Humans are having unprecedented effects on the Earth’s biological systems (Figure 1). Climate change affects even the most remote and protected ecosystems, while natural habitats are destroyed and degraded through land-use practices, altered nutrient supply, toxic contamination, invasive species, noise and light pollution, fragmentation, and other pernicious threats (Folke et al., 2021). These global change drivers are jeopardizing the ecosystem services upon which human prosperity depends (Millennium Ecosystem Assessment, 2005) and impact biodiversity through both species’ extinctions (Johnson et al., 2017; Díaz et al., 2019) and the redistribution of species into novel biotic communities (Pejchar and Mooney, 2009; Pecl et al., 2017). Human health and wellbeing are at risk and inextricably dependent upon our ability to understand and mitigate global change impacts on biological systems (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2024).

Figure 1

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Efforts to understand and mitigate human effects on biological systems have focused on two approaches (Figure 2). On the one hand, species-level approaches have been based on biological monitoring to document changes in species presence or abundances, as well as the aggregate effects on overall biodiversity locally or regionally. Such efforts are motivated by conservation laws and agreements (e.g., the Endangered Species Act (in the US) and the International Union for Conservation of Nature’s Red List) and the ethical imperative to avoid species extinctions, including bioindicator species that are monitored as surrogates for numerous unmonitored species (Lawton and Gaston, 2001). These efforts have been crucial in alerting us to the severity of the on-going biodiversity crisis of species decline and extinction in many regions (Lindenmayer and Likens, 2010), but often do not reveal the mechanistic basis of these changes.

Figure 2

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On the other hand, ecosystem-level approaches (Figure 2) are based on legally mandated monitoring efforts focused on focal ecosystem services (Lovett et al., 2007) (e.g., the US Clean Air and Water Acts, EU Ambient Air and Water Framework Directives). Technologically sophisticated surveillance programs have been developed to predict such effects, including the detection and prediction of wildfires, coastal and inland flooding, human disease vectors, and agricultural diseases and pests. At this level, responses have focused on mitigating the human consequences of degraded services but do not address the root cause or ecological mechanisms by which global change impacts ecosystems. Furthermore, the impacts of global change on ecosystem services often operate through complex, indirect pathways resulting in so-called ‘ecological surprises’ (Paine et al., 1998; Filbee‐Dexter et al., 2017), the unexpected nature of which undermine effective planning and mitigation.

Missing, and critically needed, are practices that link these two approaches, documenting the mechanisms by which global change impacts on individual species or groups of species scale up to produce emergent impacts on ecosystem services. Acquiring this knowledge is urgent, as it is the basis for the development of nature-based solutions to protect and restore the Earth’s biological systems and protect human prosperity.

In this paper, we propose that ecological network management – defined here as the study, monitoring, and management of species interactions (Figure 2) – provides the mechanistic knowledge needed to bridge existing species- and ecosystem-level practices. We first review theory on species interactions, food web network structure, and ecosystem services predicting that disrupted species interactions should drive the degradation of natural systems to the detriment of human wellbeing. We then examine case studies from ecosystems across the planet showing how the disruption of species interaction networks underlies the degradation of ecosystem services. In turn, we present case studies demonstrating how the management (e.g., conservation or restoration) of networks has successfully mitigated such effects or restored degraded ecosystems. Finally, we go on to consider how novel and accessible technologies for data acquisition and analysis provide unprecedented opportunities for ecological network management, but also the logistical and regulatory challenges to implementing such programs. Our overarching goal is to lay out a general framework for developing network management.

Several important messages arise from this review: the drivers of global change act through complex mechanisms. Nevertheless, novel approaches to identify and monitor key interactions can reduce the likelihood of unwanted ecological surprises, efficiently guide management practices and, in so doing, provide nature-based solutions to protect and restore biodiversity and human prosperity.

Global change acts in unexpected ways via altered species interactions

We next review case studies demonstrating how global change impacts on a relatively few key ecosystem providers can disrupt interaction networks and, in so doing, transmit and amplify effects to degrade ecosystem services (Tylianakis et al., 2008; Keyes et al., 2021; Bartomeus et al., 2021; Figure 1). These outcomes not only undermine human prosperity but are often unanticipated – so-called ecological surprises – because the underlying networks are typically unstudied.

