EVERY FEW weeks from June 1963 until July 1968, Robert Paine, a zoologist, made the journey from Seattle, where he taught at the University of Washington, across Puget Sound to the rocky shores of Mukkaw bay. There, he had found virtually pristine tide pools that teemed with life—limpets, anemones, mussels, seaweeds and purple-and-orange seastars known as Pisaster ochraceus. The unspoiled landscape offered the perfect setting for what was to become a seminal experiment in ecology. On each visit, Dr Paine systematically removed all the seastars he could find from one patch of rock, lobbing them as far as he could into the waves.
从 1963 年 6 月到 1968 年 7 月，动物学家罗伯特・潘恩（Robert Paine）每隔几周就会从他任教的华盛顿大学所在的西雅图出发，穿过普吉特海湾到达穆卡湾的岩石海岸。在那里，他发现了几乎完全处于原始状态的潮汐池，充满了各种各样的生物——帽贝、海葵、贻贝、海藻，还有被称作「赭色海星」（Pisaster ochraceus）的紫色和橙色的海星。未受破坏的景观提供了一个完美的实验环境，而他的实验后来也成为生态学上开创性的实验。每次造访时，潘恩都会有计划地从一片岩石上取下他能找到的所有海星，并全力将它们远远抛入海中。
He did this for five years, all the while carefully documenting how the shoreline communities evolved. Very little changed in the untouched areas. But in his seastar-free zone, everything was altered. Pisaster is a greedy carnivore that feasts on mussels, barnacles, limpets and snails. Released from their predator, these species began to spread out. The acorn barnacles took over first. Later, they were displaced by goose barnacles and mussels. By removing just one species, Dr Paine had triggered a domino effect. Soon, the number of species in the community had dropped from 15 to eight. By 1968, the mussels had taken over completely.
他这样做了五年，与此同时仔细地记录下这些海岸线社区的演变过程。在他没有干预的区域几乎没有变化。但在他的无海星区，一切都变了。赭色海星是一种贪婪的食肉动物，以贻贝、藤壶、帽贝和蜗牛为食。没有了捕食者，这些物种开始扩散。首先接管的是橡子藤壶。后来，它们被鹅藤壶和贻贝取代。通过移除一个物种，潘恩博士引发了多米诺骨牌效应。很快，这个社区中的物种数量从 15 种减少到 8 种。到 1968 年就完全被贻贝占据了。
Dr Paine dubbed Pisaster a“keystone species”；remove it and the ecosystem is transformed. Large herbivores like rhinos are keystone species, spreading seeds of the plants they consume across vast areas, thus maintaining or altering vegetation. In the kelp forests of the Pacific Northwest, sea otters play a keystone role by munching on sea urchins. The urchins graze on kelp and, left unchecked, are capable of wiping out entire kelp forests on which fish and seals depend.
Keystone species illustrate the complex webs of interactions that underpin biodiversity. Understanding, let alone predicting, the impact that removing one species can have on the REST of a non-linear system is devilishly complicated. Even if sensors and ecologists could log the identity and location of every living creature on the planet, such data would be worth little without an understanding of how everything relates to everything else.
Computer models are ideally suited to providing just that. General circulation models, for example, simulate the planetary climate, linking the physics that govern the formation and disintegration of ice sheets to the huge currents that push water through the ocean, and oceanic temperature gradients to the formation of storm systems over the continents. These models are so complex that they take months to run, even on the world’s most powerful supercomputers. Climate science and policy would be nothing without them.
Ecology has few equivalents. One reason is that ecosystems are much harder to simulate.“In a physical system, you have a set of atoms or molecules that behave in a predictable way, even if it is complex,”says Derek Tittensor, a marine-ecosystem modeller at Dalhousie University in Canada. Ecology, by contrast, deals in living things, whose interactions are determined by the unpredictable behaviour of individuals.
Added to this is the complexity of the pressures and stresses that modify ecosystems. Carbon dioxide and methane are produced by different processes and behave differently in the atmosphere, but fundamentally they both warm the atmosphere. Burning fossil fuels also produces a mix of particles which cool the climate. These emissions are all very different, but their effects can, to some approximation, be reduced to a single variable known as their“global-warming potential”。Ecosystems, by contrast, are affected by warming temperatures and changing water cycles, but also by chemical pollution, urban encroachment, hunting and overfishing. None of this can be reduced to just one or even a handful of quantitative variables.
