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Despite the increasing use of standards for documenting and testing agent-based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to ad hoc and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social-ecological systems (SES). We argue that reusable building blocks (RBBs) known from professional software development can mitigate these issues. An RBB is a submodel that represents a particular mechanism or process that is relevant across many ABMs in an application domain, such as plant competition in vegetation models, or reinforcement learning in a behavioural model. RBBs need to be distinguished from modules, which represent entire subsystems and include more than one mechanism and process. While linking modules faces the same challenges as integrating different models in general, RBBs are “atomic” enough to be more easily re-used in different contexts. We describe and provide examples from different domains for how and why building blocks are used in software development, and the benefits of doing so for the ABM community and to individual modellers. We propose a template to guide the development and publication of RBBs and provide example RBBs that use this template. Most importantly, we propose and initiate a strategy for community-based development, sharing and use of RBBs. Individual modellers can have a much greater impact in their field with an RBB than with a single paper, while the community will benefit from increased coherence, facilitating the development of theory for both the behaviour of agents and the systems they form. We invite peers to upload and share their RBBs via our website - preferably referenced by a DOI (digital object identifier obtained e.g. via Zenodo). After a critical mass of candidate RBBs has accumulated, feedback and discussion can take place and both

Rapid development of tracking technologies allow the collection of high-quality data on multiple simultaneously moving individuals. This, in turn, initiated the development of several methods to infer interactions among moving animals. However, the performance of these methods has not been studied systematically, especially with regard to the factors that are highly relevant for field ecologists, such as duration of the tracking period, its temporal resolution and the proportion of the tracked community.Here, we assessed the performance of three dynamic interaction indices (Coefficient of sociality, Correlation coefficient and Dynamic interaction) and two novel approaches based on step selection functions (SSF-occurrence-distribution and SSF-distance). We tested these methods on the data simulated with a predator–prey system, reflecting three common types of interactions while on the move: avoidance (prey individuals fleeing from the predator), attraction (predator following and chasing prey) and neutral movement (no interactions between predator and prey). We assessed the ability of each method to correctly detect the modelled interaction type by manipulating the perceptual range of the predator, the proportion of tracked prey individuals, the temporal resolution and the duration of the tracking period.We found that the ability to correctly infer interactions increased for all methods with an increase in the perceptual range of the predator and the proportion of tracked prey individuals. In contrast, the duration of the tracking period affected the methods' performance differently: some methods (Cs and SSF-distance) were insensitive to it, whereas the performance of other methods improved (DI and SSF-occurrence-distribution) and worsened (Cr) with the duration of the tracking period. The three dynamic interaction indices and SSF-distance were rather robust to changes in data resolution.Despite using the predator–prey system as our study case, our findings are appli

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Assessing and predicting the persistence of populations is essential for the conservation and control of species. Here, we argue that local mechanisms require a better conceptual synthesis to facilitate a more holistic consideration along with regional mechanisms known from metapopulation theory. We summarise the evidence for local buffer mechanisms along with their capacities and emphasise the need to include multiple buffer mechanisms in studies of population persistence. We propose an accessible framework for local buffer mechanisms that distinguishes between damping (reducing fluctuations in population size) and repelling (reducing population declines) mechanisms. We highlight opportunities for empirical and modelling studies to investigate the interactions and capacities of buffer mechanisms to facilitate better ecological understanding in times of ecological upheaval.

In the 10 Must Knows from Biodiversity Science 45 scientists present facts about biodiversity in a well-founded and generally intelligible way. They analyse the complex systems of the earth by highlighting ten key areas, each of which, in turn, is inextricably linked to all the others. And they show ways to stop the continued loss of species diversity and ecosystems, and to promote biodiversity. The underlying aim is to provide policy-makers and society with scientifically validated assessments of the latest knowledge to facilitate improved policy decisions and action at local, regional, national and global levels, in order to conserve the diversity of life – biodiversity. These are the 10MustKnows 2022: 1. Achieving climate and biodiversity protection together 2. Strengthening planetary health 3. Considering hidden biodiversity 4. Promoting biocultural habitats 5. Using forests sustainably 6. Transforming agriculture 7. Protecting land and resources 8. Expanding transnational infrastructure and education for sustainability 9. Ensuring access and open use of research data 10. Setting biodiversity-friendly incentives

