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Terrestrial Ecosystem

All of the organisms that interact with the physical environment make up an ecosystem (or ecological system). These biotic and abiotic components are linked via nutrient cycles and energy flows. In 1935, A.G. Tansley coined the term "ecosystem." It comes from the Greek word 'Oikos,' which means "house."

What is the Terrestrial Ecosystem?

A terrestrial ecosystem is a land-based population of species that includes biotic and abiotic interactions in a specific area. While there are various ecosystems on land and in the oceans around the world, terrestrial ecosystems are those that primarily live on land. The temperature range, average quantity of precipitation, soil type, and amount of light received help to determine the type of terrestrial ecosystem present in a particular location.

It is made up of a community of organisms and their surroundings that can be found on continents and islands. In terrestrial communities, there are differences in composition as well as spatial variance. The terrestrial ecosystem covers 144,150,000 km 2 , or 28% of the Earth's surface. Around 425 million years ago, the first terrestrial ecosystem was developed. 

Terrestrial ecosystems have largely witnessed a significant evolutionary process of plant and animal adaptive radiation in recent years. Adaptive radiation is the evolution of a group of animals or plants into a wide range of species that are adapted to different patterns of life. Closely related groups that have evolved in a short period of time are the best examples of adaptive radiation.

Types of Terrestrial Ecosystem

Terrestrial ecosystems can be divided into five types. Examples of terrestrial ecosystem are mentioned below: 

3. Grassland

Desert Ecosystem

Desert ecosystems are those that occur in desert environments. Deserts are dry and windy by nature. A desert ecosystem key abiotic deciding factor is the amount of rainfall. Some have sand dunes, whereas others largely rock. Although desert organisms are not as diverse as those found in forests, they do have adaptations that make them well suited to their surroundings. 

The average annual rainfall in deserts is less than 25 cm (about 10 inches). The terrestrial environment of a desert is characterized by large temperature differences between day and night. The soils are rich in minerals but low in organic matter. Succulents and cactus, as well as trees and shrubs, abound in the Sonoran Desert ecosystem. They've changed the structure of their leaves to reduce water loss. Cacti, for example, are CAM plants that are typically seen in the desert. 

Insects, reptiles, and birds are among the desert fauna. The Creosote shrub, for example, has a thick layer covering its leaves to limit water loss through transpiration. The Sahara desert, which covers the entire top half of the African continent, is one of the most well-known desert ecosystems. It is the world's largest hot desert, with temperatures reaching above 122 degrees Fahrenheit. Its expanse is similar to that of the whole United States.

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Forest Ecosystem

Forests cover around a third of the Earth's surface. Trees are the dominant vegetation in this habitat. Forest habitats are classified according to the kind of trees present and the amount of precipitation received. A forest ecosystem is made up of a variety of plants, mainly trees. This ecosystem is abundant in life due to the profusion of plants that function as producers. 

A forest is alive with not only vegetation but also animals. They are also a good supply of fruits and timber, and they help to keep the Earth's temperature stable. Temperate deciduous, temperate rainforest, tropical rainforest, tropical dry forest, and northern coniferous forests are examples of forests. 

Tropical rain forests have rain all year, while tropical dry forests have wet and dry seasons. Both of these forests are subjected to human pressure, such as the removal of trees to create a way for farmland. Rainforests have a lot of biodiversity because they get a lot of rain and have pleasant temperatures.

Grassland Ecosystem

About 10% of the Earth's surface is covered by the Grassland Ecosystem. It's found in areas with rainfall of 15–75 cm per year, which isn't enough to support a forest but more than a genuine desert. Grasslands are vegetation formations that are most common in temperate regions. Prairies and steppes are examples of temperate grasslands. Seasonal fluctuations occur, although they do not receive enough rainfall to support big woods.

In other parts of the world, these are known by different names, such as steppes in Europe and Asia, pampas in South America, Veldt in South Africa, and Downs in Australia. They are mostly found in the high Himalayas of India. The Steppes and Savana make up the majority of India's grasslands. Large regions of sandy and saline soils are covered by steppe formations. A grassland ecosystem is one in which grasses as well as other herbaceous (non-woody) plants predominate in the vegetation. 

Taiga Ecosystem

The taiga, commonly known as northern coniferous forest or boreal forest, is another type of forest environment. It extends across a wide area of land in the northern hemisphere. It has a low level of biodiversity, with only a few species. Short growing seasons, cold temperatures, and poor soil characterize Taiga habitats. The taiga is a cold-climate subarctic woodland. 

In the Northern Hemisphere, the subarctic zone is located slightly south of the Arctic Circle. The taiga is located between the tundra and temperate forests to the north and south, respectively. Summer days are long and winter days are shorter in this terrestrial environment. Lynx, moose, wolves, bears, and burrowing rodents are among the animals that live in the taiga. Animals like foxes and bears in Taiga have always been a threat to traditional hunters for their skin and fur. For thousands of years, their warm fur and tough skin, which has been transformed into leather, have helped people survive in difficult weather.

Permafrost — a layer of permanently frozen soil — is frequently seen beneath the taiga. A layer of bedrock sits just beneath the soil in other places. Direct human activity and climate change pose a threat to taiga ecosystems. Climate change threatens taigas in a variety of ways. The permafrost is partially thawing due to the warming environment. Because this water has nowhere to go, muskegs have taken over more of the taiga. The changing climate affects animal habitats as well. Native species are pushed out while non-native species are drawn in.

Tundra Ecosystem

The absence of trees is a distinguishing aspect of the tundra. There are several reasons why trees do not grow in this area. The permafrost prevents them from taking root, and those that do succeed have shallow root systems that aren't strong enough to endure heavy winds. Finally, low precipitation indicates that there is insufficient water to sustain trees.

Tundra is divided into two types: arctic and alpine. The Arctic tundra is found north of the boreal woods, in the Arctic Circle. Alpine tundras can be found on mountain peaks. Animals on the tundra are likewise evolved to harsh environments, and they take advantage of the short growing season transient boom of plant and insect life. Small mammals like Norway lemmings (Lemmus lemmus), arctic hares (Lepus arcticus), and arctic ground squirrels (Spermophilus parryii) live on the tundra, as do large mammals like caribou (Rangifer tarandus). 

These animals accumulate fat in order to survive and insulate themselves during the winter. Climate change has the potential to destabilize the cold tundra habitat and its inhabitants, as well as thaw the underlying permafrost, releasing greenhouse gases that will hasten global warming.

Impact of Terrestrial Ecosystems on Environment

Water scarcity (as compared to aquatic environments) and the importance of water as a limiting element as a result. Many of the water systems that keep ecosystems alive and sustain an ever-increasing human population are under strain. Rivers, lakes, and aquifers are drying out or polluted to the point of being unusable. Over half of the world's wetlands have vanished.

There are more diurnal and seasonal temperature changes. It's probable that some ecosystem functions will shift as the climate changes and as species are wiped out of an area; this might mean increased soil degradation, changes in agricultural production, and a decline in the quality of water given to human populations.

Characteristics of Terrestrial Ecosystem

Each of the Earth's terrestrial biomes has its own set of temperatures and precipitation patterns. When you compare annual precipitation totals and fluctuations from one biome to the next, you may get a sense of how important abiotic factors are in biome distribution. Temperature variations on a daily and seasonal basis are also crucial for determining the biome's geographic spread and vegetation type. The distribution of these biomes demonstrates that the same biome can be found in geographically separate places with similar weather.

Fun Facts/ Did You Know?

Life is tough in tundra.

Almost all tundras in the Northern Hemisphere are found in the Arctic. Small tundra-like areas do exist in Antarctica, which is located in the Southern Hemisphere. The tundra is a huge, treeless landscape. It covers roughly 20% of the Earth's surface. Because the ground is frequently continuously frozen, trees cannot grow there. Polar bears, foxes, and reindeer live on the Arctic tundra.

FAQs on Terrestrial Ecosystem

1. What are the advantages of terrestrial ecosystems?

Terrestrial ecosystems provide a variety of benefits, including habitat for fauna and flora, supplying food, fibre, fuel, and shelter. Carbon, water, and other nutrients are stored, transformed, and released.

2. What impact have we had on the world's terrestrial ecosystems?

The shape and function of terrestrial ecosystems are being radically altered by human activity. We are, for example, altering the chemical makeup of the atmosphere, transforming natural landscapes into urban areas, and transferring floral and faunal species far beyond their native ranges.

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Introduction to Terrestrial Ecosystem, Essay Example

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Terrestrial ecology is essential to human and animal health. This can be either biotic or Abiotic components of the ecosystems, which play a critical role in food production. Interaction of these factors of ecology affects human health and ability of ecology to support the rising human population. This article looks the importance and factors related to terrestrial ecology in health and food production perspective.

Terrestrial ecosystem is different from the aquatic ecology because it faces scarcity of an essential component that is water. This is an essential component in human health and production of food even to animals (Hagen 197). Prevention strategies should be utilized in full so that aspect of sustainable development is realized in the ecology. Health of this ecosystem is determined by minimal or no pollution for example, global warming or lethal components like green-house gases, which affect the health of food production. The conservation of natural resources is pertinent in determining the health of the ecosystem in that they are vital in food production and health.