And so ecosystem modelling remains in its infancy. Statistical models, built on relationships between historical data sets—for example, how the amount of vegetation in a tropical forest tends to grow or shrink as temperatures and rainfall vary—are easier to build, and have progressed furthest. But they cannot capture or predict the dynamic, non-linear ways ecosystems respond to change, including the tipping points at which cumulative damage to an ecosystem suddenly shifts it into a new regime, for example when deforestation tips a region from forest to savannah.
Doing that requires“process-based”or“mechanistic”models, which are harder to build, but can produce non-linearity and emergent behaviour. They are the ecological equivalent of general circulation models, and operate as fully functioning simulations of Earth’s biosphere. They are particularly useful for unpicking what is driving change in an ecosystem. If a fish population is growing, is it because rising temperatures have driven predators away, or because deforestation on land nearby is releasing iron-rich dust which is fertilising the local plankton population?
Marine science has produced a number of process-based models, though they are Less uniform in their design than climate models. Some are built around food chains and the way they move biomass and energy around ecosystems; others focus on how well-suited different species are to particular ecological niches, or group species and their interactions based on body size, which is a reasonable predictor of an organism’s place in the food chain.
Over the past decade marine-ecosystem modellers have formed the Fisheries and Marine Ecosystem Model Intercomparison Project. Its goal is to determine how fishing and climate change are likely to alter marine fisheries around the world, which provide 11% of the animal protein humans consume.“Fish-MIP”develops standardised scenarios that can be run across global and regional marine-ecosystem models. As with climate modelling, the idea is to run the same simulations on different models and combine the results into robust projections that can inform policy decisions. Fish-MIP studies suggest that larger fish species, which make up most of what humans consume, are affected most by climate change, as are the tropics, where people tend to be more dependent on catches and more vulnerable to economic instability and poor nutrition.
在过去的十年中，海洋生态系统的建模人员建立了「渔业和海洋生态系统模型比对项目」（Fish-MIP）。其目标是确定捕鱼和气候变化可能会如何改变世界各地的海洋渔业，这些渔业为人类消费提供了 11% 的动物蛋白。Fish-MIP 开发了可以在各种全球和区域海洋生态系统模型上运行的标准化场景。与气候建模一样，其想法是在不同的模型上运行相同的模拟，并将结果组合成可以支撑政策决策的可靠预测。Fish-MIP 的研究表明，较大的鱼类物种（构成人类消费的大部分）受气候变化的影响最大，还有热带地区，那里的人们往往更依赖捕捞，更容易受到经济不稳定和营养不良的影响。
But simulating the effects of fishing operations is more complicated than studying the impact of rising temperatures, as assumptions have to be made about a range of variables, from how the industry will redistribute fishing fleets as fish migrate towards the poles, to how fishing technology will change, and whether changing attitudes towards sustainability will mean more marine protected areas. The climate-modelling community handles such uncertainty by drawing up standardised hypothetical scenarios and producing climate projections for each one. But the scenarios do not yet take into account the ways in which humans effect biodiversity, such as by overfishing.
Modelling is far Less advanced for land ecosystems.“Dynamic global vegetation models”can simulate human impacts on plants but do not represent non-human animals. And though there are at least eight global marine-ecosystem models that simulate life in the ocean, there is just one process-based model that includes life on land: the Madingley model, first published in 2014, which represents life both on land and in the ocean.
陆地生态系统的建模远没有那么先进。「动态全球植被模型」可以模拟人类对植物的影响，但里面没有涉及非人类的动物。而虽然至少有八个全球海洋生态系统模型模拟海洋中的生命，包含了陆地生命的基于过程的模型却只有一个——「马丁利模型」（Madingley Model）。它于 2014 年首次发布，同时考虑了陆地和海洋的生命。
Named after the village in Britain where it was devised, it breaks down land and ocean into grid cells that are up to 200 square km (77 square miles). Climatic conditions are set for each cell, which are also populated with organisms, so long as they weigh more than ten micrograms. To simplify the equations involved, the model groups organisms by size, habitat and function. It therefore cannot distinguish between two species of small songbird that live in the same region, but it does simulate interactions between, say, megafauna and their prey.
这个模型以设计出它的英国村庄命名，将陆地和海洋分解成最大 200 平方公里的网格单元。它为每个单元设定气候条件并添上生物，只要这种生物的重量超过 10 微克。为了简化所涉及的方程，该模型按大小、栖息地和功能给生物分组。因此，它无法区分生活在同一地区的两种小型鸣禽，但它确实模拟了比如巨兽与其猎物之间的相互作用。
All this allows for in silico experiments in which all the world’s top predators are wiped out entirely, an extension in space of Dr Paine’s famous seastar experiment but also an extrapolation of current global trends. An assessment in 2014 of 31 of the world’s largest mammalian carnivores found that three-quarters of them were in decline, and 17 occupied Less than half of their historical territory. Using the Madingley model, Selwyn Hoeks at Radboud University in the Netherlands, and his colleagues found that removing all carnivores weighing more than 21 kg triggered a domino effect in food chains with the net result that the total amount of vegetation on Earth decreased. Their results were published in 2020 in the journal Ecography.
