Population and basic community ecology are commonly presented to students through a set of distinct models, such as those for exponential growth, logistic growth, competition, predation, and so forth. This approach mirrors the historical development of the field, but it has several shortcomings as a way to present ecological theory. First, the classical equations can appear disconnected from one another. Second, differences in the parameters and styles of the equations do not lend themselves to comparison in a common graphical form. And third, the set of equations as they are commonly presented provides no easy way to see whether any concepts are left out. In fact, something is left out that is not commonly taught: the concept of faster-than-exponential growth approaching a singularity, which is important for understanding rapidly growing systems. In the present article, we demonstrate a unified approach that simplifies the traditional equations of ecology, expands their scope, and emphasizes their interconnections.

Human populations have grown to such an extent that our species has become a dominant force on the planet, prompting geologists to begin applying the term Anthropocene to recognize the present moment. Many approaches seek to explain the past and future of human population growth, in the form of narratives and models. Some of the most influential models have parameters that cannot be precisely known but are estimated by expert opinion. Here we apply a unified model of ecology to provide a macroscale summary of the net effects of many microscale processes, using a minimal set of parameters that can be known. Our models match estimates of historic and prehistoric global human population numbers and provide predictions that correspond to some of the more complicated current models. In addition to fitting the data well they reveal that, amidst enormous complexity in our human and prehuman past, three key ecological discontinuities have occurred in turn: 1) becoming dominant competitors of large predators rather than their prey, 2) becoming mutualists with food species rather than acting as predators upon them, and 3) changing from a regime of uncontrolled population growth to one of controlled fertility instead. All three processes have been interlinked with cultural evolution and all three ushered in developments of the Anthropocene. Understanding the trajectories that have delivered us to this stage can help guide prudent paths into the future.

Many research and monitoring networks in recent decades have provided publicly available data documenting environmental and ecological change, but little is known about the status of efforts to synthesize this information across networks. We convened a working group to assess ongoing and potential cross-network synthesis research and outline opportunities and challenges for the future, focusing on the US-based research network (the US Long-Term Ecological Research network, LTER) and monitoring network (the National Ecological Observatory Network, NEON). LTER-NEON crossnetwork research synergies arise from the potentials for LTER measurements, experiments, models, and observational studies to provide context and mechanisms for interpreting NEON data, and for NEON measurements to provide standardization and broad scale coverage that complement LTER studies. Initial cross-network syntheses at co-located sites in the LTER and NEON networks are addressing six broad topics: how long-term vegetation change influences C fluxes; how detailed remotely sensed data reveal vegetation structure and function; aquatic-terrestrial connections of nutrient cycling; ecosystem response to soil biogeochemistry and microbial processes; population and species responses to environmental change; and disturbance, stability and resilience. This initial study offers exciting potentials for expanded cross-network syntheses involving multiple long-term ecosystem processes at regional or continental scales. These potential syntheses could provide a pathway for the broader scientific community, beyond LTER and NEON, to engage in cross-network science. These examples also apply to many other research and monitoring networks in the US and globally, and can guide scientists and research administrators in promoting broad-scale research that supports resource management and environmental policy. Plain Language Summary Today many research networks and monitoring networks exist in ecology and environmental science. Their complementary designs and publicly available results and data can create powerful synergies. Long-term, hypothesis-based mechanistic research can provide context and JONES ET AL.

During a burgeoning outbreak of a novel disease, public attention will ordinarily expand as the severity of the outbreak expands-as infections multiply and news reports accumulate. Such public attention will in turn reinforce tactics to control the outbreak. In classical epidemiological models, effects of such tactics can be incorporated in standard parameters of transmission, recovery, and mortality. Unfortunately, early in an outbreak those individual parameters may be poorly known, hence corresponding models can get lost in uncertainty. This makes it difficult to determine whether the disease is spreading exponentially or logistically, or along another path. Examining cases over time is also problematic, as a logistically growing infection that is leveling off appears exponential in early phases. Here we report on the most basic mechanistic, ecological model we can imagine, which can help distinguish growth that is and is not under control. This approach did a satisfactory job predicting the final outcome of the Ebola outbreak of 2014-15. The model's two parameters were computable in real time, well before the outcome was actually known. The first parameter is an intrinsic rate of increase in cumulative deaths or reported cases. The second parameter is related to the human social system and represents all tactics that combine to control the outbreak. That parameter is coupled to the number of cumulative deaths or cases. We examine the basic mechanisms operating in this model and show the predictions made during the Ebola outbreak. We also consider how this basic model is performing for the Covid-19 pandemic and highlight ecological models that align with popularly discussed concepts such as flatten-the-curve, exponential growth, and inflection points of curves.