The local landholders and users of these vital resources should practice high-level component of corporate, social responsibility. This will build concern on their adventures as well as ecological care, and this leads to sustainable development. This will create beneficial effects in food production in that this sustainability and corporate social responsibility will not threaten or affect the ability of the terrestrial ecology to support its population in terms of food production and health (Chapman, Hart, Cobb, Whitham and Koch 2867)

Policies and regulations on preservation of the ecology should be enacted and practiced in stringent code. For instance, there are laws that regulate emission of carbon to the atmosphere or trading of emissions by factories and companies. This will enhance responsible use of terrestrial natural resources (Odum 1211).  Though this may be accompanied with tough punishments and fines, it may lower the production level due to reduced exploitation of these resources by landholders. Irrespective of food reduction, sustainability is enhanced and may compromise the future ability to produce food.

Terrestrial ecology is essential in sustainability of human life food production. It has physical, biological, and chemical components that interact. The level of this interaction determines the sustainability of the ecosystem. Natural resources like water are of remarkable value in health of terrestrial life.

Works Cited

Chapman Siny, Hart Saten, Cobb Neil, Whitham Geo and Koch Wil. (2003). “Insect herb ivory increases litter quality and decomposition: an extension of the acceleration hypothesis”. Ecology, Vol. 84:2867-2876. 2003. Print

Hagen, John Bil. An Entangled Bank: The origins of ecosystem ecology . New Brunswick New Jersey, Rutgers University Press. 1999. Print

Odum Hein. Environment, Power, and Society . New York, Wiley-Inter science. 2000. Print

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• Biology Article

Terrestrial Ecosystem

A community of living organisms of a particular region living in conjunction with non-living components is called an ecosystem. An ecosystem can be as small as an oasis in a desert, or as big as an ocean.

What is a Terrestrial Ecosystem?

The type of ecosystems which are predominantly found on land are called the terrestrial ecosystems. Terrestrial ecosystems cover approximately 140 to 150 million km 2 , which is about 25 to 30 percent of the total earth surface area.

Read more: Ecosystem

Types Of Terrestrial Ecosystems

There are different types of terrestrial ecosystems, which are widely distributed around the geological zones. They include:

These types of ecosystems include both temperate deciduous forest, plantation forests and tropical rain forests. They serve as a natural habitat for a vast range of living species and also comprise the highest species diversity. Forests cover nearly 30 to 35 million square kilometres of the earth’s surface and more than 300 million species of plants and animals live in forests.

Explore more: Forests

Grasslands are the most dominant type of vegetation and these types of environments occur naturally in several parts of the world. These types of terrestrial ecosystems serve as a home for a wide diversity of animal species, such as elephants, giraffes, hyenas, jackrabbits, lions, rhinos, warthogs and zebras. Other types of grasslands include|:

• Tropical Grasslands
• Temperate Grasslands

Tundra denotes polar regions, which are characterized by harsh environmental conditions similar to deserts and is usually windswept, snow-covered and treeless. Compared to deserts, this type of ecosystem is completely filled with frozen soil throughout the year and in summer, the snow melts and shallow ponds are produced. This gives rise to lichens and a few plants with small and colorful flowers.

The Desert is a barren region of the landscape, which has extremely high or low temperatures and has scarce vegetation. Depending on the climate and temperature, deserts can be classified into hot deserts and cold deserts. There are many lives that are well-adapted to life in the desert. Animals include – Camels, foxes, hyenas, jackals, scorpions, a few varieties of snakes and lizards. The common plants are acacia, cactus and date palms.

Sahara is an example of a hot desert, which is categorized by high temperatures associated with little rainfall and complicated life for both plants and animals.

Ladakh is an example of a cold desert, which is found on the eastern side of Jammu and Kashmir near the Great Himalayas.

Explore more: Terrestrial habitat

This article concludes an introduction to Terrestrial Ecosystems. To know more about the Terrestrial Ecosystem, their types and other related topics and important questions, keep visiting our website at BYJU’S Biology.

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Terrestrial Ecosystem

A terrestrial ecosystem is a land-based community of organisms and the interactions of biotic and abiotic components in a given area. Examples of terrestrial ecosystems include the tundra, taigas, temperate deciduous forests, tropical rainforests, grasslands, and deserts. The type of terrestrial ecosystem found in a particular place is dependent on the temperature range, the average amount of precipitation received, the soil type, and amount of light it receives.

Use these resources to spark student curiosity in terrestrial ecosystems and discover how different abiotic and biotic factors determine the plants and animals found in a particular place.

Biology, Earth Science

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• Published: 22 September 2021

The three major axes of terrestrial ecosystem function

• Mirco Migliavacca   ORCID: orcid.org/0000-0003-3546-8407 1 , 2   nAff55 ,
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• Jacob A. Nelson 1 ,
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• Dennis D. Baldocchi 6 ,
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• Hiroki Ikawa 28 ,
• Ivan A. Janssens   ORCID: orcid.org/0000-0002-5705-1787 24 ,
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• Biogeography
• Ecosystem ecology

The leaf economics spectrum 1 , 2 and the global spectrum of plant forms and functions 3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species 2 . Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities 4 . However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability 4 , 5 . Here we derive a set of ecosystem functions 6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems 7 , 8 .

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Global patterns of plant functional traits and their relationships to climate

Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation

Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning

Terrestrial ecosystems provide multiple functions (for example, resource use and potential uptake of carbon dioxide, among others) and ecosystem services on which society depends 5 . To understand and predict the response mechanisms of ecosystems as a whole to climatic and other environmental changes, it is crucial to establish how many and which functions need to be measured to obtain a good representation of overall ecosystem functioning. So far, the key functional axes that control the behaviour of terrestrial ecosystems have not yet been quantified 5 . This can be achieved by identifying associations between a comprehensive set of ecosystem functions measured consistently across major terrestrial biomes and a range of climatic conditions.

Here, we identify and quantity the major axes of terrestrial ecosystem functions and sources of variation along these axes. First, we characterize multiple ecosystem functions across major terrestrial biomes. Second, we identify the most important axes of variation of ecosystem functions using an exploratory analysis similar to that used for the global spectrum of plant forms and functions 3 . Third, we analyse which variables drive the variation along these axes, from a suite of climatic variables, and the structural and chemical properties of the vegetation. Fourth, we analyse the extent to which two state-of-the-art land surface models (models that simulate the states and exchange of matter and energy between the Earth’s surface and the atmosphere) reproduce the key axes of ecosystem functions. Understanding and quantifying the main axes of variation of the multi-dimensional space of ecosystem functions, their drivers and the degree to which land surface models are able to correctly represent the axes is a crucial prerequisite for predicting which terrestrial functions are the most vulnerable to climate and environmental changes.

We use carbon dioxide (CO 2 ), water vapour (H 2 O), and energy flux data from 203 sites (1,484 site years) from FLUXNET datasets 9 , 10 . These sites cover a wide variety of climate zones and vegetation types (Extended Data Figs. 1 – 3 , Supplementary Table 1 ). A previous report 6 suggested a series of core ecosystem functional properties that can be derived from carbon, water and energy flux observations related to efficiencies or potential rates of key physiological and ecohydrological processes (for example, evapotranspiration, photosynthesis energy partitioning and so on) that control land surface–atmosphere interactions. For each site, we calculated a single set of functional properties (see ‘Calculation of ecosystem functions from FLUXNET’ in Methods for details on the calculation and definition of abbreviations): maximum gross CO 2 uptake at light saturation (GPP s at ), maximum net ecosystem productivity (NEP max ), maximum evapotranspiration (ET max ), evaporative fraction (EF) (that is, the ratio between latent heat flux and available energy, indicative of energy partitioning), EF amplitude (EF ampl ), maximum dry canopy surface conductance ( G smax ), maximum and mean basal ecosystem respiration (Rb max and Rb, respectively), and apparent carbon-use efficiency (aCUE) (that is, the remaining fraction of carbon entering the ecosystem). We also computed several metrics of growing season water-use efficiency (WUE) that account in different ways for physical evaporation and stomatal regulation effects: underlying WUE (uWUE), stomatal slope at ecosystem scale (G1), and WUE t , a second variant of WUE, but based on transpiration estimates 11 (see  Methods ). We calculated average climate and soil water availability variables for each site, encompassing the following: cumulative soil water availability index (CSWI), mean annual precipitation ( P ), mean shortwave incoming radiation (SW in ), mean air temperature ( T air ), and mean vapour pressure deficit during the growing season (VPD). In addition, we compiled information on canopy-scale structural variables such as foliar nitrogen concentration (N%), maximum leaf area index (LAI max ), maximum canopy height ( H c ), and above-ground biomass (AGB), when available (Methods, Supplementary Table 1 ).