所有这一切都让计算机可以推演假如世界上所有顶级捕食者被完全消灭会怎样。这是对潘恩著名的海星实验的扩展，也是对当前全球趋势的推断。2014 年对 31 种世界上最大的哺乳食肉动物的评估发现，其中有四分之三数量正在减少，17 种动物的领地还不到其历史领地的一半。利用马丁利模型，荷兰拉德堡德大学的塞尔温・霍克斯（Selwyn Hoeks）和他的同事们发现，去除所有体重超过 21 公斤的食肉动物会引发食物链中的多米诺骨牌效应，最终结果是地球上的植被总量减少。他们的研究结果发表在 2020 年的《Ecography》期刊上。
Ecologists have long argued that conserving large carnivores has tangible benefits beyond the cuddly feeling of saving tigers. According to the“green Earth hypothesis”，no carnivores means more herbivores and thus fewer plants. Vegetation soaks up carbon dioxide, so Less plant life would amplify global warming. What of the reverse, where all plant life is gradually removed? Changing landscapes, particularly through agriculture, is humanity’s greatest impact on biodiversity, and one that is likely to increase. Expanding agriculture reduces the amount of plant life at the base of food webs. Tim Newbold, of University College London, and colleagues simulated the removal of increasing amounts of vegetation from China, France, Libya and Uganda. They found that once 80% of plant life was gone, entire food chains began to collapse and could not be rebuilt by simply restoring the plants.
生态学家长期以来一直认为，除了拯救老虎让人心头柔软之外，保护大型食肉动物还有切实的好处。根据「绿色地球假说」，没有食肉动物意味着更多的食草动物，从而让植物减少。植被吸收二氧化碳，因此植物的减少会加剧全球变暖。如果反过来，让所有植物都逐渐消失会怎么样呢？地形地貌变化，尤其是因农业造成的变化，是人类对生物多样性最大的影响，而且这种影响很可能还会增加。农业扩张减少了食物网底层的植物量。伦敦大学学院的蒂姆・纽博德（Tim Newbold）和同事模拟了从中国、法国、利比亚和乌干达去除越来越多的植被。他们发现，一旦 80% 的植物消失，整个食物链就会开始崩溃，并且无法通过简单地恢复植物来重建。
As well as predicting outcomes, global ecosystem models make it possible to test policies. What would be the consequence of reintroducing a species from a population bred in captivity? Would the decline of a species be halted or reversed if a percentage of its territorial range were protected, or would it be more efficient to create a corridor between two existing protected areas?
Carbon storage, clean water, clean air, abundant crops and fish are all examples of“ecosystem services”that benefit humanity. The principle is undeniable on a grand scale, but the details are harder to map.“We don’t have any frameworks which link biodiversity changes to changes in ecosystem functioning, and on to the services that humans derive from those ecosystems,”says Michael Harfoot of the UN World Conservation Monitoring Centre and co-author of the Ecography paper.
碳储存、清洁水源、清洁空气、丰富的农作物和鱼类都是造福人类的「生态系统服务」的例子。这个原则在宏观上是不可否认的，但细节却更难描绘。「我们没有任何框架能将生物多样性的变化与生态系统功能的变化联系起来，继而再与人类从这些生态系统中获得的服务联系起来。」《Ecography》论文的合著者、联合国世界保护监测中心（UN World Conservation Monitoring Centre）的迈克尔・哈福特（Michael Harfoot）说。
Statistical models try to infer changes in ecosystem services from, for instance, trends in forest cover. But process-based models need further refinement so that changes in temperatures or land use can be linked to changes in biodiversity—and then, in turn, to the functioning of ecosystems and the services they provide.“That is probably the next big frontier for ecosystem modelling,”says Dr Harfoot,“and essentially, also, for conservation.”
For now, this remains some way off. Today’s ecosystem models are widely compared to where climate models were in their earliest days of development, about 50 years ago.“Given the urgency of the situation, we need ecosystem models to be where climate models will be in ten years’time,”says Dr Newbold.
目前，这还有一段路要走。今天的生态系统模型被普遍拿来与气候模型发展的初期相提并论——那大约是 50 年前了。「鉴于形势的紧迫性，我们需要生态系统模型在十年后达到气候模型届时的水平。」纽博德说。