In the 10 Must Knows from Biodiversity Science 45 scientists present facts about biodiversity in a well-founded and generally intelligible way. They analyse the complex systems of the earth by highlighting ten key areas, each of which, in turn, is inextricably linked to all the others. And they show ways to stop the continued loss of species diversity and ecosystems, and to promote biodiversity. The underlying aim is to provide policy-makers and society with scientifically validated assessments of the latest knowledge to facilitate improved policy decisions and action at local, regional, national and global levels, in order to conserve the diversity of life – biodiversity. These are the 10MustKnows 2022: 1. Achieving climate and biodiversity protection together 2. Strengthening planetary health 3. Considering hidden biodiversity 4. Promoting biocultural habitats 5. Using forests sustainably 6. Transforming agriculture 7. Protecting land and resources 8. Expanding transnational infrastructure and education for sustainability 9. Ensuring access and open use of research data 10. Setting biodiversity-friendly incentives

Ecological stability refers to a family of concepts used to describe how systems of interacting species vary through time and respond to disturbances. Because observed ecological stability depends on sampling scales and environmental context, it is notoriously difficult to compare measurements across sites and systems. Here, we apply stochastic dynamical systems theory to derive general statistical scaling relationships across time, space, and ecological level of organisation for three fundamental stability aspects: resilience, resistance, and invariance. These relationships can be calibrated using random or representative samples measured at individual scales, and projected to predict average stability at other scales across a wide range of contexts. Moreover deviations between observed vs. extrapolated scaling relationships can reveal information about unobserved heterogeneity across time, space, or species. We anticipate that these methods will be useful for cross‐study synthesis of stability data, extrapolating measurements to unobserved scales, and identifying underlying causes and consequences of heterogeneity.

Ensuring ecosystem resilience is an intuitive approach to safeguard the functioning of ecosystems and hence the future provisioning of ecosystem services (ES). However, resilience is a multi‐faceted concept that is difficult to operationalize. Focusing on resilience mechanisms, such as diversity, network architectures or adaptive capacity, has recently been suggested as means to operationalize resilience. Still, the focus on mechanisms is not specific enough. We suggest a conceptual framework, resilience trinity, to facilitate management based on resilience mechanisms in three distinctive decision contexts and time‐horizons: 1) reactive, when there is an imminent threat to ES resilience and a high pressure to act, 2) adjustive, when the threat is known in general but there is still time to adapt management and 3) provident, when time horizons are very long and the nature of the threats is uncertain, leading to a low willingness to act. Resilience has different interpretations and implications at these different time horizons, which also prevail in different disciplines. Social ecology, ecology and engineering are often implicitly focussing on provident, adjustive or reactive resilience, respectively, but these different notions of resilience and their corresponding social, ecological and economic tradeoffs need to be reconciled. Otherwise, we keep risking unintended consequences of reactive actions, or shying away from provident action because of uncertainties that cannot be reduced. The suggested trinity of time horizons and their decision contexts could help ensuring that longer‐term management actions are not missed while urgent threats to ES are given priority.

The Overview, Design concepts and Details (ODD) protocol for describing Individual-and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design ratio-nale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

Organismal movement is ubiquitous and facilitates important ecological mechanisms that drive community and metacommunity composition and hence biodiversity. In most existing ecological theories and models in biodiversity research, movement is represented simplistically, ignoring the behavioural basis of movement and consequently the variation in behaviour at species and individual levels. However, as human endeavours modify climate and land use, the behavioural processes of organisms in response to these changes, including movement, become critical to understanding the resulting biodiversity loss. Here, we draw together research from different subdisciplines in ecology to understand the impact of individual‐level movement processes on community‐level patterns in species composition and coexistence. We join the movement ecology framework with the key concepts from metacommunity theory, community assembly and modern coexistence theory using the idea of micro–macro links, where various aspects of emergent movement behaviour scale up to local and regional patterns in species mobility and mobile‐link‐generated patterns in abiotic and biotic environmental conditions. These in turn influence both individual movement and, at ecological timescales, mechanisms such as dispersal limitation, environmental filtering, and niche partitioning. We conclude by highlighting challenges to and promising future avenues for data generation, data analysis and complementary modelling approaches and provide a brief outlook on how a new behaviour‐based view on movement becomes important in understanding the responses of communities under ongoing environmental change.