The key axes of the multi-dimensional space of terrestrial ecosystem functions were identified using principal component analysis (PCA; see  Methods ). We find that the first three axes of variation (the principal components; PCs) explain 71.8% of the multi-dimensional functional space variation (Fig. 1a, b , Supplementary Information  2 ). The first axis (PC1) explains 39.3% of the variance and is dominated by maximum ecosystem productivity properties, as indicated by the loadings of GPP sat and NEP max , and maximum evapotranspiration (ET max ) (Fig. 1c, d ). Also, Rb contributes with positive loadings to PC1 (Fig. 1d ), indicating the coupling between productivity and ecosystem respiration (both autotrophic and heterotrophic) 12 . The first axis runs from sites with low productivity and evapotranspiration to sites with high photosynthesis, high net productivity, and high maximum evapotranspiration; that is, from cold and arid shrublands and wetlands, to forests in continental, tropical and temperate climates (Fig. 2a, b ). The second axis (PC2) explains 21.4% of the variance and refers to water-use strategies as shown by the loadings of water-use efficiency metrics (uWUE, WUE t , and G1), evaporative fraction and maximum surface conductance (Fig. 1c, d ). Plant functional types do not explain clearly the variability of the second axis, with the exception of the evergreen and mixed forest, and the wetlands that are at the opposite extremes of the range (Fig. 2c ). This axis runs (Fig. 2c,d) from temperate forests, dry and subtropical sites with a low average evaporative fraction (that is, available energy is mainly dissipated by sensible heat) but higher water-use efficiency (Fig. 2d ), to sites in cold or tropical climates, as well as wetlands with a high evaporative fraction (that is, available energy is used for evapotranspiration), high surface conductance and low water-use efficiency (Fig. 2c, d ). The third axis (PC3) explains 11.1% of the variance and includes key attributes that reflect the carbon-use efficiency of ecosystems. PC3 is dominated by apparent carbon-use efficiency (aCUE), basal ecosystem respiration (Rb and Rb max ) and the amplitude of EF (EF ampl ) (Fig. 1c, d ). Rb and aCUE contribute to PC3 with opposite loadings, indicating that the PC3 ranges from sites with high aCUE and low Rb to sites with low aCUE and high Rb. The third axis runs from Arctic and boreal sites with low PC values to hot and dry climates (Fig. 2f ), potentially indicating the imprint of aridity and temperature over the efficiency of ecosystems to use the assimilated carbon. We find no clear relation to plant functional types, with the exception of deciduous and evergreen forests that are at the extremes of the PC3 range (Fig. 2e ).

a , Biplot resulting from the PCA. Different colours of the points represent different plant functional types (PFTs): CSH (closed shrublands); DBF (deciduous broadleaved forest); DNF (deciduous needleleaf forests); EBF (evergreen broadleaved forest); ENF (evergreen needleleaf forest); GRA (grasslands); MF (mixed forest); OSH (open shrublands); SAV (savannah); and WET (wetlands). Bigger points represent the centroid of the distribution for each PFT. b , Explained variance for each principal component. c , d , Bar plots of the contribution ( c ) and loading ( d ) of each ecosystem functional property (EFP) to each principal component. Orange bars represent the loadings and the contributions that are considered significant (Supplementary Information  2 ).

a , c , e , Plant functional types (PFTs). b , d , f , Climate types. Letters represent statistically significant differences in the average PCs (Tukey’s HSD test, P  < 0.05), such that groups not containing the same letter are different. The effect size of the one-way ANOVA ( η 2 ) is reported ( n  = 203 sites). In the box plots the central line represents the mean; the lower and upper box limits correspond to the 25th and 75th percentiles and the upper (lower) whiskers extend to 1.5 (−1.5) times the interquartile range, respectively. Colours indicate different climate types and PFTs (cont, continental; subtrop, subtropical; temp, temperate; trop, tropical; PFT definitions are as in Fig. 1).

We analyse the predictive relative importance of five climatic variables ( T air , VPD, CSWI, P , and SW in ) and four vegetation structural characteristics (LAI max , AGB, H c and N%) on the predictability of the principal components using random forests (see ‘Predictive variable importance’ in Methods). We find that the maximum productivity axis (PC1) is largely explained by vegetation structure (LAI max , AGB, H c  and N%) and VPD (Fig. 3a , Extended Data Fig. 4a–e ). The water-use strategies axis (PC2) is mostly explained by maximum canopy height ( H c ), followed by climate variables (Fig. 3b , Extended Data Fig. 4i–l ). Structural and climate variables jointly explain the variability of the carbon-use efficiency axis (PC3). The most important structural predictors of PC3 are AGB and N%, whereas VPD, T air and SW in are the most important climate drivers (Fig. 3c , Extended Data Fig. 4m–q ).

a – c , Predictive relative importance for PC1 ( a ), PC2 ( b ) and PC3 ( c ). Numbers in the circles represent the percentage increase in mean squared error (MSE). Yellow circles represent vegetation structural variables; light blue circles represent climate variables.

The dependencies described above can only be interpreted causally if the regression models are in fact causal regression models (see Supplementary Information  3 for a formal definition). In many situations, this fails to be the case owing to the existence of hidden confounders; that is, unmeasured variables that influence both the principal components and the covariates (here climate and structural variables) 13 . Using an invariance-based analysis (see ‘Invariant causal regression models and causal variable importance’ in Methods), we find evidence that the full regression model including all the selected structural and climatic variables might be causal (Supplementary Information  3.2.1 , Supplementary Fig. 3.3 ). If this is indeed the case, we can make the following statements. When considering groupwise causal variable importance, we can conclude that vegetation structure is a stronger causal driver than climate of the spatial (that is, across sites) variability of the maximum realized productivity axis (PC1) (Supplementary Fig. 3.7 ), and both are significant (Supplementary Table 3.2 ). Consider two contiguous plots of forest experiencing the same climate conditions, one disturbed and the other not. The undisturbed forest, which is likely to be taller, with higher LAI and carbon stocks, would probably have higher maximum photosynthetic rates and net ecosystem production, which are the most important variables loading on the first axis. Although, in time, the variability of climate controls the variability of gross and net CO 2 uptake and productivity 14 , 15 , which are variables related to the maximum productivity axis (PC1), in space (that is, across sites) we find only a marginal control in very cold and radiation-limited sites (Extended Data Fig. 5a for a PC1 map), or for very warm and high atmospheric aridity (high VPD) conditions (Extended Data Fig. 4d based on predictive variable importance). Both vegetation structure and climate variables seem to have a joint direct causal effect on PC2 (Supplementary Fig  3.7 ). Although vegetation canopy height is constrained by resource availability 16 , particularly water, our results suggest that it acts itself as a control on the water-use strategies axis (PC2) and that it has a stronger causal effect on PC2 than each of the climate variables (Supplementary Fig. 3.6 ). The importance of vegetation height for ecosystem water-use strategies is manifold. First, vegetation height controls the coupling between stomata and atmosphere by influencing surface roughness and then aerodynamic resistance 17 , which modulates leaf-to-air VPD and water use efficiency. Second, vegetation height reflects variation in water-use efficiency that decreases as a consequence of progressive hydraulic constraints on stomatal conductance to water vapour and growth in taller vegetation 16 . Third, canopy height might reflect stand age and it is influenced by disturbances. Studies on forest chronosequence show a more conservative use of water in younger forests, which results in higher water-use efficiency 18 . We cannot exclude that our results are indirectly affected by the gradient from grass to forests, but postulate that these effects are likely to be minimal (Extended Data Fig. 6 ). Vegetation structure has a direct causal effect on the carbon-use efficiency axis (PC3; Supplementary Fig  3.7 ). Previous studies show that vegetation structure reflects climatic constraints but also the successional stage of an ecosystem after disturbance 19 . Increasing stand age—which is typically associated with higher above-ground biomass—is also associated with reduced forest production efficiency 20 . The negative partial dependence of PC3 on above-ground biomass (Extended Data Fig. 4n , based on predictive variable importance) is likely to be related to higher autotrophic and heterotrophic respiration rates per unit of CO 2 taken up by photosynthesis as biomass increases 21 . The positive dependence of PC3 on N% (Extended Data Fig. 4q , based on predictive variable importance) supports previous findings that carbon-use efficiency might be controlled by the nutrient status of the vegetation 22 .

The two representative—yet complementary—land surface models examined here (OCN and JSBACH) partially reproduce the main axes of terrestrial ecosystem functions (Extended Data Fig. 7 ). This is shown when comparing the PCA calculated from FLUXNET data with simulated ecosystem functional properties from 48 site-level runs, mostly in temperate and boreal sites (Extended Data Fig. 7 ). The models are broadly consistent with the FLUXNET observations in the description of the potential productivity axis (PC1), but diverge in the description of the water-use strategies (PC2) and the carbon-use efficiency (PC3) axes. Despite the overall good agreement between observed and modelled fluxes at a half-hourly timescale (Supplementary Table 4 ), we show that, first, models are limited in simulating the relationships between ecosystem functions (Extended Data Fig. 8 ); and, second, models tend to overstate observed correlation strengths among ecosystem functions, as shown by the larger variance explained by the PC1 in models compared to observations (Extended Data Fig  7h, i ). As a result, the ecosystem functional space that can be simulated by the models, represented by the area shown in Extended Data Fig. 9 , is smaller than that expected from observations, particularly in the plane spanned by the PC2 and PC3 (Extended Data Fig. 9d–f ). The limited variability of the model output points to an insufficient representation of the actual variability of the vegetation properties by the average parameterization of plant functional types. Uncertain implementation of plant hydraulics and water acquisition or conservation strategies in land surface models is a key limitation 23 that explains the observed discrepancy in PC2. With regard to PC3, one limitation is that models lack flexibility in representing the response of respiration rates and carbon-use efficiency to climate, nutrients, disturbances and substrate availability (including biomass and stand age) 20 , 24 .

