between different thematic legends can be problematic.

Differences in thematic resolution and definitions can lead to large variations in area-based land-cover estimates 27 , and errors have been shown to increase uncertainty and decrease accuracy of any subsequent analysis 28 . These problems are likely to affect AOH estimates as described above, for instance by treating climatically distinct habitats - such as savannah-dominated and subtropical-moist shrub-covered land - as equivalent. Although the potential distribution of a species can be estimated statistically 29,30 , it is challenging to do so in a robust, consistent and reproducible manner 31,32 and in most cases the primary biodiversity data necessary to do so are not available 33 . There is therefore a need to explore alternative approaches to mapping AOH.

Here we describe a method to map the IUCN habitats classification scheme directly for most terrestrial and inland water habitats. We do so by overlaying the best available data on land cover, climate and other ancillary data sources using simple map algebra. The derived map describes the global distribution of habitats at levels 1 and 2 as outlined by the IUCN classification scheme in the year 2015 16 . We validated the classes from this global map using independent spatially-explicit estimates. To our knowledge this is the first attempt to map IUCN habitat classes at a global scale.

Life cycle and population structure of the terrestrial isopod Hemilepistus klugii (Brandt, 1833) (Isopoda: Oniscidea) in Iran

The life cycle and population structure of Hemilepistus klugii were studied in a population in Varamin, Iran. The population was sampled monthly (or fortnightly during the breeding season) from February 2008 to June 2009 and a total of 7015 individuals, comprising 1069 males, 1079 females and 4867 juveniles, were collected. As in other Hemilepistus species, five distinct phenophases, namely pair formation, gestation, hatching, growth and stationary, were recorded during the life cycle of H. klugii. The overall sex ratio was 1 : 1 but varied over time. Ovigerous females were observed only in April, indicating a seasonal and very short breeding period. With a short lifespan after breeding, females demonstrated true semelparity. The mean cephalothorax width for ovigerous/post-ovigerous females was higher in 2008 than in 2009. These females attained the largest size in the population throughout the year. The number of eggs per female ranged from 28 to 147 (mean ± SE, 78 ± 1.8). There was a positive correlation between female size and fecundity. Recruitment occurred in late April and resulted in the highest population density in this month, whereas the lowest densities were observed during November to January. Despite a high percentage of ovigerous females carrying undeveloped eggs (72.3%), intramarsupial mortality was low (3.5%).


We are grateful to the late Ehsan Entezari for his help during field and laboratory investigations. We would like to thank Dr Martin Zimmer, Kiel, for his advice and useful scientific suggestions and for improving the English text we also thank the anonymous reviewers.

A Conservation Assessment of the Terrestrial Ecoregions of Latin America and the Caribbean

This priority-setting study elevates, as a first principle, maintaining the representation of all ecosystem and habitat types in regional investment portfolios. Second, it recognizes landscape-level features as an essential guide for effective conservation planning. Without an objective framework to assess the conservation status and biological distinctiveness of geographic areas, donors run the risk of overlooking areas that are seriously threatened and of greatest biodiversity value. The lack of such an objective regional framework prompted this study, whose goals were: 1) to replace the relatively ad hoc decisionmaking process of donors investing in biodiversity conservation with a more transparent and scientific approach 2) to move beyond evaluations based largely on species lists to a new framework that also incorporates maintaining ecosystems and habitat diversity 3) to better integrate the principles of conservation biology and landscape ecology into decisionmaking and 4) to ensure that proportionately more funding be channeled to areas that are of high biological value and under serious threat.

David Moore's World of Fungi: where mycology starts

Life first emerged on land during the pre-cambrian period (see Fig. 5 below for the geological timescale), when it was colonised by phototrophic micro-organisms, which were probably prokaryotic. Until fairly recently, it was believed that land plants became established during the late Silurian. However, recent evidence from several disciplines suggest that they may have emerged earlier, during the Ordovician period.

Vascular plants almost certainly arose from green algae, which become semi-aquatic, and then fully terrestrial during their evolution into the first land plants. When semi-aquatic algae did emerge and begin to invade the land, between 490 and 409 million years ago (Mya), they encountered a harsh environment. The soils contained no organic matter and therefore only mineral nutrients. This poor soil also meant that any nutrients or water obtained were quickly lost.