Forage availability has been suggested as one driver of the observed decline in honey bees. However, little is known about the effects of its spatiotemporal variation on colony success. We present a modeling framework for assessing honey bee colony viability in cropping systems. Based on two real farmland structures, we developed a landscape generator to design cropping systems varying in crop species identity, diversity, and relative abundance. The landscape scenarios generated were evaluated using the existing honey bee colony model BEEHAVE, which links foraging to in‐hive dynamics. We thereby explored how different cropping systems determine spatiotemporal forage availability and, in turn, honey bee colony viability (e.g., time to extinction, TTE) and resilience (indicated by, e.g. brood mortality). To assess overall colony viability, we developed metrics, PH and PP, which quantified how much nectar and pollen provided by a cropping system per year was converted into a colony’s adult worker population. Both crop species identity and diversity determined the temporal continuity in nectar and pollen supply and thus colony viability. Overall farmland structure and relative crop abundance were less important, but details mattered. For monocultures and for four‐crop species systems composed of cereals, oilseed rape, maize and sunflower, PH and PP were below the viability threshold. Such cropping systems showed frequent, badly timed, and prolonged forage gaps leading to detrimental cascading effects on life stages and in‐hive work force, which critically reduced colony resilience. Four‐crop systems composed of rye‐grass‐dandelion pasture, trefoil‐grass pasture, sunflower and phacelia ensured continuous nectar and pollen supply resulting in TTE > 5 years, and PH (269.5 kg) and PP (108 kg) being above viability thresholds for five years. Overall, trefoil‐grass pasture, oilseed rape, buckwheat and phacelia improved the temporal continuity in forage supply and colon

Designing, implementing, and applying agent-based models (ABMs) requires a structured approach, part of which is a comprehensive analysis of the output to input variability in the form of uncertainty and sensitivity analysis (SA). The objective of this paper is to assist in choosing, for a given ABM, the most appropriate methods of SA. We argue that no single SA method fits all ABMs and that different methods of SA should be used based on the overarching purpose of the model. For example, abstract exploratory models that focus on a deeper understanding of the target system and its properties are fed with only the most critical data representing patterns or stylized facts. For them, simple SA methods may be sufficient in capturing the dependencies between the output-input spaces. In contrast, applied models used in scenario and policy-analysis are usually more complex and data-rich because a higher level of realism is required. Here the choice of a more sophisticated SA may be critical in establishing the robustness of the results before the model (or its results) can be passed on to end-users. Accordingly, we present a roadmap that guides ABM developers through the process of performing SA that best fits the purpose of their ABM. This roadmap covers a wide range of ABM applications and advocates for the routine use of global methods that capture input interactions and are, therefore, mandatory if scientists want to recognize all sensitivities. As part of this roadmap, we report on frontier SA methods emerging in recent years: a) handling temporal and spatial outputs, b) using the whole output distribution of a result rather than its variance, c) looking at topological relationships between input data points rather than their values, and d) looking into the ABM black box – finding behavioral primitives and using them to study complex system characteristics like regime shifts, tipping points, and condensation versus dissipation of collective system behavior.