The identification of the key axes of terrestrial ecosystem function and their relationships with climate and vegetation structure will help to support the development of the next generation of land surface models and complement their benchmarking 25 . By comparing the contributions of the functions and their loadings to the principal components, we can assess whether the representations of ecosystem functions in the models and in the ‘real world’ are coherent, and if not, which key processes or model formulations need improvement. For example, we show that vegetation height controls the water-use strategies axis (PC2), which is not well reproduced by the land surface models 23 . This suggests that future land surface models need to include a representation of water-use strategies that explicitly accounts for hydraulic limitations to growth, vegetation stature, vertical and horizontal structures and microenvironments of the canopy, and a refined parameterization of stomatal control. Likewise, the inclusion of a flexible representation of carbon-use efficiency would enable models to reproduce the third axis of ecosystem functions 24 . The comparison of the variances explained by functional axes and the loadings of the functions in simulated and observed data will indicate whether simulated ecosystem functions are appropriately coordinated. The overly tight coupling of ecosystem functions by models indicates a lack of flexibility in ecosystem responses to environmental drivers, such as adaptive carbon and water couplings.

In summary, by analysing a consistent set of ecosystem functions across major terrestrial biomes and climate zones, we show that three key axes capture the terrestrial ecosystem functions. The first and most important axis represents maximum productivity and is driven primarily by vegetation structure, followed by mean climate. The second axis is related to water-use strategies, and is driven by vegetation height. The third axis is related to ecosystem carbon-use efficiency; it is controlled by vegetation structure, but shows a gradient related to aridity. We find that the plant functional type concept does not necessarily capture the variability of ecosystem functions, because the majority of plant functional types are evenly distributed along the water-use strategies (PC2) and carbon-use efficiency (PC3) axes. Our approach allows the overall functioning of terrestrial ecosystems to be summarized and offers a way towards the development of metrics of ecosystem multifunctionality 5 —a measure of ecosystem functions as a whole, which is crucial to achieving a comprehensive assessment of the responses of ecosystems to climate and environmental variability, as well as biodiversity losses 5 . The analysis focuses on relatively few critical functions related to carbon, water and energy cycling of ecosystems. To attain a fully comprehensive characterization of the key axes of terrestrial ecosystem functions, more parameters related to nutrient cycling, seed dispersal and chemical defences—among others—should be included. The concept of the key axes of ecosystem functions could be used as a backdrop for the development of land surface models, which might help to improve the predictability of the terrestrial carbon and water cycle in response to future changing climatic and environmental conditions.

FLUXNET data

The data used in this study belong to the FLUXNET LaThuile 9 and FLUXNET2015 Tier 1 and Tier 2 datasets 10 , which make up the global network of CO 2 , water vapour and energy flux measurements. We merged the two FLUXNET releases and retained the FLUXNET2015 (the most recent and with a robust quality check) version of the data when the site was present in both datasets. Croplands were removed to avoid the inclusion of sites that are heavily managed in the analysis (for example, fertilization and irrigation).

The sites used cover a wide variety of climate zones (from tropical to Mediterranean to Arctic) and vegetation types (wetlands, shrublands, grasslands, savanna, evergreen and deciduous forests). It should be noted though that tropical forests are underrepresented in the FLUXNET database (Extended Data Figs. 1 , 3 ).

Sites were excluded in cases in which: (i) data on precipitation or radiation were not available or completely gap-filled; (ii) the calculation of functional properties failed because of low availability of measured data (see ‘Calculation of ecosystem functions from FLUXNET’); and (iii) fluxes showed clear discontinuities in time series indicating a change of instrumentation set-up (for example, changes in the height of the ultrasonic anemometer or gas analyser).

The final number of sites selected was 203 (1,484 site years). The geographical distribution is shown in Extended Data Fig. 1 , the distribution in the climate space is shown in Extended Data Fig. 2 and the fraction of sites for each climate classes is reported in Extended Data Fig. 3 .

For each site, we downloaded the following variables at half-hourly temporal resolution: (i) gross primary productivity (GPP, μmol CO 2 m – 2 s – 1 ) derived from the night-time flux partitioning 26 (GPP_NT_VUT_50 in FLUXNET 2015 and GPP_f in LaThuile), (ii) net ecosystem exchange (NEE, μmol CO 2 m – 2 s – 1 ) measurements filtered using annual friction velocity ( u* , m s − 1 ) threshold (NEE_VUT_50 in FLUXNET 2015; NEE in LaThuile); (iii) latent heat (LE, W m −2 ) fluxes, which were converted to evapotranspiration (ET, mm); (iv) sensible heat ( H , W m − 2 ) fluxes; (v) air temperature ( T air , °C); (vi) vapour pressure deficit (VPD, hPa); (vii) global shortwave incoming radiation (SW in , W m −2 ); viii) net radiation ( R n , W m −2 ); (ix) ground heat flux ( G , W m −2 ); (x) friction velocity u * (m s −1 ); and (xi) wind speed ( u , m s −1 ). For the energy fluxes ( H , LE) we selected the fluxes not corrected for the energy balance closure to guarantee consistency between the two FLUXNET datasets (in the LaThuile dataset energy fluxes were not corrected).

The cumulative soil water index (CSWI, mm) was computed as a measure of water availability according to a previous report 27 . Half-hourly values of transpiration estimates ( T , mm) were calculated with the transpiration estimation algorithm (TEA) 28 . The TEA has been shown to perform well against both model simulations and independent sap flow data 28 .

For 101 sites, ecosystem scale foliar N content (N%, gN 100 g −1 ) was computed as the community weighted average of foliar N% of the major species at the site sampled at the peak of the growing season or gathered from the literature 29 , 30 , 31 , 32 . Foliar N% for additional sites was derived from the FLUXNET Biological Ancillary Data Management (BADM) product and/or provided by site principal investigators (Supplementary Table 1 , Extended Data Fig. 1 ). It should be noted that this compilation of N% data might suffer from uncertainties resulting from the scaling from leaves to the eddy covariance footprint, the sampling strategy (including the position along the vertical canopy profile), the species selection and the timing of sampling. About 30% of the data comes from a coordinated effort that minimized these uncertainties 29 , 30 , and for the others we collected N% data that were representative for the eddy covariance footprint 31 , 32 .

Maximum leaf area index (LAI max , m 2  m −2 ) and maximum canopy height ( H c , m) were also collected for 153 and 199 sites, respectively, from the literature 32 , 33 , the BADM product, and/or site principal investigators.

Earth observation retrievals of above-ground biomass (AGB, tons of dry matter per hectare (t DM ha −1 )) were extracted from the GlobBiomass dataset 34 at its original resolution (grid cell 100 × 100 m) for each site location. All the grid cells in a 300 × 300 m and 500 × 500 m window around each location were selected to estimate the median and 95th percentiles of AGB for each site. The median of AGB was selected to avoid the contribution of potential outliers to the expected value of AGB. The analysis further explored the contribution of higher percentiles in the local variation of AGB as previous studies have highlighted the contribution of older and larger trees in uneven stand age plots to ecosystem functioning 35 . According to the evaluation against AGB measured at 71 FLUXNET sites (Extended Data Fig. 10 ), we decided to use the product with median AGB values extracted from the 500 × 500 m window.

A total of 94 sites have all the data on vegetation structure (N%, LAI max , H c , and AGB).

The list of sites is reported in Supplementary Table 1 along with the plant functional type (PFT), Köppen-Geiger classification, coordinates, and when available N%, LAI max , H c and AGB.

In this study we did not make use of satellite information, with the exception of the AGB data product. Future studies will benefit from new missions such as the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), the fluorescence explorer (FLEX), hyperspectral, and radar and laser detection and ranging (LiDAR) missions (for example, Global Ecosystem Dynamics Investigation (GEDI)), to characterize a multivariate space of structural and functional properties.

Calculation of ecosystem functions from FLUXNET

Starting from half-hourly data, we calculated at each site a single value for each of the ecosystem functions listed below. For the calculations of functional properties we used, unless otherwise indicated, good-quality data: quality flag 0 (measured data) and 1 (good-quality gap-filled data) in the FLUXNET dataset.

Gross primary productivity at light saturation (GPP sat )

GPP at light saturation using photosynthetically active radiation as driving radiation and 2,000 μmol m −2  s −1 as saturating light. GPP sat represents the ecosystem-scale maximum photosynthetic CO 2 uptake 15 , 30 , 36 . The GPP sat was estimated from half-hourly data by fitting the hyperbolic light response curves with a moving window of 5 days and assigned at the centre of the moving window 30 , 37 . For each site the 90th percentile from the GPP sat estimates was then extracted.

Maximum net ecosystem productivity (NEP max )

This was computed as the 90th percentile of the half-hourly net ecosystem production (NEP = −NEE) in the growing season (that is, when daily GPP is higher than 30% of the GPP amplitude). This metric represents the maximum net CO 2 uptake of the ecosystem.

Basal ecosystem respiration (Rb and Rb max )

Basal ecosystem respiration at reference temperature of 15  ° C was derived from night-time NEE measurements 26 . Daily basal ecosystem respiration (Rb d ) was derived by fitting an Arrhenius type equation over a five-day moving window and by keeping the sensitivity to temperature parameter ( E 0 ) fixed as in the night-time partitioning algorithms 26 , 38 . Rb d varies across seasons because it is affected by short-term variations in productivity 33 , 39 , phenology 40 and water stress 41 . For each site, the mean of the Rb d (Rb) and the 95 th percentile (Rb max ) were computed. The calculations were conducted with the REddyProc R package v.1.2.2 (ref.  38 ).

Apparent carbon-use efficiency (aCUE)

The aCUE as defined in this study is the efficiency of an ecosystem to sequester the carbon assimilated with photosynthesis 39 . aCUE is an indication of the proportion of respired carbon with respect to assimilated carbon within one season. A previous report 6 showed that little of the variability in aCUE can be explained by climate or conventional site characteristics, and suggested an underlying control by plant, faunal and microbial traits, in addition to site disturbance history. Daily aCUE (aCUE d ) is defined as aCUE d  = 1 − (Rb d /GPP d ), where GPP d is daily mean GPP and Rb d is derived as described above. For each site, aCUE was computed as the median of aCUE d .

