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At least 19 recordsLinked to original sources

bioRxiv2018

Convergence of socio-ecological dynamics in disparate ecological systems under strong coupling to human social systems

It is widely recognized that coupled socio-ecological dynamics can be qualitatively different from the dynamics of social or ecological systems in isolation from one another. The influence of the type of ecological dynamics on the dynamics of the larger socio-ecological system is less well studied, however. Here, we carry out such a comparison using a mathematical model of a common pool resource problem. A population must make decisions about harvesting a renewable resource. Individuals may either be cooperators, who harvest at a sustainable level, or defectors, who over-harvest. Cooperators punish defectors through social ostracism. Individuals can switch strategies according the costs and benefits of harvesting and the strength of social ostracism. These mechanisms are represented by a differential equation for social dynamics which is coupled to three different types of resource dynamics: logistic growth, constant inflow, and threshold growth. We find that when human influence is sufficiently weak, the form of natural dynamics leaves a strong imprint on the socio-ecological dynamics, and human social dynamics are qualitatively very different from natural dynamics. However, stronger human influence introduces a broad intermediate parameter regime where dynamical patterns converge to a common type: the three types of ecological systems exhibit similar dynamics, but also, social and ecological dynamics strongly mirror one another. This is a consequence of stronger coupling and is reminiscent of synchrony from other fields, such as the classic problem of coupled oscillators in physics. Socio-ecological convergence has implications for how we understand and manage complex socio-ecological systems. In an era of growing human influence on ecological systems, further empirical and theoretical work is required to determine whether socio-ecological convergence is present in real systems.

ecology↗

bioRxiv2017

Ecological, angler and spatial heterogeneity drive social and ecological outcomes in an integrated landscape model of freshwater recreational fisheries

Freshwater recreational fisheries constitute complex adaptive social-ecological systems (SES) where mobile anglers link spatially structured ecosystems. We present a general social-ecological model of a spatial recreational fishery for northern pike (Esox lucius) that included an empirically measured mechanistic utility model driving angler behaviors. We studied emergent properties at the macro-scale (e.g., region) as a result of local-scale fish-angler interactions, while systematically examining key heterogeneities (at the angler and ecosystem level) and sources of uncertainty. We offer three key insights. First, the angler population size and the resulting latent reginal angling effort exerts a much greater impact on the overall regional-level overfishing outcome than any residential pattern (urban or rural), while the residential patterns strongly affects the location of local overfishing pockets. Second, simplifying a heterogeneous angler population to a homogenous one representing the preference and behaviours of an average angler risks severely underestimating landscape-level effort and regional overfishing. Third, we did not find that ecologically more productive lakes were more systematically overexploited than lower-productive lakes. We conclude that understanding regional-level outcomes depends on considering four key ingredients: regional angler population size, the angler population composition, the specific residential pattern in place and spatial ecological variation. Simplification of any of these may obscure important dynamics and render the system prone to collapse.

ecology↗

bioRxiv2018

Directional selection limits ecological diversification and promotes ecological tinkering during the competition for substitutable resources

Microbial communities can evade competitive exclusion by diversifying into distinct ecological niches. This spontaneous diversification often occurs amid a backdrop of directional selection on other microbial traits, where competitive exclusion would normally apply. Yet despite their empirical relevance, little is known about how diversification and directional selection combine to determine the ecological and evolutionary dynamics within a community. To address this gap, we introduce a simple, empirically motivated model of eco-evolutionary feedback based on the competition for substitutable resources. Individuals acquire heritable mutations that alter resource uptake rates, either by shifting metabolic effort between resources or by increasing overall fitness. While these constitutively beneficial mutations are trivially favored to invade, we show that the accumulated fitness differences can dramatically influence the ecological structure and evolutionary dynamics that emerge within the community. Competition between ecological diversification and ongoing fitness evolution leads to a state of diversification-selection balance, in which the number of extant ecotypes can be pinned below the maximum capacity of the ecosystem, while the ecotype frequencies and genealogies are constantly in flux. Interestingly, we find that fitness differences generate emergent selection pressures to shift metabolic effort toward resources with lower effective competition, even in saturated ecosystems. We argue that similar dynamical features should emerge in a wide range of models with a mixture of directional and diversifying selection.