Provisioning services

Food security – fisheries

Ocean fisheries represent an important source of food globally and provide huge economic benefits through employment both off and on shore. Like many other fisheries, Atlantic cod (Gadus morhua) populations collapsed in the early 1990s, devastating 39,000 fishers and reducing annual revenue by US $200 million (Myers et al., 1997). Surprisingly, cod populations have failed to recover despite extensive fishing moratoriums (Sguotti et al., 2019). We now know that this is because adult cod promote the survival of their own eggs and juveniles by keeping competitors and predators in check, thus maintaining adult stocks in a positive feedback loop. This dynamic was set in reverse when overharvesting of these protective adults indirectly increased the abundance of smaller fish species and invertebrates upon which adult cod had previously preyed (Frank et al., 2005). With the reduction in adult cod, predation on eggs and competition with juvenile cod increased (Frank et al., 2011; Figure 3A). As a result, adult cod stocks are now not only smaller, but also fluctuate dramatically, increasing economic uncertainty. As of yet, fisheries managers have been unable to restore historic interaction regimes in this system.

Figure 3

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Forest resources

Forests provide timber, sequester carbon, and support clean water, biodiversity, and recreation services which collectively are estimated to be worth US $4.7 trillion annually (Costanza et al., 1997). Forest wildfires threaten these services. Paradoxically, the increase in catastrophic wildfires has been traced back to the indirect consequences of long-term fire suppression. By reducing tree mortality, decades of fire suppression have increased forest density and competition among trees, reducing tree vigor and thus vulnerability to attack from insects (Waring and Pitman, 1985; Figure 3B). Fire suppression also negatively affects predators that would otherwise suppress insect outbreaks (Hekkala et al., 2021). The indirect effects of fire suppression in turn increase tree mortality, leading to the accumulation of fuels for additional wildfires. At the same time, global warming has increased insect winter survival and accelerated summer population growth (Robbins et al., 2022). For example, bark beetles (Curculionidae) have affected 14 million hectares in western Canada (Safranyik et al., 2010), in some areas leading to the loss of >50% of the total merchantable pine (British Columbia Ministry of Forests, Lands and Natural Resource Operations, 2012). Because forests that are heavily infested by bark beetles are particularly prone to catastrophic wildfires (Lynch et al., 2006), wildfire frequencies are predicted to increase by 30–50% by 2100, resulting in a positive feedback loop in carbon production via reduced photosynthesis and increased carbon release associated with fires (United Nations Environment Programme, 2022).

Network management in action

There are several case studies evidencing how an understanding of interaction networks can be effectively used to guide management and raise public awareness. In particular, here we draw from research and management in two study systems which serve as good examples in this regard (Figure 4).

Figure 4

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Managing oak forest networks to control Lyme disease

Lyme disease is the costliest vector-borne disease in North America. In the United States alone, Lyme disease annually affects more than a half-million people (Adrion et al., 2015; Schwartz et al., 2017), with costs for healthcare exceeding US $1 billion (Adrion et al., 2015), and indirect costs from reduced recreation, tourism, and real estate values reaching $5 billion (Berry et al., 2018). More than two decades of research has revealed that the indirect effects of periodic ‘masting’ events – years of high acorn production – are key to understanding and managing Lyme disease risk. High acorn availability triggers increases in populations of white-footed mice (Peromyscus leucopus) and deer (Odocoileus virginianus), both critical hosts for black-legged ticks (Ixodes scapularis), which are vectors for the Lyme disease-causing bacterium (Borrelia burgdorferi) (Jones et al., 1998; Ostfeld et al., 2001). In contrast, increased diversity in non-competent hosts (thereby ‘diluting’ the availability of suitable hosts, particularly mice) reduces Lyme prevalence and thus risk to humans (Ostfeld and Keesing, 2000).

Both forest fragmentation and the loss of key predators, such as foxes and certain bird species, have disrupted the regulating services provided by this network, exacerbating disease risk (Allan et al., 2003; Levi et al., 2012). For instance, fragmented forests harbor larger populations of mice and deer, while simultaneously having lower predator densities, thus amplifying tick populations and the risk of disease transmission. Further, forest fragmentation also leads to increased acorn production (Morán-López et al., 2016), an additional pathway boosting mouse populations and disease risk. These insights have led to several innovative management strategies aimed at restoring balance in these networks. For instance, forest managers have implemented measures to increase habitat connectivity with corridors, reducing fragment edge and area effects, and have also reintroduced predators, practices that have jointly contributed to the recovery of predator populations and reduced mouse abundances (Levi et al., 2012; Ostfeld and Keesing, 2012). In addition, understanding the network dynamics between oak masting and disease risk has made it possible for predictive models to forecast Lyme disease risk 2 years in advance (Ostfeld et al., 2006), allowing health authorities and land managers to implement proactive interventions, including public awareness campaigns and targeted host population controls and application of insecticides in high-risk zones (Ostfeld and Keesing, 2000). The understanding gained through these long-term studies underscores both the complexity and value of managing interaction networks to sustain ecosystem services.