Metrics of water-use efficiency (WUE)

Various metrics of WUE are described below: stomatal slope or slope coefficient (G1), underlying water-use efficiency (uWUE), and water-use efficiency based on transpiration (WUE t ). The three metrics were used because they are complementary, as shown in previous studies 11 , 42 .

Stomatal slope or slope coefficient (G1)

This is the marginal carbon cost of water to the plant carbon uptake. G1 is the key parameter of the optimal stomatal model derived previously 43 . G1 is inversely related to leaf-level WUE. At leaf level, G1 is calculated using nonlinear regression and can be interpreted as the slope between stomatal conductance and net CO 2 assimilation, normalized for VPD and CO 2 concentration 43 . A previous report 42 showed the potential of the use of G1 at ecosystem scale, where stomatal conductance is replaced by surface conductance ( G s ), and net assimilation by GPP. The methodology is implemented in the bigleaf R package 44 . The metric was computed in the following situations: (i) incoming shortwave radiation (SW in ) greater than 200 W m −2 ; (ii) no precipitation event for the last 24 h 45 , when precipitation data are available; and (iii) during the growing season: daily GPP > 30% of its seasonal amplitude 44 .

Underlying water-use efficiency (uWUE)

The underlying WUE was computed following a previous method 46 . uWUE is a metric of water-use efficiency that is negatively correlated to G1 at canopy scale 44 :

uWUE was calculated using the same filtering that was applied for the calculation of G1. The median of the half-hourly retained uWUE values was computed for each site and used as a functional property.

Water-use efficiency based on transpiration (WUE t )

The WUE based on transpiration ( T ) was computed to reduce the confounding effect resulting from soil evaporation 11 , 28 :

where T is the mean annual transpiration calculated with the transpiration estimation algorithm (TEA) developed by in a previous study 28 and GPP is the mean annual GPP.

Maximum surface conductance ( G smax )

Surface conductance ( G s ) was computed by inverting the Penman–Monteith equation after calculating the aerodynamic conductance ( G a ).

Among the different formulations of G a (m s – 1 ) in the literature, we chose to use here the calculation of the canopy (quasi-laminar) boundary layer conductance to heat transfer, which ranges from empirical to physically based (for example, ref. 47 ). Other studies 48 , 49 suggested an empirical relationship between G a , the horizontal wind speed ( u ) and the friction velocity, u *:

G s (m s −1 ) is computed by inverting the Penman–Monteith equation:

where Δ is the slope of the saturation vapour pressure curve (kPa K −1 ), ρ is the air density (kg m −3 ), C p is the specific heat of the air (J K −1  kg −1 ), γ is the psychrometric constant (kPa K −1 ), VPD (kPa), R n (W m −2 ), G (W m −2 ) and S is the sum of all energy storage fluxes (W m −2 ) and set to 0 as not available in the dataset. When not available, G also was set to 0.

G s represents the combined conductance of the vegetation and the soil to water vapour transfer. To retain the values with a clear physiological interpretation, we filtered the data as we did for the calculation of G1.

For each site, the 90th percentile of the half-hourly G s was calculated and retained as the maximum surface conductance of each site ( G smax ). G s was computed using the bigleaf R package 44 .

Maximum evapotranspiration in the growing season (ET max )

This metric represents the maximum evapotranspiration computed as the 95th percentile of ET in the growing season and using the data retained after the same filtering applied for the G1 calculation.

Evaporative fraction (EF)

EF is the ratio between LE and the available energy, here calculated as the sum of H  + LE (ref. 50 ). For the calculation of EF, we used the same filtering strategy as for G1. We first calculated mean daytime EF. We then computed  the EF per site as the growing season average of daytime EF. We also computed the amplitude of the EF in the growing season by calculating the interquartile distance of the distribution of mean daytime EF (EF ampl ).

Principal component analysis

A PCA was conducted on the multivariate space of the ecosystem functions. Each variable (ecosystem functional property, EFP) was standardized using z -transformation (that is, by subtracting its mean value and then dividing by its standard deviation). From the PCA results we extracted the explained variance of each component and the loadings of the EFPs, indicating the contribution of each variable to the component. We performed the PCA using the function PCA() implemented in the R package FactoMineR 51 .

We justify using PCA over nonlinear methods because it is an exploratory technique that is highly suited to the analysis of the data volume used in this study, whereas other nonlinear methods applied to such data would be over-parameterized. For the same reason, PCA was used in previous work concerning the global spectrum of leaf and plant traits, and fluxes 1 , 3 , 52 .

To test the significance of dimensionality of the PCA, we used a previously described methodology 53 . We used the R package ade4 (ref. 54 ) and evaluated the number of significant components of the PCA to be retained to minimize both redundancy and loss of information (Supplementary Information  2 ). We tested the significance of the PCA loadings using a combination of the bootstrapped eigenvector method 55 and a threshold selected using the number of dimensions 56 (Supplementary Information  2 ).

Predictive variable importance

A random forests (RF) analysis was used to identify the vegetation structure and climate variables that contribute the most to the variability of the significant principal components, which were identified with the PCA analysis (see ‘Principal component analysis’). In the main text we refer to the results of this analysis as ‘predictive variable importance’ to distinguish this to the ‘causal variable importance’ described below.

The analysis was conducted using the following predictor variables: as structural variables, N% (gN 100 g −1 ), LAI max (m 2  m −2 ), AGB (t DM ha −1 ) and H c (m); as climatic variables, mean annual precipitation ( P , mm), mean VPD during the growing season (VPD, hPa), mean shortwave radiation (SW in , W m −2 ), mean air temperature ( T air , °C); and the cumulative soil water index (CSWI, −), as indicator of site water availability.

We used partial dependencies of variables to assess the relationship between individual predictors and the response variable (that is, PC1, PC2 and PC3).

The results from the partial dependency analysis can be used to determine the effects of individual variables on the response, without the influence of the other variables. The partial dependence function was calculated using the pdp R package 57 .

The partial dependencies were calculated restricted to the values that lie within the convex hull of their training values to reduce the risk of interpreting the partial dependence plot outside the range of the data (extrapolation).

Invariant causal regression models and causal variable importance

We have quantified the dependence of the principal components on the different structural and climatic variables using nonlinear regression. Such dependencies can only be interpreted causally if the regression models are in fact causal regression models (see Supplementary Information  3 for a formal definition), which may not be the case if there are hidden confounders. To see whether the regression models allow for a causal interpretation, we use invariant causal prediction 58 . This method investigates whether the regression models are stable with respect to different patterns of heterogeneity in the data, encoded by different environments (that is, subsets of the original dataset). The rationale is that a causal model, describing the full causal mechanism for the response variable, should be invariant with respect to changes in the environment if the latter does not directly influence the response variable 13 , 59 . Other non-causal models may be invariant, too, but a non-invariant model cannot be considered causal.

How to choose the environments is a modelling choice that must satisfy the following criteria. First, it should be possible to assign each data point to exactly one environment. Second, the environments should induce heterogeneity in the data, so that, for example, the predictor variables have different distributions across environments. Third, the environments must not directly affect the response variable, only via predictors, although the distribution of the response may still change between environments. The third criterion can be verified by expert knowledge and is assumed to hold for our analysis. In addition, if it is violated, then, usually, no set is invariant 58 , which can be detected from data.

In our analysis, we assigned each data point (that is, each site) to one of two environments (two subsets of the original dataset): the first includes forest sites in North America, Europe or Asia; and the second includes non-forest and forest ecosystems from South America, Africa or Oceania, and non-forest ecosystems from North America, Europe or Asia (see Supplementary Information  3.1.3.1 for details). Our choice satisfies the method’s assumption that the distribution of the predictors is different between the two environments (that is, they induce heterogeneity in the data; see Supplementary Fig. 3.1 ). Environments that are too small or too homogeneous do not provide any evidence against the full set of covariates being a candidate for the set of causal predictors. Other choices of environments than the one presented here yield consistent results (Supplementary Information  3.2.1 , Supplementary Fig. 3.4 ).

For each subset of predictors, we test whether the corresponding regression model is invariant (yielding the same model fit in each environment). Although many models were rejected and considered non-invariant, the full model (with all the nine predictors and used in the predictive variable importance analysis) was accepted as invariant, establishing the full set of covariates as a reasonable candidate for the set of direct causal predictors. We used both RF (randomForest package in R 60 ) and generalized additive models, GAM 61 (mgcv package 62 in R) to fit the models. Both methods lead to comparable results but with a better average performance of the RF: GAM led to slightly better results than RF for PC1, whereas for PC2 and PC3 RF showed a much better model performance (Supplementary Table 3.1 , Supplementary Information  3.2.2 ). Therefore, in the main text we showed only the results from the RF (except for PC1).

If, indeed, the considered regression models are causal, this allows us to make several statements. First, we can test for the existence of causal effects by testing for statistical significance of the respective predictors in the fitted models. Second, we can use the response curves of the fitted model to define a variable importance measure with a causal interpretation. In the main text we refer to this variable importance as ‘causal variable importance’. For details, see Supplementary Information  3.1.2 . More formally, we considered the expected value of the predicted variables (the principal components) under joint interventions on all covariates (AGB, H c , LAI max , N%, T air , VPD, SW in , CSWI and P ) at once, and then, to define the importance, we quantified how this expected value depends on the different covariates. We applied the same analysis to groups of vegetation structural and climate covariates (see ‘Groupwise variable importance’ in Supplementary Information  3 .1.2.3, 3.2.3 ).