evolutionary biology↗

bioRxiv2017

Phylofactorization - a graph partitioning algorithm to identify phylogenetic scales of ecological data

The problem of pattern and scale is a central challenge in ecology. The problem of scale is central to community ecology, where functional ecological groups are aggregated and treated as a unit underlying an ecological pattern, such as aggregation of \"nitrogen fixing trees\" into a total abundance of a trait underlying ecosystem physiology. With the emergence of massive community ecological datasets, from microbiomes to breeding bird surveys, there is a need to objectively identify the scales of organization pertaining to well-defined patterns in community ecological data.\n\nThe phylogeny is a scaffold for identifying key phylogenetic scales associated with macroscopic patterns. Phylofactorization was developed to objectively identify phylogenetic scales underlying patterns in relative abundance data. However, many ecological data, such as presence-absences and counts, are not relative abundances, yet it is still desireable and informative to identify phylogenetic scales underlying a pattern of interest. Here, we generalize phylofactorization beyond relative abundances to a graph-partitioning algorithm for any community ecological data.\n\nGeneralizing phylofactorization connects many tools from data analysis to phylogenetically-informe analysis of community ecological data. Two-sample tests identify three phylogenetic factors of mammalian body mass which arose during the K-Pg extinction event, consistent with other analyses of mammalian body mass evolution. Projection of data onto coordinates defined by the phylogeny yield a phylogenetic principal components analysis which refines our understanding of the major sources of variation in the human gut microbiome. These same coordinates allow generalized additive modeling of microbes in Central Park soils and confirm that a large clade of Acidobacteria thrive in neutral soils. Generalized linear and additive modeling of exponential family random variables can be performed by phylogenetically-constrained reduced-rank regression or stepwise factor contrasts. We finish with a discussion of how phylofac-torization produces an ecological species concept with a phylogenetic constraint. All of these tools can be implemented with a new R package available online.

ecology↗

bioRxiv2018

WFD ecological status indicator shows poor correlation with flow parameters in a large Alpine catchment

Since the implementation of the Water Framework Directive, the ecological status of European running waters has been evaluated using a set of harmonised ecological indicators that should guide conservation and restoration actions. Among these, the restoration of the natural flow regime (ecological flows) is considered indispensable for the achievement of the good ecological status, and yet the sensitivity of the current biological indicators to hydrologic parameters remains understudied. The Italian Star_ICMi well represents other similar WFD indicators; it is a macroinvertebrate-based multimetric index officially adopted to assess the ecological status of running waters at the national level. Recent legislation has also included the Star_ICMi as one of the indicators used to assess and prescribe ecological flows in river reaches regulated by water abstraction. However, the relationship between river hydrology and the Star_ICMi index is so far virtually unknown. Using data from the Trentino - Alto Adige Alpine region, we first assessed the relationship between the Star_ICMi and synthetic descriptors of the physico-chemical (LIMeco) and morphological (MQI) status of respectively 280 and 184 river reaches. Then, we examined the relation between the Star_ICMi and a set of ecologically-relevant hydrologic parameters derived from discharge time-series measured at 21 hydrometric stations, representing both natural and regulated river reaches. Although the Star_ICMi showed significant and linear relationships with the physico-chemical character and, slightly, with the morphological quality of the reaches, its response to flow parameters appeared weak or non-existent when examined with linear models. Mixed quantile regressions allowed the identification of flow parameters that represented limiting factors for macroinvertebrate communities and the associated Star_ICMi scores. In particular, the index showed negative floors where lower values were observed in reaches with large temporal variation in flow magnitude as well as frequent low and high flow events. The modelled quantiles also tracked the transition of the index from acceptable to unacceptable conditions.\n\nThe results suggest that while the central tendency of the Star_ICMi index is not strongly influenced by river flow character, some key flow parameters represent limiting factors that allow the index to reach its lowest values, eventually pushing the site towards unacceptable ecological conditions. The identification of limiting flow parameters can aid the setting of hydrologic thresholds over which ecological impairment is likely to occur. Overall, however, results imply caution is needed in using biological indicator like the Star_ICMi for the quantitative assessment and design of ecological flows.

ecology↗

bioRxiv2015

The Ecological Forecast Horizon, and examples of its uses and determinants

1Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure, and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental , and demographic stochasticity, evolution), level of ecological organisation (e.g. population or community), organismal properties (e.g. body size or number of trophic links) on temporal, spatial, and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development.