Managing aquatic food webs to control algal blooms

Harmful algal blooms in lakes cause massive economic losses in temperate regions (Carpenter et al., 2010; Cooke et al., 2016). Studies suggest that the economic costs in the United States alone could exceed $4 billion annually due to losses in fisheries, tourism, and public health impacts (Anderson et al., 2000). In these lakes, large piscivorous fish play a crucial role in regulating a well described four-level food chain including smaller fish that feed on grazing zooplankton (e.g., large species of water fleas, Daphnia) which in turn feed on algae (Carpenter et al., 2001). When populations of piscivorous fish decline, mainly due to overfishing, smaller zooplanktivorous fish proliferate unchecked, resulting in overconsumption of algae-consuming zooplankton (Lathrop et al., 2002). Due to weaker trophic control by zooplankton, algal populations proliferate, leading to algal blooms that can severely impact water quality by depleting oxygen levels, creating dead zones, and sometimes promoting harmful algal species that produce toxins detrimental to marine life and human health (Carpenter et al., 2010). Here, management actions have involved the reintroduction of top predators to reverse this food web imbalance. Piscivore reintroduction efforts have been successful in a number of regions and using different predatory fish (e.g., walleye, Stizostedion vitreum: Lathrop et al., 2002; largemouth bass, Micropterus salmoides: Mittelbach et al., 2006), leading to significant reductions in the populations of small predators and allowing algae-grazing organisms to flourish. This has ultimately led to improved water quality and a significant decrease in the occurrence of harmful algal blooms, effectively restoring lake food webs and associated ecosystem services across a broad range of biotic and abiotic conditions (Carpenter et al., 2010). Further, research has yielded a mechanistic understanding of the conditions under which these trophic cascades are strongest and manipulations are more likely to succeed (Hansson et al., 1998; Cooke et al., 2016), including the influence of additional stressors such as invasive species introductions (Walsh et al., 2016) or variability in nutrient inputs due to runoff (Carpenter et al., 2010), thereby guiding adaptive management.

The process, challenge, and promise of implementing network management

Harnessing novel streams of data on species interactions for network management will in turn require five steps, each of which is associated with its own set of unique challenges (Figure 5).

Figure 5

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First is the need to identify the ecosystem services to be maximized or protected (Figure 5, step 1). One social challenge is that the perceived value of ecosystem services or disservices can vary among societal stakeholder groups (Gray et al., 2014), and trade-offs will have to be negotiated.

The next steps are to identify the species or guilds that provide or disrupt those identified ecosystem services (Timberlake et al., 2024), as well as networks of interactions that directly and indirectly affect these species’ or guild’s performance (Figure 5, steps 2 and 3). This requires knowledge on the basic science of ecosystem function, which exists in many cases or will need to be developed.

Monitoring programs can then be developed around focal interactions, focusing especially on those most likely to be disrupted by global change (step 4). Given manager resource limitations, such efforts will need to leverage the data produced from existing species monitoring programs, as well as citizen science data and other novel data streams (see technologies section). One added challenge is that current conservation laws and regulations typically require monitoring species, not interactions, and this will require the revisions to conservation law (Gray et al., 2014), including the establishment of common standards and currencies for interaction monitoring.

The final step in this process is to leverage the insights from network monitoring to prescribe active management that restores key interactions and networks that support targeted ecosystem services, including exporting knowledge gained from well-studied ecosystems to guide management of those for which less is known. Historic management practices have often taken the phenomenological approach of restoring ecosystems to historic biophysical conditions. In contrast, network management, with its focus on defined ecosystem services (step 1), will have to accept that such natural conditions are often unattainable, and that active manipulation (e.g., species introductions or removals) will thus be required to restore ecosystem services. This fact underscores the importance of clearly stated and accepted goals for the service to be maintained so that the benefits of such manipulations are understood (step 1). Such interventions will not only require knowledge of ecosystem function but will also require placing network management within an interdisciplinary framework (Bodin and Tengö, 2012; Felipe-Lucia et al., 2022), including the use of socio-ecological networks which analyze global change drivers from the perspective of human behaviors and social interaction networks influencing ecosystem management (Bodin et al., 2019; Yletyinen et al., 2021). The inclusion of indigenous knowledge within this context is also key (Ban et al., 2018).