The details of the methodology and the results are described in Supplementary Information  3 , in which we also provide further details on the choice of environment variable and on the statistical tests that we use to test for invariance. An overview of the invariance-based methodology is shown in Supplementary Fig. 3.1 .

Land surface model runs

We run two widely used land surface models: Orchidee-CN (OCN) and Jena Scheme for Biosphere Atmosphere Coupling in Hamburg (JSBACH):

The dynamic global vegetation model OCN is a model of the coupled terrestrial carbon and nitrogen cycles 63 , 64 , derived from the ORCHIDEE land surface model. It operates at a half-hourly timescale and simulates diurnal net carbon, heat and water exchanges, as well as nitrogen trace gas emissions, which jointly affect the daily changes in leaf area index, foliar nitrogen, and vegetation structure and growth. The main purpose of the model is to analyse the longer-term (interannual to decadal) implication of nutrient cycling for the modelling of land–climate interactions 64 , 65 . The model can run offline, driven by observed meteorological parameters, or coupled to the global circulation model.

JSBACH v.3 is the land surface model of the MPI Earth System Model 66 , 67 . The model operates at a half-hourly time step and simulates the diurnal net exchange of momentum, heat, water and carbon with the atmosphere. Daily changes in leaf area index and leaf photosynthetic capacity are derived from a prognostic scheme assuming a PFT-specific set maximum leaf area index and a set of climate responses modulating the seasonal course of leaf area index. Carbon pools are prognostic allowing for simulating the seasonal course of net land–atmosphere carbon exchanges.

We selected OCN and JSBACH because they are widely used land surface models with different structures. JSBACH is a parsimonious representation of the terrestrial energy, water and carbon exchanges used to study the coupling of land and atmosphere processes in an Earth system model 67 . OCN has also been derived from the land surface model ORCHIDEE 68 , but it includes a more comprehensive representation of plant physiology, including a detailed representation of the tight coupling of the C and N cycling 63 . Both models contribute to the annual global carbon budget of the Global Carbon Project 69 and have shown good performance compared to a number of global benchmarks. OCN was further used in several model syntheses focused on the interaction between changing N deposition and CO 2 fertilization 70 , 71 , 72 . Both OCN and JSBACH can operate at a half-hourly timescale and simulate net and gross carbon exchanges, water and energy fluxes, and therefore are ideal for the extraction of ecosystem functional properties, as done with the eddy covariance data.

The models were driven by half-hourly meteorological variables (shortwave and longwave downward flux, air temperature and humidity, precipitation, wind speed and atmospheric CO 2 concentrations) observed at the eddy covariance sites. OCN was furthermore driven by N deposition fields 73 . Vegetation type, soil texture and plant available water were prescribed on the basis of site observations, but no additional site-specific parameterization was used. Both models were brought into equilibrium with respect to their ecosystem water storage and biogeochemical pools by repeatedly looping over the available site years. We added random noise (mean equal to 0 and standard deviation of 5% of the flux value) to the fluxes simulated by the models to mimic the random noise of the eddy covariance flux observations. An additional test conducted without noise addition showed only a marginal effect on the calculations of the functional properties and the ecosystem functional space.

We used runs of the JSBACH and OCN model for 48 FLUXNET sites (Supplementary Table 1 ). The simulated fluxes were evaluated against the observation to assess the performance of the models at the selected sites. From the model outputs and from each site we derived the ecosystem functions using the same methodology described above. Then the PCA analysis was performed on the three datasets (FLUXNET, OCN and JSBACH) and restricted to the 48 sites used to run the models. We ran the models only on the subset of sites for which the information for the parameterization and high-quality forcing was available. However, the different ecosystem functions emerge from the model structure and climatological conditions. Therefore, even with a smaller set of site we can evaluate whether models reproduce the key dimensions of terrestrial ecosystem function by comparing the PCA results from FLUXNET and the model runs.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this paper.

Data availability

Data used for this study are the FLUXNET dataset LaThuile ( https://fluxnet.fluxdata.org/data/la-thuile-dataset/ ) and FLUXNET2015 ( https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/ ). Biological, ancillary, disturbance and metadata information for the sites were collected from databases and the literature and are available at the following address together with the reproducible workflow ( https://doi.org/10.5281/zenodo.5153538 ). OCN and JSBACH model runs are available in the reproducible workflow ( https://doi.org/10.5281/zenodo.5153538 ).

Code availability

The R codes used for this analysis are available at: https://doi.org/10.5281/zenodo.5153538 . The R codes for the causality analysis are available at: https://doi.org/10.5281/zenodo.5153534 . The TEA algorithm is available at https://doi.org/10.5281/zenodo.3921923 .

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Acknowledgements

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 721995. M.M. and M. Reichstein acknowledge the Alexander Von Humboldt Foundation for funding with the Max Planck Research Prize 2013 to M. Reichstein. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, Swiss FluxNet, TCOS-Siberia and USCCC. The ERA-Interim reanalysis data were provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, the AmeriFlux Management Project and the Fluxdata project of FLUXNET, with the support of the CDIAC and the ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. R.C. and J. Peters were supported by the VILLUM FONDEN (18968) and J. Peters in addition by the Carlsberg Foundation; P.R. acknowledges funding support from the US National Science Foundation (NSF) Long-Term Ecological Research (DEB-1831944) and Biological Integration Institutes (NSF-DBI-2021898); N.B. acknowledges funding from various SNF projects, including ICOS-CH (20FI21_148992, 20FI20_173691), the ETH Board and ETH Zurich (TH-1006-02); and T.F.K. acknowledges support from the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA), which is sponsored by the Regional and Global Model Analysis (RGMA) Program of the Office of Biological and Environmental Research (BER) in the US Department of Energy Office of Science. OzFlux is supported by the Australian Government’s Terrestrial Ecosystem Research Network (TERN, www.tern.org.au ). We thank K. Morris and S. Paulus for comments on the draft, K. Blakeslee for English editing and G. Bohrer for sharing nitrogen data for his site.

Open access funding provided by Max Planck Society.

Author information

Mirco Migliavacca

Present address: European Commission, Joint Research Centre (JRC), Ispra, Italy

Jürgen Knauer

Present address: Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

Authors and Affiliations

Max Planck Institute for Biogeochemistry, Jena, Germany

Mirco Migliavacca, Talie Musavi, Miguel D. Mahecha, Jacob A. Nelson, Silvia Caldararu, Nuno Carvalhais, Tarek S. El-Madany, Ulisse Gomarasca, Mathias Göckede, Martin Jung, Jens Kattge, David Martini, Daniel E. Pabon-Moreno, Ulrich Weber, Sönke Zaehle & Markus Reichstein

German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany

Mirco Migliavacca, Miguel D. Mahecha, Jens Kattge & Markus Reichstein

Remote Sensing Center for Earth System Research, Leipzig University, Leipzig, Germany

Miguel D. Mahecha & Guido Kraemer

Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

Miguel D. Mahecha

CSIRO Oceans and Atmosphere, Canberra, Australian Capital Territory, Australia

Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA, USA

Dennis D. Baldocchi & Trevor F. Keenan

Department of Forest Engineering, ERSAF Research Group, University of Cordoba, Cordoba, Spain

Oscar Perez-Priego

Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark

Rune Christiansen & Jonas Peters

Environment and Sustainability Institute, University of Exeter, Penryn, UK

Karen Anderson

Department of Ecology, University of Innsbruck, Innsbruck, Austria

Michael Bahn & Georg Wohlfahrt

Faculty of Land and Food Systems, Vancouver, British Columbia, Canada

T. Andrew Black

Department of Geography, University of Colorado, Boulder, CO, USA

Peter D. Blanken

Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France

Damien Bonal

Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland

Nina Buchmann & Sebastian Wolf

Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), Paterna, Spain

Arnaud Carrara

Departamento de Ciências e Engenharia do Ambiente, Universidade Nova de Lisboa, Caparica, Portugal

Nuno Carvalhais

European Commission, Joint Research Centre (JRC), Ispra, Italy

Alessandro Cescatti

Landscape Ecology & Ecosystem Science (LEES) Lab, Center for Global Change and Earth Observations, and Department of Geography, Environmental and Spatial Science, Michigan State University, East Lansing, MI, USA

Jiquan Chen

School of Life Sciences, University of Technology Sydney, Ultimo, New South Wales, Australia

Jamie Cleverly

Terrestrial Ecosystem Research Network, College of Science and Engineering, James Cook University, Cairns, Queensland, Australia

Climate Change Unit, Environmental Protection Agency of Aosta Valley, Aosta, Italy

Edoardo Cremonese, Gianluca Filippa & Marta Galvagno

Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, USA

Ankur R. Desai

O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN, USA

Martha M. Farella

Research Group Plant and Ecosystems (PLECO), Department of Biology, University of Antwerp, Wilrijk, Belgium

Marcos Fernández-Martínez & Ivan A. Janssens

Institute of Photogrammetry and Remote Sensing, TU Dresden, Dresden, Germany

Matthias Forkel

Department of Biology, Virginia Commonwealth University, Richmond, VA, USA

Christopher M. Gough

Department of Environmental Engineering, Technical University of Denmark (DTU), Kongens Lyngby, Denmark