However, the barren land was also a place of opportunities, since there was very little competition, and carbon dioxide and light were readily available. To exploit this new environment, the earliest plants could either develop their own means of gaining nutrients and water or, as most did, they could alter their relationship with the aquatic fungi that were also invading the land. The earlist fungi were aquatic chytrids with a small simple thallus (virtually a single cell), rhizoids to anchor the thallus to the substratum and extract nutrients, and flagellated motile spores.

Neither the alga nor the fungus was fully equipped to exploit the terrestrial environment. The alga lacked the ability to extract essential nutrients from the soil (what there was of it), whilst the fungi lacked the ability to manufacture carbohydrates. Both had problems dealing with desiccation.

Instead of the fungi becoming parasitic on the rhizoids of the evolving plants (roots or root hairs had not yet evolved), the two organisms formed a mutualistic symbiosis that allowed them both to exploit the terrestrial environment.

The algal/plant component became the morphologically dominant partner, as it was better equipped to survive terrestrially due to autotrophy, and hence the plant component became the rapidly evolving partner. The partnership, (perhaps representing the earliest lichen) was capable of more efficient uptake and assimilation of nutrients, and this could also include nutrients derived by the fungal partner's ability to digest and recycle the two-billion year accumulation of dead bacterial remains washed up on the shores of the shallow seas and lakes. Thus, the partnership would have had a selective advantage over non-symbiotic plant forms, not only for survival, but also for dynamic evolution, since the extra energy generated using extra nutrients would allow for more differentiation and development of complex tissues.

The fungal thalli involved in these early associations almost certainly gave rise to the aseptate hyphae (which we now classify among the Zygomycota), although whether this was before or after the emergence of land plants is unknown. However, as most microbes today thrive best in biofilm communities it is a fair assumption that this was also true of the most ancient microbial communities. Life in a biofilm might have been adequate for a semi-aquatic/semi-terrestrial partnership between unicellular fungi and unicellular algae. But the filamentous mode of growth is an excellent way of escaping the biofilm to explore dry land. Fossil and DNA evidence suggests that zygomycete aseptate hyphae originated during the Cambrian, and resemble those fungi that form arbuscular mycorrhizal associations today (Figs 1-3). The septate fungi (Ascomycotina and Basidiomycotina) do not emerge into the fossil record until the Devonian period.

The AM fungi are one of the few plant-fungus relationships that have a fossil record (Simon et al, 1993) and it is generally accepted that early vascular plants were associated with AM-like fungi, and that their origin and ability to colonise land was highly dependant upon the association (Lewis, 1987 Selosse & LeTacon, 1998 Allen, 1991).

Ribosomal DNA sequencing by Simon et al (1993) place the origin of AM-like fungi between 462 and 363 Mya, within the Ordovician, Silurian and Devonian periods. These dates would easily place them at the time of land plant emergence.

Fossil mycorrhizas were first discovered by Weiss (1904) in lower carboniferous strata. The earliest and best examples of endomycorrhizas are from the Rhynie Chert fossils, from the Devonian period, discovered by Kidstone & Lang (1921). These show fungal structures resembling vesicles and spores, from the fungus Palaeomyces, associated with the rhizoids of plants such as Rhynia and Asteroxylon.

Recent reappraisal of the Rhynie Chert plants suggest those primitive plants may have been associated with fungi very similar to modern AM fungi by the early Devonian period (410 to 360 Mya) (Pirozynski & Dalpé, 1989).

The most important recent discoveries of mycorrhizal fossils are the arbuscles found by Stubblefield et al (1987) in Triassic strata in Antartica. These fossils show arbuscles organised like those of modern day AM fungi, indicating that the structural characteristics of the AM were well developed by the Triassic, even if the functional properties may not have been.

The AM association is now widespread and found in most of the extant plant families. Its worldwide dominance is such that the association is now regarded as ancestral in plants (see Selosse & LeTacon, 1998).