Ecology↗

bioRxiv2017

Time-delayed biodiversity feedbacks and the sustainability of social-ecological systems.

The sustainability of coupled social-ecological systems (SESs) hinges on their long-term ecological dynamics. Land conversion generates extinction and functioning debts, i.e. a time-delayed loss of species and associated ecosystem services. Sustainability theory, however, has not so far considered the long-term consequences of these ecological debts on SESs. We investigate this question using a dynamical model that couples human demography, technological change and biodiversity. Human population growth drives land conversion, which in turn reduces biodiversity-dependent ecosystem services to agricultural production (ecological feedback). Technological change brings about a demographic transition leading to a population equilibrium. When the ecological feedback is delayed in time, some SESs experience population overshoots followed by large reductions in biodiversity, human population size and well-being, which we call environmental crises. Using a sustainability criterion that captures the vulnerability of an SES to such crises, we show that some of the characteristics common to modern SESs (e.g. high production efficiency and labor intensity, concave-down ecological relationships) are detrimental to their long-term sustainability. Maintaining sustainability thus requires strong counteracting forces, such as the demographic transition and land-use management. To this end, we provide integrative sustainability thresholds for land conversion, biodiversity loss and human population size - each threshold being related to the others through the economic, technological, demographic and ecological parameters of the SES. Numerical simulations show that remaining within these sustainable boundaries prevents environmental crises from occurring. By capturing the long-term ecological and socioeconomic drivers of SESs, our theoretical approach proposes a new way to define integrative conservation objectives that ensure the long-term sustainability of our planet.

ecology↗

bioRxiv2018

Categorized analysis of forest ecological values in the China’s conversion cropland to forest program

Background\n\nThe Chinas Conversion Cropland to Forest Program (CCFP) is one of the large state ecological construction programs. Up to now, the program has effectively improved the ecological environment and produced large ecological benefit. However, there were also some problems in its implementation process, the program has been sometimes less effective than the expected.\n\nMethods\n\nBased on the data and the methods of State report on monitoring ecological effects in CCFP and the Chinese Forest Ecosystem Research Network (CFERN) in 2013, we analyzed the categorized forest ecological benefit value (B-V) s in the three forest restoration ways in different regions in China to provide references for CCFP construction.\n\nResults\n\nThe unit area B-Vs in CCFP varied between 35 000 RMBs.hm-2.a-1 and 100 000 RMBs.hm-2.a-1. Water conservation B-V and species conservation B-V were the two largest constituents, nutrient accumulation B-V was the least in all the categorized B-Vs on regional and unit area scale. The rank of restoration ways on average unit area total B-Vs was-- hillside forest conservation > returning cropland to forest > afforestation on suitable barren hills and wasteland in CCFP. Among the categorized B-Vs, some pairs were positively correlated with each other and some were negatively correlative. The correlation coefficients and some regression equations were given in the text and the attached Fig.s.\n\nConclusions\n\nWater conservation B-V was the highest and nutrient accumulation B-V was the lowest whether on regional or unit area scale in CCFP.\n\nForest ecological B-Vs varied in different forest restoration ways and different regions in CCFP. The hillside forest conservation restoration way and the water conservation B-V should be paid more attention in Chinas future forest restoration. We suggest that suitable forest restoration ways should be selective according to the regional specific and ecological targets.\n\nThere were correlations among the categorized B-Vs, and the correlations varied with different forest restoration ways in CCFP. Knowing about the correlations could clarify the targeted restoration ways according to the goal of ecological benefit.

ecology↗

bioRxiv2018

On the prevalence of uninformative parameters in statistical models applying model selection in applied ecology