Ensuring ecosystem resilience is an intuitive approach to safeguard the functioning of ecosystems and hence the future provisioning of ecosystem services (ES). However, resilience is a multi‐faceted concept that is difficult to operationalize. Focusing on resilience mechanisms, such as diversity, network architectures or adaptive capacity, has recently been suggested as means to operationalize resilience. Still, the focus on mechanisms is not specific enough. We suggest a conceptual framework, resilience trinity, to facilitate management based on resilience mechanisms in three distinctive decision contexts and time‐horizons: 1) reactive, when there is an imminent threat to ES resilience and a high pressure to act, 2) adjustive, when the threat is known in general but there is still time to adapt management, and 3) provident, when time horizons are very long and the nature of the threats is uncertain, leading to a low willingness to act. Resilience has different interpretations and implications at these different time horizons, which also prevail in different disciplines. Social ecology, ecology, and engineering are often implicitly focussing on provident, adjustive, or reactive resilience, respectively, but these different notions of resilience and their corresponding social, ecological, and economic tradeoffs need to be reconciled. Otherwise, we keep risking unintended consequences of reactive actions, or shying away from provident action because of uncertainties that cannot be reduced. The suggested trinity of time horizons and their decision contexts could help ensuring that longer‐term management actions are not missed while urgent threats to ES are given priority.

Understanding host–pathogen dynamics requires realistic consideration of transmission events that, in the case of directly transmitted pathogens, result from contacts between susceptible and infected individuals. The corresponding contact rates are usually heterogeneous due to variation in individual movement patterns and the underlying landscape structure. However, in epidemiological models, the roles that explicit host movements and landscape structure play in shaping contact rates are often overlooked. We adapted an established agent‐based model of classical swine fever (CSF) in wild boar Sus scrofa to investigate how explicit representation of landscape heterogeneity and host movement between social groups affects invasion and persistence probabilities. We simulated individual movement both phenomenologically as a correlated random walk (CRW) and mechanistically by representing interactions of the moving individuals with the landscape and host population structure. The effect of landscape structure on the probability of invasion success and disease persistence depended remarkably on the way host movement is simulated and the case fatality ratio associated with the pathogen strain. The persistence probabilities were generally low with CRW which ignores feedbacks to external factors. Although the basic reproduction number R0, a measure of the contagiousness of an infectious disease, was kept constant, these probabilities were up to eight times higher under mechanistic movement rules, especially in heterogeneous landscapes. The increased persistence emerged due to important feedbacks of the directed movement on the spatial variation of host density, contact rates, and transmission events to distant areas. Our findings underscore the importance of accounting for spatial context and group size structures in eco‐epidemiological models. Our study highlights that the simulation of explicit, mechanistic movement behaviour can reverse predictions of disease persistence in

The Overview, Design concepts and Details (ODD) protocol for describing Individual- and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design rationale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

The acceptance and usefulness of simulation models are often limited by the efficiency, transparency, reproducibility, and reliability of the modelling process. We address these issues by suggesting that modellers (1) “trace” the iterative modelling process by keeping a modelling notebook corresponding to the laboratory notebooks used by empirical researchers, (2) use a standardized notebook structure and terminology based on the existing TRACE documentation framework, and (3) use their notebooks to compile TRACE documents that supplement publications and reports. These practices have benefits for model developers, users, and stakeholders: improved and efficient model design, analysis, testing, and application; increased model acceptance and reuse; and replicability and reproducibility of the model and the simulation experiments. Using TRACE terminology and structure in modelling notebooks facilitates production of TRACE documents. We explain the rationale of TRACE, provide example TRACE documents, and suggest strategies for keeping “TRACE Modelling Notebooks.”

Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species‐specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi‐dimensional assessments are still to be recommended.

Understanding the drivers underlying disease dynamics is still a major challenge in disease ecology, especially in the case of long‐term disease persistence. Even though there is a strong consensus that density‐dependent factors play an important role for the spread of diseases, the main drivers are still discussed and, more importantly, might differ between invasion and persistence periods.Here, we analysed long‐term outbreak data of classical swine fever, an important disease in both wild boar and livestock, prevalent in the wild boar population from 1993 to 2000 in Mecklenburg‐Vorpommern, Germany. We report outbreak characteristics and results from generalized linear mixed models to reveal what factors affected infection risk on both the landscape and the individual level.Spatiotemporal outbreak dynamics showed an initial wave‐like spread with high incidence during the invasion period followed by a drop of incidence and an increase in seroprevalence during the persistence period. Velocity of spread increased with time during the first year of outbreak and decreased linearly afterwards, being on average 7.6 km per quarter. Landscape‐ and individual‐level analyses of infection risk indicate contrasting seasonal patterns. During the persistence period, infection risk on the landscape level was highest during autumn and winter seasons, probably related to spatial behaviour such as increased long‐distance movements and contacts induced by rutting and escaping movements. In contrast, individual‐level infection risk peaked in spring, probably related to the concurrent birth season leading to higher densities, and was significantly higher in piglets than in reproductive animals.Our findings highlight that it is important to investigate both individual‐ and landscape‐level patterns of infection risk to understand long‐term persistence of wildlife diseases and to guide respective management actions. Furthermore, we highlight that exploring different temporal aggre