Other mycorrhizal forms evolved from the AM-like at varying rates. The evolution of ectomycorrhizas (ECM) is a relatively recent event geologically, with a fossil record dating back to the second half of the Mesozoic. This is consistent with the emergence of its associated plant taxa. Pinaceae may have originated by the late Triassic, and certainly by the Jurassic, with Pinus in existence by the start of the Cretaceous (Fig. 5). However, the main differentiation of the family occurred from the mid-Cretaceous onwards, when Betulaceae, Fagaceae and Salicaceae originated (Malloch, D.W., Pirozynski, K.A. & Raven, P.H. (1980) Ecological and evolutionary significance of mycorrhizal symbioses in vascular plants (A Review). Proceedings of the National Academy of Sciences USA, 77: 2113-2118). The fungi associated with ECM, septate Ascomycotina and Basidiomycotina were certainly present during the Cretaceous (130 Mya) and ascomycotina possibly earlier.

Orchid and ericoid mycorrhizas most likely arose early in the evolution of their host taxon, but are more difficult to date. The ericoid association is geologically fairly recent, with fossil records of the Ericaceae dating back only to the early Tertiary.

Fig. 4. Possible orchid fossil (Protorchis monorchis) from the Eocene epoch of the Tertiary period, 54 Mya. From Arditti (1977). Orchid Biology: Reviews and Perspectives I. Cornell University Press, London. © Arditti with permission.

The Orchidaceae probably arose before the ericoid, during the Eocene, a division of the Tertiary period beginning 54 Mya. Several orchids or proto-orchids have been identified from this period, such as the fossil above, although whether these are true orchids is still debated. Fossils evidence is scarce though, since orchids do not fossilise well (Fig. 4).

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The global distribution of tetrapods reveals a need for targeted reptile conservation

The distributions of amphibians, birds and mammals have underpinned global and local conservation priorities, and have been fundamental to our understanding of the determinants of global biodiversity. In contrast, the global distributions of reptiles, representing a third of terrestrial vertebrate diversity, have been unavailable. This prevented the incorporation of reptiles into conservation planning and biased our understanding of the underlying processes governing global vertebrate biodiversity. Here, we present and analyse the global distribution of 10,064 reptile species (99% of extant terrestrial species). We show that richness patterns of the other three tetrapod classes are good spatial surrogates for species richness of all reptiles combined and of snakes, but characterize diversity patterns of lizards and turtles poorly. Hotspots of total and endemic lizard richness overlap very little with those of other taxa. Moreover, existing protected areas, sites of biodiversity significance and global conservation schemes represent birds and mammals better than reptiles. We show that additional conservation actions are needed to effectively protect reptiles, particularly lizards and turtles. Adding reptile knowledge to a global complementarity conservation priority scheme identifies many locations that consequently become important. Notably, investing resources in some of the world’s arid, grassland and savannah habitats might be necessary to represent all terrestrial vertebrates efficiently.


The non-natural and polymer-based structure of plastics together with their composition rich in xenobiotics and poorly soluble biopersistent hydrophobic particles confers to microplastic pollution the fundamental nature of “combined physical and chemical effects” (Figure 2). Previous extrapolations of some mechanisms of physical effects of microplastics from aquatic to terrestrial environments have been discussed (Horton, Walton, et al., 2017 Rillig, 2012 ). For instance, large plastics limit the exchange of gases and compounds that might affect environmental health (Steinmetz et al., 2016 ) and cause organism entanglement (Barnes et al., 2009 ). Smaller particles can be ingested or inhaled causing pseudosatiation and blockage of the digestive tract, or abrasion and irritation of mucosa (Barnes et al., 2009 Rehse et al., 2016 ). Potential chemical effects are less discussed and might have at least two components as outlined in detail below.

The first component is the leaching of plastic additives, plasticizers, and components of the polymer matrix, which occurs during use, in the environment, or within organisms ((CONTAM), 2016, Whitacre, 2014 ). This leaching is problematic because many of these additives such as phthalates and bisphenol A are known for their estrogenic activity and further potential endocrine disruption in vertebrates and some invertebrate species (Sohoni & Sumpter, 1998 ). In fact, plastic additives are now reported amongst the most commonly found anthropogenic substances in environmental samples (Whitacre, 2014 ). Phthalates, bisphenol, and many other plastic additives have been found at moderately high levels in potentially microplastic-rich sludge from water treatments used for agricultural purposes (Clarke & Smith, 2011 ). Most plastic materials leach compounds with estrogenic activity (Yang et al., 2011 ), which is problematic as ambient estrogenicity and demasculinizing effects in laboratory populations and in the wild have been shown to be linked (Manikkam, Tracey, Guerrero-Bosagna, & Skinner, 2013 Marty et al., 2017 Tamschick et al., 2016 Ziková et al., 2017 ). The broad use of plastics and increasing environmental concentrations of endocrine active compounds are of ecological concern: endocrine systems were reasonably well preserved during the evolution of vertebrates, and therefore endocrine disruptors might trigger wide-ranging direct consequences for animal health. It is not appropriate to assume that the many plastic components with known potential for endocrine disruption will have no ecological impacts on exposed biota. When larger plastic particles fragment into smaller pieces there is an exponential increase in the surface/volume ratio. This increases the potential for leaching estrogenically active compounds because many additives are physically, but not chemically, bound to a polymeric structure and hence can almost always leach from the polymer surface (Yang et al., 2011 ).