Pursuing network management in turn offers three principal benefits. First, changes in interactions can provide early indications of ecosystem changes. Ecological surprises arise from lack of knowledge on indirect interactions within networks such that monitoring those interactions can provide information to forecast and better mitigate impacts. Second, each community has unique attributes and dynamics, so that monitoring interactions within each system provides knowledge on local responses to develop more effective system-specific interventions. Third, monitoring interactions can also yield generalities and promote synthesis. Institutionalizing network monitoring on a broad scale will fill key knowledge gaps, allowing managers and scientists to ask key questions such as: What types of interactions are most susceptible to change? What trophic levels are directly impacted by different agents of global change, and to which trophic levels do such effects propagate (i.e., indirect effects)? And finally, how do these dynamics vary among ecosystems and with respect to different agents of global change? Research shows common underlying network structures and subsets of ecosystem service providers with similar effects across ecosystems (Bramon Mora et al., 2018), and knowledge gained from well-studied systems (e.g., top-predator reintroductions, restoring foundation species) can be used in others that are less-studied. These cross-system commonalities and inferences can provide key knowledge for land managers and policy makers.

Additional examples of degraded ecosystem services driven by species interactions case studies depicted in Figure 1 that are not highlighted in the main text are described below with network diagrams. Numbers reference the depiction of these case studies in Figure 1. Some case studies address multiple classes of ecosystem service.

Regulating services‍

Appendix 1—figure 1

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Appendix 1—figure 2

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Appendix 1—figure 3

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Appendix 1—figure 4

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Appendix 1—figure 5

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Appendix 1—figure 6

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Appendix 1—figure 7

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‍Provisioning services

Appendix 1—figure 8

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Appendix 1—figure 9

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Appendix 1—figure 10

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Cultural services‍

Appendix 1—figure 11

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Appendix 1—figure 12

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Appendix 1—figure 13

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Appendix 1—figure 14

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Appendix 1—figure 15

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Appendix 1—figure 16

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Appendix 1—figure 17

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Author details

1. Luis Abdala-Roberts

   Departamento de Ecología Tropical, Campus de Ciencias Biologicas y Agropecuarias, Universidad Autonoma de Yucatán, Yucatan, Mexico

   Contribution

   Conceptualization, Supervision, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Adriana Puentes, Christer Björkman and Kailen A Mooney

   For correspondence

   abdala.luis@yahoo.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1394-3043

2. Adriana Puentes

   Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden

   Contribution

   Conceptualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Luis Abdala-Roberts, Christer Björkman and Kailen A Mooney

   Competing interests

   No competing interests declared

3. Deborah L Finke

   Division of Plant Sciences, University of Missouri, Columbia, United States

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Robert J Marquis

   Department of Biology and the Whitney R. Harris World Ecology Center, University of Missouri–St. Louis, St. Louis, United States

   Contribution

   Conceptualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

5. Marta Montserrat

   Instituto de Hortofruticultura Subtropical y Mediterránea “La Mayora” (IHSM-UMA-CSIC), Consejo Superior de Investigaciones Cientıficas, Málaga, Spain

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Erik H Poelman

   Laboratory of Entomology, Wageningen University, Wageningen, Netherlands

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Sergio Rasmann

   Institute of Biology, University of Neuchȃtel, Neuchâtel, Switzerland

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3120-6226

8. Arnaud Sentis

   UMR RECOVER, INRAE, Aix Marseille University, Aix-en-Provence, France

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Celia C Symons

   Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, United States

   Contribution

   Conceptualization, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4120-0327

10. Nicole M van Dam

    Plant Biotic Interactions, Leibniz Institute for Vegetable and Ornamental Crops, Grosbeeren, Germany

    Contribution

    Conceptualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2622-5446

11. Gina Wimp

    Department of Biology, Georgetown University, Washington, DC, United States

    Contribution

    Conceptualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Christer Björkman

    Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Contribution

    Conceptualization, Methodology, Writing – review and editing

    Contributed equally with

    Luis Abdala-Roberts, Adriana Puentes and Kailen A Mooney

    Competing interests

    No competing interests declared

13. Kailen A Mooney

    Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, United States

    Contribution

    Conceptualization, Supervision, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Luis Abdala-Roberts, Adriana Puentes and Christer Björkman

    For correspondence

    mooneyk@uci.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7102-7794

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