Research in applied ecology provides scientific evidence to guide conservation policy and management. Applied ecology is becoming increasingly quantitative and model selection via information criteria has become a common statistical modeling approach. Unfortunately, parameters that contain little to no useful information are commonly presented and interpreted as important in applied ecology. I review the concept of an uninformative parameter in model selection using information criteria and perform a literature review to measure the prevalence of uninformative parameters in model selection studies applying Akaikes Information Criterion (AIC) in 2014 in four of the top journals in applied ecology (Biological Conservation, Conservation Biology, Ecological Applications, Journal of Applied Ecology). Twenty-one percent of studies I reviewed applied AIC metrics. Many (31.5 %) of the studies applying AIC metrics in the four applied ecology journals I reviewed had or were very likely to have uninformative parameters in a model set. In addition, more than 40 % of studies reviewed had insufficient information to assess the presence or absence of uninformative parameters in a model set. Given the prevalence of studies likely to have uninformative parameters or with insufficient information to assess parameter status (71.5 %), I surmise that much of the policy recommendations based on applied ecology research may not be supported by the data analysis. I provide warning signals and a decision tree to help reduce the prevalence of uninformative parameters in studies applying model selection with information criteria. The four warning signals and decision tree should assist authors, reviewers, and editors to screen for uninformative parameters in studies applying model selection with information criteria. In the end, careful thinking at every step of the scientific process and greater reporting standards are required to detect uninformative parameters in studies adopting an information criteria approach.

ecology↗

bioRxiv2017

Stability and critical transitions in mutualistic ecological systems

Successful conservation of complex ecosystems, their function and associated services, requires deep understanding of their underlying dynamics and potential instabilities. While the study of ecological dynamics is a mature and diverse field, the lack of a general model that uses basic ecological parameters to predict system-level behaviour has allowed unresolved contradictions to persist. Here, we provide a general model of a mutualistic ecological community and show for the first time how the conditions for instability, the nature of ecological collapse, and potential early-warning signals, can be derived from the basic ecological parameters. We also resolve open questions concerning effects of interaction heterogeneity on both resilience and abundance, and discuss their potential trade-off in real systems. This framework provides a basis for rich investigations of ecological system dynamics, and can be generalised across many ecological contexts.

ecology↗

bioRxiv2017

Ecological Network Metrics: Opportunities For Synthesis

Network ecology provides a systems basis for approaching ecological questions, such as factors that influence biological diversity, the role of particular species or particular traits in structuring ecosystems, and long-term ecological dynamics (e.g. stability). Whereas the introduction of network theory has enabled ecologists to quantify not only the degree, but also the architecture of ecological complexity, these advances have come at the cost of introducing new challenges, including new theoretical concepts and metrics, and increased data complexity and computational intensity. Synthesizing recent developments in the network ecology literature, we point to several potential solutions to these issues: integrating network metrics and their terminology across sub-disciplines; benchmarking new network algorithms and models to increase mechanistic understanding; and improving tools for sharing ecological network research, in particular \"model\" data provenance, to increase the reproducibility of network models and analyses. We propose that applying these solutions will aid in synthesizing ecological subdisciplines and allied fields by improving the accessibility of network methods and models.

ecology↗

bioRxiv2018

Developing an automated iterative near-term forecasting system for an ecological study

O_LIMost forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform.\nC_LIO_LIIterative near-term ecological forecasting involves repeated daily to annual scale forecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a long-term study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modeling and forecasting, archiving, and presentation of the forecasts.\nC_LIO_LIThe forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development.\nC_LIO_LIAutomated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build automated forecasting systems and use them to advance our understanding of natural systems.\nC_LI

ecology↗

bioRxiv2026

Environmental sensing capacity predicts bacterial ecological strategies and environmental preferences