The second component of the chemical effect arises from properties of poorly soluble biopersistent small microplastics (<1 μm) that enable them to interact with biological membranes, organelles, and molecules. This can incite many effects commonly triggered by toxic chemicals such as inflammation, changes in membrane permeability, oxidative stress, among others ((Forte et al., 2016 Hamoir et al., 2003 Jeong et al., 2016 Oberdorster, 2000 ), also see toxicity targets of nanoplastics). The nature of physico-chemical combined effects of microplastics might be the cause of the lack of monotonicity (i.e. lack of constant or increasing effects when increasing exposure concentrations) often found in microplastic dose–response curves ((CONTAM), 2016 , Mahler et al., 2012 Rehse et al., 2016 ). Indeed, the lack of monotonicity in acute toxicity of a particle-solute complex mixture might be associated with the nonmonotonic behavior of particles at nanoscale (Machado, Zarfl, Rehse, & Kloas, 2017 ). Taken altogether, future studies investigating the effects of microplastic exposure should consider the idiosyncratic interactions of plastic materials (leachable chemical components), their particle size distribution, and the chemical behavior of their surfaces.

The nature of microplastic combined effects can affect soils through physico-chemical changes on soil texture and structure, which is consequential for water cycling and ecosystem functioning in terrestrial systems and diverse plant–soil feedbacks (Bergmann et al., 2016 Zheng, Morris, Lehmann, & Rillig, 2016 ). In this context, microplastic-driven changes in the hydrologic properties of soils could influence soil microbial biodiversity, with potential impacts on key symbiotic associations in terrestrial ecosystems, such as mycorrhizal (Hallett et al., 2009 ) and N-fixing (Conrad, 1996 ) associations. Such potential physical impacts on soil structure and function are of particular concern for the soil microbiome because the mechanistic understanding of biodiversity loss and extinction in those ecosystems are not fully understood (Machado, Valyi, & Rillig, 2017 Veresoglou, Halley, & Rillig, 2015 ). Moreover, the hydrophobic surfaces of plastics and their eco-corona are known to interact with hydrophobic compounds (Barnes et al., 2009 Galloway et al., 2017 Zhan et al., 2016 ). Trophic effects and other ecological impacts were observed when the chemicals adsorbed on microplastics were linked to marine intra- or interspecies communication pathways (Galloway et al., 2017 ). In soils many hydrophobic and amphiphilic compounds also regulate species communication and ecosystem processes. For instance, hydrophobins are amphiphilic proteins ubiquitous in soils that are secreted by fungi (Rillig, 2005 ). These cysteine-rich polypeptides play important roles in soil hydrophobicity and soil aggregate stability, with direct potential consequences for soil erosion and biogeochemical cycles (Rillig, 2005 ). It was suggested that microplastics might present distinct sorption properties for soil inorganic elements (Hodson, Duffus-Hodson, Clark, Prendergast-Miller, & Thorpe, 2017 ), and laboratory results suggest that hydrophobins play a role in the protection against nanoplastic toxicity to filamentous fungi (Nomura et al., 2016 ). Relevant biogeochemical changes might arise if the hydrophobic surfaces of microplastics interact with hydrophobins or other chemical drivers of soil structure in a manner significantly different from natural soil particles. Thus, further research is required to clarify the extent to which microplastic pollution could affect soil chemistry, texture, structure, and function.