Andreas Ibrom

Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan

Hiroki Ikawa

Earth and Environmental Science Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Trevor F. Keenan

Bioclimatology, Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Goettingen, Germany

Alexander Knohl

Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, Germany

Research Institute for Global Change, Institute of Arctic Climate and Environment Research, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan

Hideki Kobayashi

Image Processing Laboratory (IPL), Universitat de València, València, Spain

Guido Kraemer

Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, USA

Beverly E. Law

Centre for Tropical, Environmental, and Sustainability Sciences, James Cook University, Cairns, Queensland, Australia

Michael J. Liddell

College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China

Xuanlong Ma

Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland

Ivan Mammarella

CSIRO Land and Water, Floreat, Western Australia, Australia

Craig Macfarlane

Consiglio Nazionale delle Ricerche, Istituto per la BioEconomia (CNR – IBE), Sesto Fiorentino, Italy

Giorgio Matteucci

Facoltà di Scienze e Tecnologie, Libera Universita’ di Bolzano, Bolzano, Italy

Leonardo Montagnani

Forest Services of the Autonomous Province of Bozen-Bolzano, Bolzano, Italy

Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Milan, Italy

Cinzia Panigada & Micol Rossini

Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy

Dario Papale

Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

Elise Pendall, Peter B. Reich & Ian J. Wright

CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain

Josep Penuelas

CREAF, Barcelona, Spain

Department of Biology, Indiana University, Bloomington, IN, USA

Richard P. Phillips

Department of Forest Resources, University of Minnesota, Saint Paul, MN, USA

Peter B. Reich

Institute for Global Change Biology and School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel

Eyal Rotenberg & Dan Yakir

Southwest Watershed Research Center, USDA Agricultural Research Service, Tucson, AZ, USA

Russell L. Scott

INRAE, UMR EcoFoG, CNRS, Cirad, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France

Clement Stahl

Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia

Ian J. Wright

Michael-Stifel-Center Jena for Data-driven and Simulation Science, Friedrich-Schiller-Universität Jena, Jena, Germany

Markus Reichstein

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Contributions

M.M., M. Reichstein, M.D.M. and T.M. conceived the study. M.M. and T.M. performed the majority of the analysis. R.C. and J. Peters designed and coded the causality analysis. J.A.N. provided the transpiration partitioning data. J. Knauer and S.Z. performed the land surface model runs. N.C. and U.W. processed the above-ground biomass data. O.P.-P. provided support with data analysis and discussions. M.M. wrote the first draft. All of the authors participated in intensive discussions on the manuscript and the revision phase, and contributed to writing the final manuscript. In addition, many site principal investigators contributed with additional data for their site.

Corresponding authors

Correspondence to Mirco Migliavacca or Markus Reichstein .

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Competing interests.

The authors declare no competing interests.

Additional information

Peer review information Nature thanks J. Hans C. Cornelissen, Diego Miralles and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended data fig. 1 map of the 203 fluxnet sites used in this analysis..

Colours represent different plant functional types according to the IGBP classification. IGBP classes are: CSH (close shrublands); DBF (deciduous broadleaved forest), DNF (deciduous needleleaf forests), EBF (evergreen broadleaved forest), ENF (evergreen needleleaf forest), GRA (grasslands), MF (mixed forest), OSH (open shrublands), SAV (savannah), and WET (wetlands). The map was generated with the ggplot2 R package 74 . The shape files used to create the maps were downloaded from https://github.com/ngageoint/geopackage-js .

Extended Data Fig. 2 FLUXNET sites used in the analysis plotted in the precipitation–temperature space.

The background represent climate space of the major biomes according to Whittaker 75 and further modifications 76 . Biomes are defined as function of the mean annual temperature and mean annual precipitation (MAP). The figure is modified from Liu et al., 77 using the code available in git ( https://github.com/kunstler/BIOMEplot ).

Extended Data Fig. 3 Distribution of the selected FLUXNET sites within the climate types.

Climate types were defined according to Köppen-Geiger classification as follow: Tropical (Aw, Af, Am), Dry (BSh, BSk, BWh), Temperate (Cfb), Sub-Tropical (Cfa, Csa, Csb, Cwa), Temperate/Continental Hot (Dfa, Dfb, Dwa, Dwb, Dwc), Arctic (ET)], and Boreal (Dfc, Dsc).

Extended Data Fig. 4 Results of the relative importance analysis conducted with the Random Forest and partial dependence.

See ‘Predictive variable importance’ in Methods. The slopes of the partial dependence plot indicate the sensitivity of the response (PCs) to the specific predictor. The out-of-bag cross-validation leads to predictive explained variance of 56.76% for PC1, 30.24% for PC2, and 20.41% for PC3. The portion of unexplained variance might be related to missing leaf traits predictor such as leaf mass per area or phenological traits. The partial dependence plots of all variables are shown: top panels for PC1 (a–e), middle panels for PC2 (f–l), and bottom panels for PC3 (m–q). The blue lines represent the locally estimated scatterplot (LOESS) smoothing of the partial dependence. Tick marks in the x axis represent the minimum, maximum and deciles of the variable distribution.

Extended Data Fig. 5 Map of FLUXNET sites colour-coded for the value of PC1 and PC2.

a , PC1. b , PC2. The map of the PC1 shows the areas of the globe with high productivity (positive values of PC1 in the temperate areas, Eastern North America, Eastern Asia, and Tropics), and areas characterized by lower productivity (Semi-arid regions, high latitude and Mediterranean ecosystems). The map of the PC2 shows the gradient of evaporative fraction and the spatial patterns of water use efficiency. This PC2 runs from sites with a high evaporative fraction (i.e. available energy is dissipated preferentially to evaporated or transpired water), high surface conductance, and low water use efficiency (positive PC2 values), to water limited sites (i.e. low evaporative fraction where available energy is mainly dissipated by sensible heat) that also show higher water-use efficiency (negative PC2 values). The maps were generated with the ggplot2 R package 74 . The shape files used to create the maps were downloaded from https://github.com/ngageoint/geopackage-js .

Extended Data Fig. 6 Biplot resulting from the principal component analysis.

Plot as in Fig. 1. In panel a, points are colour-coded by grass vs. non-grass classes. In panel b, the points are colour-coded according to the logarithm of vegetation height. From these results we conclude that there is not a clear cluster in the biplot for grass and non-grass vegetation. In fact, in Extended Data Fig. 6a , the sites do not cluster according to the designation to grasslands or not, but there is a clear gradient as a function of the vegetation height (Extended Data Fig. 6b ).

Extended Data Fig. 7 Comparing observed and modelled global ecosystem functional trade-offs.

PCA for a subset of 48 FLUXNET sites mainly distributed in temperate and boreal regions and 2 different land surface models (Supplementary Table 1 ). The left column is FLUXNET, the centre column is OCN, and the right column is JSBACH. Panels a, b, c: the biplot resulting from the PCA. Panels d, e, f, bar plot of the loading of each ecosystem functional property to each principal component. Orange bars represent the loadings that are selected as significant and with high contribution (Supplementary Information  2 ). Panels g, h, i report the variance explained by each principal component. EFP acronym list: apparent carbon-use efficiency (aCUE), evaporative fraction (EF), amplitude of EF (EF ampl ), maximum evapotranspiration (ET max ), gross primary productivity at light saturation (GPP sat ), maximum surface conductance ( G smax ), maximum net ecosystem productivity (NEP max ), maximum and mean basal ecosystem respiration (Rb max and Rb, respectively), and growing season underlying water-use efficiency (uWUE). Note that the PCA results for FLUXNET (panels a, d, g) are different from Fig. 1 because here we use the subset of 48 sites used for the modelling analysis.

Extended Data Fig. 8 Pairwise relationship between some key ecosystem functional properties derived from FLUXNET, and modelled with JSBACH and OCN.

n  = 48 sites; see Supplementary Table 1. The grey areas represent the 95% confidence interval of the linear and nonlinear regression. Overall the correlation between modelled functions is larger than in the observations. Acronym list: evaporative fraction (EF), amplitude of EF (EF ampl ), gross primary productivity at light saturation (GPP sat ), maximum surface conductance ( G s ), maximum net ecosystem productivity (NEP max ), basal ecosystem respiration (Rb), and growing season underlying water-use efficiency (uWUE).

Extended Data Fig. 9 Representation of the 2D ecosystem functional properties space derived from FLUXNET observations and land surface model runs (OCN, JSBACH).

The points represent the principal component (PC) value calculate for each site. The contour lines are computed using a 2D kernel density estimates. The contour lines show the area occupied by ecosystem functional properties and its boundary that, according to the results of the analysis, are set by vegetation characteristics (PC1), water availability, abiotic limitations, and vegetation height (PC2), and above-ground biomass, foliar nitrogen and atmospheric aridity (PC3). The areas computed for FLUXNET are wider than for the models, particularly for PC2 and PC3. This means that ecosystem functional properties as simulated by models are more constrained than for the observations.