The ability to sense environmental variation is a prerequisite for ecological success. Sensor domains enable bacteria to detect nutrients, neighboring organisms, and physicochemical conditions, but whether variation in these sensing systems reflects ecological specialization remains unresolved. Here, we analyzed sensor domains across 51,343 bacterial genomes and 255 soil metagenomes spanning a climatic gradient to determine whether sensory repertoires encode bacterial ecological strategies and environmental preferences. Sensory repertoires exhibited strong phylogenetic conservatism and revealed signatures of genome streamlining, indicating that environmental sensing reflects trade-offs associated with maintaining sensory complexity. Taxa occupying environmentally heterogeneous habitats, particularly free-living aerobic generalists, encoded the largest sensory repertoires, consistent with selection for expanded environmental information processing. To link sensory function with ecological adaptation, we mapped experimentally-characterized ligand-binding motifs (LBMs) across genomes and metagenomes. Distinct LBM profiles discriminated host-associated and free-living taxa, aerobic and anaerobic lineages, and generalists and non-generalists, revealing a tight coupling between sensory capacity and ecological strategy. Across soil communities, motifs associated with osmoprotection and oxygen sensing were consistently enriched under increasing aridity, linking sensory function to environmental filtering in natural ecosystems. These findings identify environmental sensing as an important organizational axis of bacterial trait-based ecology that integrates evolutionary history, ecological lifestyle, and adaptation to local conditions. Environmental sensing should be considered for predicting microbial niches and responses to environmental change.

ecology↗

bioRxiv2017

Eco-evolutionary dynamics under limited dispersal: ecological inheritance, altruism within and spite between species

Understanding selection on ecological interactions that take place in dispersal-limited communities is an important challenge for ecology and evolutionary biology. The problem is that local demographic stochasticity generates eco-evolutionary dynamics that are too complicated to make tractable analytical investigations. Here, we circumvent this problem by approximating the selection gradient on a quantitative trait that influences local community dynamics, assuming that such dynamics are deterministic with a stable fixed point, but incorporating the kin selection effects arising from demographic stochasticity. Our approximation reveals that selection depends on how an individual expressing a trait-change influences: (1) its own fitness and the fitness of its current relatives; and (2) the fitness of its downstream relatives through modifications of local ecological conditions (i.e., through ecological inheritance). Mathematically, the effects of ecological inheritance on selection are captured by dispersal-limited versions of press-perturbations of community ecology. We use our approximation to investigate the evolution of helping within- and harming between-species when these behaviours influence demography. We find helping evolves more readily when competition is for material resources rather than space because then, the costs of kin competition are paid by downstream relatives. Similarly, harming between species evolves when it alleviates downstream relatives from inter-specific competition. Beyond these examples, our approximation can help better understand the influence of ecological inheritance on a variety of eco-evolutionary dynamics, from plant-pollinator to predator-prey coevolution.

evolutionary biology↗

bioRxiv2018

Spatial and ontogenetic variation in susceptibility to polarotactic ecological traps

Ecological traps occur when environmental cues become unreliable, causing an evolutionary mismatch between features of the environment and expected outcome that leads to suboptimal behavioural responses and, ultimately, reduced fitness. Ecological traps arise due to anthropogenic disturbance in the environment introducing novel elements that mimic those environmental cues. Therefore, ecological traps represent a strong selective pressure in areas where anthropogenic changes are frequent, such as cities. However, given the exposure to these traps over generations, localised adaptations to ecological traps might be expected in urban populations. Dragonflies and damselflies (Odonata) are one of the many taxa vulnerable to ecological traps: odonates use horizontally polarised light as a cue of suitable water bodies, although some artificial surfaces also reflect horizontally polarised light strongly, thus misleading odonates to oviposit preferentially on these unsuitable surfaces rather than in water. Here, we compare the behavioural response to horizontally polarised light between urban and rural populations of the odonate Ischnura elegans to test the potential for localised adaptations to ecological traps. Laboratory choice experiments were performed using field-caught adults from urban and rural areas, and individuals reared in controlled conditions to account for environmental variation and exposure to polarised light. We also studied the association between ontogeny and polarotaxis that has been suggested - but not empirically tested - by other studies. The results showed that field-caught rural individuals had a significantly stronger preference for horizontally polarised light compared to urban individuals, suggesting there is strong selection against polarotaxis in urban areas. However, individuals reared in controlled conditions showed no difference between urban and rural populations, suggesting that there has not yet been adaptation in urban odonates. Instead, adults developed a strong preference for horizontally polarised light with increasing age, showing that mature adults are more prone to ecological traps. Possible mechanisms driving this response are discussed.