Microplastics might accumulate in terrestrial and continental food webs at levels similar to or higher than in marine counterparts, although conclusive evidence is yet to be found. Zhao et al. found microplastic present in the digestive tract of 94% of dead terrestrial birds with diverse foraging behavior in China (Zhao, Zhu, & Li, 2016 ). Microplastics in the guts of freshwater continental birds have also been reported (Gil-Delgado et al., 2017 Holland, Mallory, & Shutler, 2016 ), and microplastic from agricultural activities seem to be an important source (Gil-Delgado et al., 2017 ). In some cases, microplastic was considerably smaller than the usual food of those birds, which suggests microplastic ingestion to be either accidental or via trophic transfer (Zhao et al., 2016 ). Moreover, a first quantitative assessment of trophic transfer of microplastic found increasing microplastic concentration in soils (

0.9 particles/g), earthworm casts (

14 particles/g), and chicken feces (

Most large plastic particles present low lethal toxicity. Nevertheless, the exposure, intake and uptake of small microplastics might cause toxicity and act as a new long-term environmental stressor and exert selective pressure on terrestrial organisms. Sublethal negative responses such as growth reduction were observed after the exposure of earthworms to 150 μm microplastics in their food (Lwanga et al., 2016 ). Such effects might be partially explained by histological damage and changes in the gene expression associated with microplastic exposure (Rodriguez-Seijo et al., 2017 ). Moreover, microplastics could act as vector of toxic Zn to earthworms under environmental conditions due to a higher adsorption of this metal to high density polyethylene microplastic (Hodson et al., 2017 ). Lethal effects (100% mortality) were observed after 1 h exposure of yeast cells of Saccharomyces cerevisiae to polystyrene nanobeads (50 and 100 nm, 10–15 mg/L) in 5 mM NaCl culture media (Miyazaki et al., 2014 ). For the filamentous fungi Aspergillus oryzae and Aspergillus nidulans the nanoplastic toxicity was not uniform among species or phenotypes, which was explained by the variability in one single trait: the resistance and hydrophobicity of cell walls (Nomura et al., 2016 ). The response and sensitivity to polystyrene nanobeads was also compared in vertebrates, which was linked to immunological traits of the respiratory system, i.e. the quantitative and qualitative attributes of surface respiratory macrophages of the domestic duck and rabbit (Mutua, Gicheru, Makanya, & Kiama, 2011 ). Given the environmental persistence of microplastics and their selective toxicity to organisms there is the potential for selective pressure of species traits with consequences for phenotypic, genetic, and functional biodiversity.



To create the human footprint we adopted the methods developed by Sanderson et al. 14 . To facilitate comparison across pressures we placed each human pressure within a 0–10 scale (not all pressure range across the full 0–10 scale, details on the weightings for each pressure are provided in the flowing sections) and acquired data for the early 1990s (on average 1993) and 2009. The human pressures we considered included the following: (1) the extent of built environments (2) crop land (3) pasture land (4) human population density (5) night-time lights (6) railways (7) roads and (8) navigable waterways. These pressures were weighted according to estimates of their relative levels of human pressure following Sanderson et al. 14 and summed together to create the standardized human footprint for all non-Antarctic land areas. Pressures are not intended to be mutually exclusive, and many will co-occur in the same location. Three pressures only had data from a single time period, and these are treated as static and excluded from all trend analyses (Table 1). We tested the sensitivity of our results to these static data, and to the scoring scheme, results below. We used ArcGIS 10.1 to integrate spatial data on human pressures. Analyses were conducted in Goode’s homolosine equal area projection at the 1 km 2 resolution, yielding ∼ 134.1 million pixels for Earth’s terrestrial surface. For any grid cell, the human footprint can range between 0 and 50.

Built environments

Built environments are human-produced areas that provide the setting for human activity. In the context of the human footprint, we take these areas to be primarily urban settings, including buildings, paved land and urban parks. Built environments do not provide viable habitats for many species of conservation concern, nor do they provide high levels of ecosystem services 45,46 . As such, built environments were assigned a pressure score of 10.

To map built environments, we use the Defense Meteorological Satellite Program Operational Line Scanner (DMSP-OLS) composite images, which gives the annual average brightness of 30 arcsec ( ∼ 1 km at the equator) pixels in units of digital numbers 47 . These data are provided for each year from 1992 to 2012. We extracted data for the years 1994 (1993 was excluded due to anomalies in the data) and 2009, and all years were then inter-calibrated to facilitate comparison across the years 48 . Using the DMSP-OLS data sets, we considered pixels to be built if they exhibited a calibrated digital number (DN) >20. We selected this threshold based on a global analysis of the implications of a range of thresholds for mapped extent of cities 49 , and visual validation against Landsat imagery for 10 cities spread globally.