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Anthropogenic pressures increasingly alter natural systems. Therefore, understanding the resilience of agent-based complex systems such as ecosystems, i.e. their ability to absorb these pressures and sustain their functioning and services, is a major challenge. However, the mechanisms underlying resilience are still poorly understood. A main reason for this is the multidimensionality of both resilience, embracing the three fundamental stability properties recovery, resistance and persistence, and of the specific situations for which stability properties can be assessed. Agent-based models (ABM) complement empirical research which is, for logistic reasons, limited in coping with these multiple dimensions. Besides their ability to integrate multidimensionality through extensive manipulation in a fully controlled system, ABMs can capture the emergence of system resilience from individual interactions and feedbacks across different levels of organization. To assess the extent to which this potential of ABMs has already been exploited, we reviewed the state of the art in exploring resilience and its multidimensionality in ecological and socio-ecological systems with ABMs. We found that the potential of ABMs is not utilized in most models, as they typically focus on a single dimension of resilience by using variability as a proxy for persistence, and are limited to one reference state, disturbance type and scale. Moreover, only few studies explicitly test the ability of different mechanisms to support resilience. To overcome these limitations, we recommend to simultaneously assess multiple stability properties for different situations and under consideration of the mechanisms that are hypothesised to render a system resilient. This will help us to better exploit the potential of ABMs to understand and quantify resilience mechanisms, and hence support solving real-world problems related to the resilience of agent-based complex systems.

Ecological disturbances are important drivers of biodiversity patterns. Many biodiversity studies rely on endpoint measurements instead of following the dynamics that lead to those outcomes and testing ecological drivers individually, often considering only a single trophic level. Manipulating multiple factors (biotic and abiotic) in controlled settings and measuring multiple descriptors of multi-trophic communities could enlighten our understanding of the context dependency of ecological disturbances. Using model microbial communities, we experimentally tested the effects of imposed disturbances (i.e. increased dilution simulating density-independent mortality as press or pulse disturbances coupled with resource deprivation) on bacterial abundance, diversity and community structure in the absence or presence of a protist predator. We monitored the communities immediately before and after imposing the disturbance and four days after resuming the pre-disturbance dilution regime to infer resistance and recovery properties. The results highlight that bacterial abundance, diversity and community composition were more affected by predation than by disturbance type, resource loss or the interaction of these factors. Predator abundance was strongly affected by the type of disturbance imposed, causing temporary relief of predation pressure. Importantly, prey community composition differed significantly at different phases, emphasizing that endpoint measurements are insufficient for understanding the recovery of communities.

Cereal grasses of the Triticeae tribe have been the major food source in temperate regions since the dawn of agriculture. Their large genomes are characterized by a high content of repetitive elements and large pericentromeric regions that are virtually devoid of meiotic recombination. Here we present a high-quality reference genome assembly for barley (Hordeum vulgare L.). We use chromosome conformation capture mapping to derive the linear order of sequences across the pericentromeric space and to investigate the spatial organization of chromatin in the nucleus at megabase resolution. The composition of genes and repetitive elements differs between distal and proximal regions. Gene family analyses reveal lineage-specific duplications of genes involved in the transport of nutrients to developing seeds and the mobilization of carbohydrates in grains. We demonstrate the importance of the barley reference sequence for breeding by inspecting the genomic partitioning of sequence variation in modern elite germplasm, highlighting regions vulnerable to genetic erosion.