evolutionary biology↗

bioRxiv2018

Revealing biases in the sampling of ecological interaction networks

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases in both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These issues may affect the accuracy of empirically constructed ecological networks. Yet statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying ecological networks structure. To explore properties of large-scale modular networks, we developed EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different sampling designs that may be employed in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties accurately depends both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, the modules with nested structure were the easiest to detect, regardless of sampling design. Sampling according to species degree (number of interactions) was consistently found to be the most accurate strategy to estimate network structure. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. We recommend that these findings be incorporated into field sampling design of projects aiming to characterize large species interactions networks to reduce sampling biases.\n\nAuthor SummaryEcological interactions are commonly modeled as interaction networks. Analyses of such networks may be sensitive to sampling biases and detection issues in both the interactors and interactions (nodes and links). Yet, statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying networks structure. For insight into ecological networks, we developed software EcoNetGen (available in R and Python). These allow the generation and sampling of several types of large-scale modular networks with predetermined topologies, representing a wide variety of communities and types of ecological interactions. Networks can be sampled according to designs employed in field observations. We demonstrate, through first uses of this software, that underlying network topology interacts strongly with empirical sampling design, and that constructing empirical networks by starting with highly connected species may be the give the best representation of the underlying network.

ecology↗

bioRxiv2014

mangal - making ecological network analysis simple

The study of ecological networks is severely limited by (i) the difficulty to access data, (ii) the lack of a standardized way to link meta-data with interactions, and (iii) the disparity of formats in which ecological networks themselves are represented. To overcome these limitations, we conceived a data specification for ecological networks. We implemented a database respecting this standard, and released a R package (rmangal) allowing users to programmatically access, curate, and deposit data on ecological interactions. In this article, we show how these tools, in conjunctions with other frameworks for the programmatic manipulation of open ecological data, streamlines the analysis process, and improves eplicability and reproducibility of ecological networks studies.

Ecology↗

bioRxiv2015

Sampling networks of ecological interactions

O_LISampling ecological interactions presents similar challenges, problems, potential biases, and constraints as sampling individuals and species in biodiversity inventories. Interactions are just pairwise relationships among individuals of two different species, such as those among plants and their seed dispersers in frugivory interactions or those among plants and their pollinators. Sampling interactions is a fundamental step to build robustly estimated interaction networks, yet few analyses have attempted a formal approach to their sampling protocols.\nC_LIO_LIRobust estimates of the actual number of interactions (links) within diversified ecological networks require adequate sampling effort that needs to be explicitly gauged. Yet we still lack a sampling theory explicitly focusing on ecological interactions.\nC_LIO_LIWhile the complete inventory of interactions is likely impossible, a robust characterization of its main patterns and metrics is probably realistic. We must acknowledge that a sizable fraction of the maximum number of interactions Imax among, say, A animal species and P plant species (i.e., Imax = AP) is impossible to record due to forbidden links, i.e., life-history restrictions. Thus, the number of observed interactions I in robustly sampled networks is typically I << Imax, resulting in extremely sparse interaction matrices with low connectance.\nC_LIO_LIReasons for forbidden links are multiple but mainly stem from spatial and temporal uncoupling, size mismatches, and intrinsically low probabilities of interspecific encounter for most potential interactions of partner species. Ad-equately assessing the completeness of a network of ecological interactions thus needs knowledge of the natural history details embedded, so that for-bidden links can be \"discounted\" when addressing sampling effort.\nC_LIO_LIHere I provide a review and outline a conceptual framework for interaction sampling by building an explicit analogue to individuals and species sampling, thus extending diversity-monitoring approaches to the characterization of complex networks of ecological interactions. This is crucial to assess the fast-paced and devastating effects of defaunation-driven loss of key ecological interactions and the services they provide and the analogous losses related to interaction gains due to invasive species and biotic homogenization.\nC_LI

Ecology↗