Population density

Many of the pressures humans impose on the environment are proximate to their location, these include pressures such as disturbance, hunting and the persecution of non-desired species 50 . Moreover, even low-density human populations with limited technology and infrastructure developments can have significant impacts on biodiversity, as evidenced by the widespread loss of various taxa, particularly mega fauna, following human colonization of previously unpopulated areas 51 .

Human population density was mapped using the Gridded Population of the World data set developed by the Centre for International Earth Science Information Network 52 . The data set provides a ∼ 4 km × ∼ 4 km gridded summary of population census data for the years 1990 and 2010, which we downscaled to match the 1 km 2 resolution of the other data sets. For all locations with more than 1,000 people·per km, we assigned a pressure score of 10. For more sparsely populated areas, we logarithmically scaled the pressure score using

Nighttime lights

The high sensitivity of the DMSP-OLS 47 data set provides a means for mapping the sparser electric infrastructure typical of more rural and suburban areas. In 2009, 79% of the lights registered in the DMSP-OLS data set had a DN <20, and are therefore not included in our built environments layers. However, these lower DN values are often important human infrastructures, such as rural housing or working landscapes, with associated pressures on natural environments.

To include these pressures, we used the inter-calibrated DMSP-OLS layers 47 . The equations for inter-calibrating across years are second-order quadratics trained using data from Sicily, which was chosen as it had negligible infrastructure change over this period, where DN average roughly 14. For our purposes, DN values of 6 or less were excluded from consideration before inter-calibration of data, as the shape of the quadratic function leads to severe distortion of very low DN values. The inter-calibrated DN data from 1994 were then rescaled using an equal quintile approach into a 0–10 scale. The thresholds used to bin the 1994 data were then used to convert the 2009 data into a comparable 0–10 scale.

Crop and pasture lands

Crop lands vary in their structure from intensely managed monocultures receiving high inputs of pesticides and fertilizers to mosaic agricultures such as slash and burn methods that can support intermediate levels of many natural values 53,54 . For the purposes of the human footprint, we focused only on intensive agriculture because of its greater pressure on the environment, as well as to circumvent the shortcomings of using remotely sensed data to map mosaic agriculture globally, namely the tendency to confound agriculture mosaics with natural woodland and savannah ecosystems 55 .

Spatial data on remotely sensed agriculture extent in 1992 were extracted from the UMD Land Cover Classification 56 , and for 2009 from GlobCover 57 . Although intensive agriculture often results in whole-scale ecosystem conversion, we gave it a lower score than built environments because of less impervious cover.

Pasture lands cover 22% of the Earth’s land base or almost twice that of agricultural crops 58 , making them one of the most extensive direct human pressure on the environment. Land grazed by domesticated herbivores is often degraded through a combination of fencing, intensive browsing, soil compaction, invasive grasses and other species, and altered fire regimes 59 . We mapped grazing lands for the year 2000 using a spatial data set that combines agricultural census data with satellite derived land cover to map pasture extent 58 . We assigned pasture a pressure score of 4, which was then scaled from 0 to 4 using the per cent pasture for each 1 km 2 pixel.

Roads and railways

As one of humanity’s most prolific linear infrastructures, roads are an important direct driver of habitat conversion 60 . Beyond simply reducing the extent of suitable habitat, roads can act as population sinks for many species through traffic-induced mortality 61 . Roads also fragment otherwise contiguous blocks of habitat, and create edge effects such as reduced humidity 62 and increased fire frequency that reach well beyond the roads’ immediate footprint 63 . Finally, roads provide conduits for humans to access nature, bringing hunters and nature users into otherwise wilderness locations 64 .

We acquired data on the distribution of roads from the global roads open access data set (gROADS) 65 , and excluded all trails and private roads, which were inconsistently mapped. The data set is the most comprehensive publicly available database on roads, which has compiled nationally mapped road data spanning the period 1980–2000. We mapped the direct and indirect pressure of roads by assigning an pressure score of 8 for 0.5 km out for either side of roads, and access pressures were awarded a score of 4 at 0.5 km and decaying exponentially out to 15 km either side of the road.