Extended Data Fig. 10 Evaluation of above-ground biomass satellite products against FLUXNET observation.

n  = 71. We evaluated the three above-ground biomass (AGB, t DM ha −1 ) products derived from the GlobBiomass dataset as reported in the Method section. From the product at its original resolution (100 x 100 m) we extracted the 95th percentile of the estimated AGB in 5 by 5 grid cell windows (AGB5x5, panel a with all sites, and panel b with the grasslands excluded) centered around the location of the FLUXNET sites used for the evaluation. Further, we extracted the median in 3 by 3 and 5 by 5 grid cells centered around the location of the FLUXNET site (panels c and d). Total above-ground biomass observations were gathered from the BADM dataset downloaded from the AMERIFLUX network and from the FLUXNET LaThuile release. Only data with the clear indication of the unit of AGB expressed in in dry matter (t DM ha −1 ) were retained for the analysis. Results show that the median of the 5 by 5 grid cell window (panel c) is the best extraction method to characterize AGB at the FLUXNET sites, and therefore retained for further analysis. Adjusted determination coefficient (R 2 ), linear regression function, and p-value calculated with the F-test are also reported.

Supplementary information

Supplementary information 2.

Significance test of the PCA and information redundancy: We report the number of significant axes to be retained in the PCA analysis and summarize the results of the statistical analysis in Table S2.

Reporting Summary

Supplementary information 3.

Invariant causal regression models and causal variable importance. This section contains theoretical concepts and a detailed description of the methods used in the causality analysis, and additional results.

Supplementary Table 1

List of FLUXNET sites used in the analysis. Coordinates (latitude and longitude), plant functional type (IGBP class), Köppen Geiger class, nitrogen content (N%), maximum leaf area index (LAI max ), maximum vegetation height ( H c ), and above-ground biomass from the GlobBiomass dataset (AGB) are reported.

Supplementary Table 4

Evaluation of land surface model performances. We report an additional evaluation of the land surface model outputs.

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Migliavacca, M., Musavi, T., Mahecha, M.D. et al. The three major axes of terrestrial ecosystem function. Nature 598 , 468–472 (2021). https://doi.org/10.1038/s41586-021-03939-9

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Concept of the Terrestrial Ecology Essay

Terrestrial ecology is an organization that deals with study and conservation of plants, animals and soil by ensuring sustainability of natural resources for the benefit of humanity.

The organization has observed that human activities and natural processes cannot conserve the environment if there are no viable objectives that ensure sustainability of natural resources. Human activities such as farming, construction, settlement, deforestation, poaching, hunting and gathering gradually leads to the depletion of natural resources if not checked.

Moreover, natural processes such as erosion, weathering, predation, competition, floods, volcanic, and earthquakes destroy the natural environment thus calling for the ecological conservation. Kjaer explains that, “The terrestrial organization focuses on how plants, animals and their habitats are affected by natural conditions, climate and soil, as well as human activities, air pollution, pesticides, genetically modified plants and various agricultural management systems” (1).

The organization conducts research on various aspects of terrestrial ecology and gives viable recommendations for the government to make real time policies and laws regarding conservation of environmental resources for sustainability. Due to rampant pollution and destructive natural processes all over the world, the organization has designed effective programs that ensure real time assessment and conservation of terrestrial environment.

The terrestrial ecology has several programs directed at and specialized in soil, plants and animals. The soil, plants and animals are three components of the terrestrial environment that need conservation in order to avoid depletion of natural resources or pollution of the environment.

Regarding conservation of the soil, the terrestrial organization has realized that soil is the dominant factor in the terrestrial environment, which determines existence of plants and animals. The organization has designed programs that assess and evaluate the extent of pollution in the soil with the view of mitigating the effects of pollution and even preventing their occurrence.

According to Wickland, “…programs such as field surveys and laboratory assessment of toxic substances in the soil provide an over view of the extent of soil pollution that will enhance formulation of essential measures of conservation” (25). Quality of soil in a given terrestrial environment is critical in determining survival of plants and animals in certain environment.

Other programs of the terrestrial organization focus on the study and conservation of the plants and animals. Human beings pollute the environment and the toxic chemicals find their way into plants and animals, thus threatening their existence, which may lead to extinction.

In the environment, animals can least survive because they accumulate more toxins in their system as compared to the plants due to biomagnifications of toxins up the food chain.

Lawson and Smith argue that, “…genetic modification of plants and animals coupled with the increased pollution are deliberate human activities in that if not controlled, they may lead to extinction of organisms or reduction of biodiversity in the universe” (51).

Therefore, the terrestrial organization assesses genetic aberrations and toxicity in the organism relative to the air, soil and water pollution in order to come up with appropriate measures of environmental conservation.

The terrestrial ecology organization is an international organization that aims at conservation of the world’s natural resources for sustainable benefit of humanity. According to Kjaer, “terrestrial ecology collaborates with a number of international partners and has the overall responsibility for several large international research and development programs in various parts of the world,” (5).

The research studies carried by the organization have both national and international significance and are imperative in addressing environment issues relating to pollution and human activities. The organization also encourages development of sustainable agricultural systems by designing good agricultural practices that enhance productive farming.

Works Cited

Kjaer, Christian. “Terrestrial Ecology.” National Environmental Research Institute, (2011): 1-12

Lawson, James & Smith, Richard. “Institute of Terrestrial Ecology.” Natural Environment Research Council, (2002): 1-36 Wickland, Diane. “Terrestrial Ecology.” Tennessee Valley Authority , (2007): 1-62

• Tropical Rain Forest
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• Vertical Stratification
• Mapping the Terrestrial Reptile Distributions in Oman and the UAE
• Predation on Zebra Mussels by Freshwater Drum and Yellow Perch in Western Lake Erie
• Tornado's Variations and Formation
• Effects of Global Warming on the Environment
• Environmental Leadership Program (ELP)
• Hunting and Gathering
• The Effect of Polymers on Environment vs Glass
• Chicago (A-D)
• Chicago (N-B)

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IvyPanda . 2018. "Concept of the Terrestrial Ecology." May 30, 2018. https://ivypanda.com/essays/terrestrial-ecology/.

1. IvyPanda . "Concept of the Terrestrial Ecology." May 30, 2018. https://ivypanda.com/essays/terrestrial-ecology/.

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Terrestrial ecosystem

There are a variety of different ecosystems around the world. Remember that an ecosystem is a group of communities of both living and inert things that are related to each other. While there are many ecosystems on earth and water, terrestrial ecosystem is only found on earth . Biotic beings found in this type of ecosystem include a wide variety of life forms, such as plants and animals . The abiotic or non-living elements found in such an ecosystem include the forms of land and climate that predominate in a given place.

Related topics

Ecosystem , marine ecosystem

What is a terrestrial ecosystem?

The terrestrial ecosystem is one in which animals and plants live in the soil and air, where they find what they need to live, each of these animals and plants have different characteristics and they need to adapt to the place where they live.

Terrestrial ecosystem characteristics

Terrestrial ecosystem types, terrestrial ecosystem fauna and flora, climate and temperature.

• Animals and plants live on land and air .
• They are classified according to abiotic factors .
• The flora and fauna depend on each other.
• Animals, birds and plants adapt to the habitat in which they live.
• When changes occur and species fail to adapt, extinction

Components found in a terrestrial ecosystem are, therefore: the biotic part that includes everything related to land or soil, vegetation, aerial and terrestrial animals, decomposing organisms, and part or abiotic factors such as sun, rain, wind , erosion , temperature and climate change.

There are several types of terrestrial ecosystem that we mention below:

• Tundra : is located between the Arctic Ocean , filled with coniferous forests and extends over a vast area in North America , Europe and Asia. The animals we can find are, for example: arctic fox, polar bear, snowy bear.
• Taiga : This is the most extensive biome On Earth and extends into northern Europe, Asia and North America, and has more moderate temperatures than the tundra, however, they always reach below freezing point. The dominant vegetation includes perennial conifers with some pines and firs, their forests are dense and always remain green. The animals that inhabit the place are small birds that eat seeds and predators like falcons, pumas, Siberian tigers, wolves, etc.
• Temperate deciduous or Mediterranean forests : Extend over central and southern Europe, eastern North America, western China , Japan, New Zealand, etc. The flora includes trees such as oak and maple. Most animals are vertebrates and invertebrates . It is characterized by a warm summer and a mild, low-rainfall winter . This type of biome is used as a focus for migratory birds.
• Tropical rainforest : Found in tropical areas of high rainfall mainly in the equatorial regions of Central America and North South America. Broad-leaved perennials are found.
• Savannah : a tropical region dominated by grasses with dispersed trees and fire-resistant trunk shrubs. Fauna includes a great diversity of herbivores and explorer animals such as antelopes, buffaloes, zebras, elephants and rhinoceroses. It has a dry and rainy season and its soil is clayey and impermeable .

Terrestrial fauna are groups of animals that live in a geographic area and are found in a given ecosystem. The study of these animals is known as zoogeography . Fauna is dependent on abiotic and biotic factors , competition and predation of species. The Terrestrial flora implies the group of vegetal species that are in a determined territory. Depending on the geographical location, the flora is considered both abundant and poor, so we can say that it is very variable . It consists of all plants that grow either on land or in the sea, from giant trees to seaweeds.

In reality, both climate and temperature will depend on the type of ecosystem we are referring to and, in fact, both issues are part of the most important aspects that are taken into account when making an appropriate ecosystem classification . Sunlight , radiation , amount of rain , humidity , latitude and altitude are some of the aspects that determine the climate and the type of flora and fauna of a place.

Ecosystems are important to man because they give him a wide range of benefits for life, from trees used to extract wood and build, to some types of animals needed to survive.

• Tropical forests.
• Temperate forests.
• Grasslands, scrubland savannas.
• Serum formations.

How to cite this article?

Briceño V., Gabriela. (2019). Terrestrial ecosystem . Recovered on 24 February, 2024, de Euston96: https://www.euston96.com/en/terrestrial-ecosystem/

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