Population viability analysis (PVA) models are used to estimate population extinction risk under different scenarios. Both simple and complex PVA models are developed and have their specific pros and cons; the question therefore arises whether we always use the most appropriate model type. Generally, the specific purpose of a model and the availability of data are listed as determining the choice of model type, but this has not been formally tested yet. We quantified the relative importance of model purpose and nine metrics of data availability and resolution for the choice of a PVA model type, while controlling for effects of the different life histories of the modelled species. We evaluated 37 model pairs: each consisting of a generally simpler, population-based model (PBM) and a more complex, individual-based model (IBM) developed for the same species. The choice of model type was primarily affected by the availability and resolution of demographic, dispersal and spatial data. Low-resolution data resulted in the development of less complex models. Model purpose did not affect the choice of the model type. We confirm the general assumption that poor data availability is the main reason for the wide use of simpler models, which may have limited predictive power for population responses to changing environmental conditions. Conservation biology is a crisis discipline where researchers learned to work with the data at hand. However, for threatened and poorly-known species, there is no short-cut when developing either a PBM or an IBM: investments to collect appropriately detailed data are required to ensure PVA models can assess extinction risk under complex environmental conditions.

Most research that demonstrates enhancement and stabilization of ecosystem functioning due to biodiversity is based on biodiversity manipulations within one trophic level and measuring changes in ecosystem functions provided by that same trophic level. However, it is less understood whether and how modifications of biodiversity at one trophic level propagate vertically to affect those functions supplied by connected trophic levels or by the whole ecosystem. Moreover, most experimental designs in biodiversity–ecosystem functioning research assume random species loss, which may be of little relevance to non-randomly assembled communities. Here, we used data from a published ecotoxicological experiment in which an insecticide gradient was applied as an environmental filter to shape consumer biodiversity. We tested how non-random consumer diversity loss affected gross primary production (an ecosystem function provided by producers) and respiration (an ecosystem function provided by the ecosystem as whole) in species-rich multitrophic freshwater communities (total of 128 macroinvertebrate and 59 zooplankton species across treatments). The insecticide decreased and destabilized macroinvertebrate and, to a lesser extent, zooplankton diversity. However, these effects on biodiversity neither affected nor destabilized any of the two studied ecosystem functions. The main reason for this result was that species susceptible to environmental filtering were different from those most strongly contributing to ecosystem functioning. The insecticide negatively affected the most abundant species, whereas much less abundant species had the strongest effects on ecosystem functioning. The latter finding may be explained by differences in body size and feeding guild membership. Our results indicate that biodiversity modifications within one trophic level induced by non-random species loss do not necessarily translate into changes in ecosystem functioning supported by other trophic levels o

Environmental change is expected to shift the geographic range of species and communities. To estimate the consequences of these shifts for the functioning and stability of ecosystems, reliable predictions of alterations in species distributions are needed. Projections with correlative species distribution models, which correlate species’ distributions to the abiotic environment, have become a standard approach. Criticism of this approach centres around the omission of relevant biotic feedbacks and triggered the search for alternatives. A new generation of mechanistic process-based species distribution models aims at implementing formulations of relevant biotic processes to cover species’ life histories, physiology, dispersal abilities, evolution, and both intra- and interspecific interactions. Although this step towards more structural realism is considered important, it remains unclear whether the resulting projections are more reliable. Structural realism has the advantage that geographic range shifting emerges from the interplay of relevant abiotic and biotic processes. Having implemented the relevant response mechanisms, structural realistic models should better tackle the challenge of generating projections of species responses to (non-analogous) environmental change. However, reliable projections of future species ranges demand ecological information that is currently only available for few species. In this opinion paper, we discuss how the discrepancy between demand for structural realism on the one hand and the related knowledge gaps on the other hand affects the reliability of mechanistic species distribution models. We argue that omission of relevant processes potentially impairs projection accuracy (proximity of the mean outcome to the true value), particularly if species range shifts emerge from species and community dynamics. Yet, insufficient knowledge that limits model specification and parameterization, as well as process complexity, increases proje