While railways are an important component of our global transport system, their pressure on the environment differs in nature from that of our road networks. By modifying a linear swath of habitat, railways exert direct pressure where they are constructed, similar to roads. However, as passengers seldom disembark from trains in places other than rail stations, railways do not provide a means of accessing the natural environments along their borders. To map railways we used the same data set as was used in the original footprint 66 , as no update of this data set or alternate source has been developed. The direct pressure of railways was assigned a pressure score of 8 for a distance of 0.5 km on either side of the railway.

Navigable waterways

Like roads, coastlines and navigable rivers act as conduits for people to access nature. While all coastlines are theoretically navigable, for the purposes of the human footprint we only considered coasts 66 as navigable for 80 km either direction of signs of a human settlement within 4 km of the coast. We chose 80 km as an approximation of the distance a vessel can travel and return during daylight hours if travelling at 40 km h −1 . As new settlements can arise to make new sections of coast navigable, coastal layers were generated for the years 1994 and 2009.

Large lakes can act essentially as inland seas, with their coasts frequently plied by trade and fishing vessels. On the basis of their size and visually identified shipping traffic and shore side settlements, we treated the great lakes of North America, Lake Nicaragua, Lake Titicaca in South America, Lakes Onega and Peipus in Russia, Lakes Balkash and Issyk Kul in Kazakhstan, and Lakes Victoria, Tanganyika and Malawi in Africa as we did navigable marine coasts.

Rivers were considered as navigable if their depth was >2 m and there were signs of human settlements within 4 km of their banks, or if contiguous with a navigable coast or large inland lake, and then for a distance of 80 km or until stream depth is likely to prevent boat traffic. To map rivers and their depth we used the hydrosheds (hydrological data and maps based on shuttle elevation derivatives at multiple scales) 67 data set on stream discharge, and the following formulae from 68,69 :

Navigable river layers were created for the years 1994 and 2009, and combined with the navigable coasts and inland seas layers to create the final navigable waterway layers. The access pressure from navigable water bodies was awarded a score of 4 adjacent to the water body, decaying exponentially out to 15 km.

Validating the human footprint map

High-resolution images (median=0.5 m) were used to visually interpret human pressures at 3,560 1 km 2 sample points randomly located across the Earth’s non-Antarctic land areas (Supplementary Fig. 1). For the visual interpretation, the extent of built environments, crop land, pasture land, roads, human settlements, infrastructures and navigable waterways was recorded using a standard key for identifying these features (Supplementary Note 1). Shape, size, texture and colour were important characteristics for identifying human pressures on the environment. Interpretations were also marked as certain or not certain, and the year and the resolution of the interpreted image were recorded. The 344 uncertain points were discarded, leaving 3,116 validation points. The human footprint score for each point was determined in ArcGIS, and the visual and human footprint scores were then normalized to a 0–1 scale. The human footprint score was considered as a match to the visual score if they were within 20% of one another on the 0–1 scale.

Sensitivity to static data sets and scoring

Three data sets (pasture lands, roads and railways) were treated as static pressures in our human footprint maps, as temporally inter-comparable data were not available for these pressures at a resolution ammenable to inclusion in the human footprint. If these pressures changed at rates that were higher or lower relative to the dynamic data sets, it could mean that our estimates of change in the human footprint were similarly lower or higher than actual change. We were able to test the sensitivity of maps to static data sets for pasture lands. We acquired data on national level changes in pasture extent from 1993 to 2009 from the United Nations Food and Agricultural Organization 1 .

Given that these data are national scale, we were able to determine how the analyses of change across countries would be perturbed if our static pasture data were replaced with dynamic data from the United Nations Food and Agricultural Association (UN FAO). Using the FAO data we were able to estimate the likely changes in the average contribution of pasture land pressures to changes in the human footprint across countries. This was done by multiplying the 1993 human footprint pasture pressure data by the country-level change in pasture extent from the FAO. We found inclusion of dynamic pasture pressures in this way did not change our national-scale analyses of changes in the human footprint. Our estimates of national level change in human footprint were very similar using the static or dynamic pasture data (Pearson’s R 2 =99.8%, P<0.0001) with an average perturbation from the static data with the dynamic data of just ±2.6%, and Upper-middle-income countries still underwent the greatest increases and high-income countries underwent the least (Supplementary Table 1). We could not perform similar analyses for railways or roads, as changes in these linear infrastructures over time are simply not available, even at the national scale.

As described in the preceding sections, the eight pressures were scaled onto a 0–10 scale according to estimates of their relative levels of human pressure following Sanderson et al. 14 , before summing together to create the standardized human footprint maps. We adopted the same scaling methods as Sanderson et al. 14 , as the original human footprint map has proven to be a strong predictor of a wide range of ecological phenomena, lending support to the scoring scheme. Similar to the sensitivity analyses for static data sets, we tested the sensitivity of our national-scale results to this scoring scheme.

We achieved by first determining the contribution of each of the eight pressures to the overall human footprint score for each country. We then randomly perturbed the score or ‘weighting’ for each pressure up by 50%, down by 50% or keep it the same. After this random perturbation, we calculated the new national-scale average human footprint score for each country by multiplying the old score by the random perturbation from, and then summed across pressures. The proportional change in national-scale human footprint was calculated by comparing the original and new human footprint values. Finally, we calculated the relative proportional change in national-scale human footprint by dividing the proportion change observed for a country by the global-scale change induced by the scoring perturbation. These steps were repeated 100 times.

We found that a 50% perturbation to the scoring of each pressure led to on average a 14.5% change in each country’s national-scale human footprint. These national-scale changes also led to overall global-scale changes in human footprint values. When removing this global effect and focusing on only the relative changes across countries (such as would be done for the results contained in Fig. 6), we find that the 50% perturbations to the scores led to on average a 7.5% relative change in the national-scale human footprint values. These results demonstrate that national level human footprint values, especially when evaluating how countries compare relative to one another, are robust to how pressures are scored.

The human footprint national-level change

We compiled a number of national-scale data sets to determine if over-the-horizon consumption, socio-economic transition, urbanization or governance can explain the difference in footprint trajectories among the most rapidly expanding economies. Rapidly developing economies were considered to be the top 50 percentile of countries for GDP at purchasing power parity growth per person over the 1993 to 2009 period (n=73). Over-the-horizon consumption was measured as the trade balance (exports minus imports) for all agricultural (including crops and livestock) and forestry products in 2009, extracted from UN FAO 70 . Economic transition was measured in terms of economic development (2009 GDP per capita at PPP 33 ) and human development (Non-income Human Development Index, HDI 71 . The non-income HDI takes into account the average achievements of a country for health and education. The degree a country has urbanized was measured in terms of the proportion of its population that lives in urban areas in 2009 (ref. 33). Overall governance capacity was measured in terms of a country’s control of corruption 72 , and as a more direct measure of environmental governance, we used the proportion of a country’s terrestrial area that has been set aside in protected areas 33 . We excluded all countries smaller than 1,000 km 2 and those for which data were not available, leaving us with a 146 countries.

To explain the divergent environmental trajectories for the most rapidly expanding economies (countries within top 50 percentile for GDP at PPP per person change between 1993 and 2009) we fitted a general linear model at the country level, including the following variables: country area GDP at PPP per person in 2009 control of corruption proportion of country under protection net trade in agricultural and forestry products (calculated as the sum of the value of agricultural and forestry exports minus that of imports) and the proportion of population in urban areas and non-income HDI. The proportion of urban population and non-income HDI was highly correlated (Spearman’s rho=0.72) and they were therefore never included in the same models. We generated all possible subsets of the full model containing all variables and selected the most parsimonious one based on their Akaike information criterion (AIC) score. We also performed the same tests measuring trade in kilograms instead of dollars, but found that it did not alter our results.

Data availability

The 1 km 2 resolution human footprint maps are stored in the Dryad Digital Repository (doi:10.5061/dryad.052q5) 73 , and may also be freely accessed through the Socioeconomic Data and Applications Center website ( From Dryad the files may be downloaded as a single 7-zip file archive (, which contains individual GeoTIFF data sets, an excel file with the validation data and a PDF with the validation key. The GeoTIFFs include the human footprint maps for 1993 and 2009, as well 14 additional GeoTIFFs of the processed data for each of the eight pressures from each time step. The individual pressure layers are provided should data users wish to rework these data to create alternate maps of human pressure for their particular needs or region. These data are described in ref. 74

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