Climate change disrupts core habitats of marine species.

Driven by climate change, marine biodiversity is undergoing a phase of rapid change that has proven to be even faster than changes observed in terrestrial ecosystems. Understanding how these changes in species composition will affect future marine life is crucial for conservation management, especially due to increasing demands for marine natural resources. Here, we analyse predictions of a multiparameter habitat suitability model covering the global projected ranges of >33,500 marine species from climate model projections under three CO2 emission scenarios (RCP2.6, RCP4.5, RCP8.5) up to the year 2100. Our results show that the core habitat area will decline for many species, resulting in a net loss of 50% of the core habitat area for almost half of all marine species in 2100 under the high-emission scenario RCP8.5. As an additional consequence of the continuing distributional reorganization of marine life, gaps around the equator will appear for 8% (RCP2.6), 24% (RCP4.5), and 88% (RCP8.5) of marine species with cross-equatorial ranges. For many more species, continuous distributional ranges will be disrupted, thus reducing effective population size. In addition, high invasion rates in higher latitudes and polar regions will lead to substantial changes in the ecosystem and food web structure, particularly regarding the introduction of new predators. Overall, our study highlights that the degree of spatial and structural reorganization of marine life with ensued consequences for ecosystem functionality and conservation efforts will critically depend on the realized greenhouse gas emission pathway.

General statistical scaling laws for stability in ecological systems.

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.

Effects of site-selection bias on estimates of biodiversity change.

Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300-400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of -0.1 to -0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.

Efectos del Sesgo en la Selección de Sitio sobre las Estimaciones del Cambio en la Biodiversidad Resumen Las estimaciones del cambio en la biodiversidad son esenciales para el manejo y la conservación de los ecosistemas. Las estimaciones precisas dependen de la selección de sitios representativos pero su monitoreo con frecuencia se enfoca en los sitios de interés especial. En su mayoría se desconoce cómo influyen tales sesgos en la selección de sitios sobre las estimaciones del cambio en la biodiversidad. El sesgo en la selección de sitios ocurre potencialmente en cuatro fuentes principales de datos sobre biodiversidad, disminuyendo en probabilidad cuando los datos vienen de la ciencia ciudadana, museos, el monitoreo de los parques nacionales y la investigación académica. Definimos al sesgo en la selección de sitios como la preferencia por sitios que están densamente poblados (es decir, sesgo por abundancia) o que son ricos en especies (es decir, sesgo por riqueza). Simulamos el cambio en la biodiversidad en un paisaje virtual y le dimos seguimiento a la biodiversidad observada en un sitio muestreado. El sitio fue seleccionado al azar o con un sesgo en la selección de sitio. Usamos un modelo simple basado en los individuos y resuelto espacialmente para predecir el movimiento o la dispersión de los individuos dentro y fuera del sitio de muestreo elegido. El sesgo en la selección de sitio exageró las estimaciones de la pérdida de la biodiversidad en los sitios seleccionados con un sesgo en promedio de 300-400% en comparación con sitios seleccionados al azar. Con base en nuestras simulaciones, el sesgo en la selección de sitio derivó en que las tendencias positivas se estimaran como tendencias negativas: se estimó que el incremento en la riqueza fue de 0.1 en sitios seleccionados al azar, mientras que en los sitios seleccionados con un sesgo mostraron un cambio en la riqueza de −0.1 a −0.2 en promedio. Así, el sesgo en la selección de sitio puede indicar erróneamente la existencia de disminuciones en la biodiversidad. Variamos el diseño del muestreo y las características de las especies y encontramos que los sesgos en la selección de sitio estaban más consolidados en las series de tiempo corto, para los granos pequeños, organismos con una baja habilidad de dispersión, grandes patrimonios genéticos de especies regionales y una agregación espacial fuerte. Con base en estos resultados, para lograr minimizar el sesgo en la selección de sitio, recomendamos usar esquemas sistemáticos de selección de sitio; maximizar el área de muestreo; calcular las medidas de biodiversidad acumulativamente en los lotes; y usar las medidas de biodiversidad que son menos sensibles a las especies raras, como el número efectivo de especies. Se necesita tener conciencia sobre el impacto potencial del sesgo en la selección de sitio para el monitoreo de la biodiversidad, el diseño de nuevos estudios sobre el cambio en la biodiversidad y la interpretación de los datos existentes.

Thresholds for ecological responses to global change do not emerge from empirical data.

To understand ecosystem responses to anthropogenic global change, a prevailing framework is the definition of threshold levels of pressure, above which response magnitudes and their variances increase disproportionately. However, we lack systematic quantitative evidence as to whether empirical data allow definition of such thresholds. Here, we summarize 36 meta-analyses measuring more than 4,600 global change impacts on natural communities. We find that threshold transgressions were rarely detectable, either within or across meta-analyses. Instead, ecological responses were characterized mostly by progressively increasing magnitude and variance when pressure increased. Sensitivity analyses with modelled data revealed that minor variances in the response are sufficient to preclude the detection of thresholds from data, even if they are present. The simulations reinforced our contention that global change biology needs to abandon the general expectation that system properties allow defining thresholds as a way to manage nature under global change. Rather, highly variable responses, even under weak pressures, suggest that 'safe-operating spaces' are unlikely to be quantifiable.

Biodiversity-ecosystem functioning relationships in fish communities: biomass is related to evenness and the environment, not to species richness.

The relationship between biodiversity and ecosystem functioning (BEF) is a topic of considerable interest to scientists and managers because a better understanding of its underlying mechanisms may help us mitigate the consequences of biodiversity loss on ecosystems. Our current knowledge of BEF relies heavily on theoretical and experimental studies, typically conducted on a narrow range of spatio-temporal scales, environmental conditions, and trophic levels. Hence, whether a relationship holds in the natural environment is poorly understood, especially in exploited marine ecosystems. Using large-scale observations of marine fish communities, we applied a structural equation modelling framework to investigate the existence and significance of BEF relationships across northwestern European seas. We find that ecosystem functioning, here represented by spatial patterns in total fish biomass, is unrelated to species richness-the most commonly used diversity metric in BEF studies. Instead, community evenness, differences in species composition, and abiotic variables are significant drivers. In particular, we find that high fish biomass is associated with fish assemblages dominated by a few generalist species of a high trophic level, who are able to exploit both the benthic and pelagic energy pathway. Our study provides a better understanding of the mechanisms behind marine ecosystem functioning and allows for the integration of biodiversity into management considerations.

Spatial heterogeneity in species composition constrains plant community responses to herbivory and fertilisation.

Environmental change can result in substantial shifts in community composition. The associated immigration and extinction events are likely constrained by the spatial distribution of species. Still, studies on environmental change typically quantify biotic responses at single spatial (time series within a single plot) or temporal (spatial beta diversity at single time points) scales, ignoring their potential interdependence. Here, we use data from a global network of grassland experiments to determine how turnover responses to two major forms of environmental change - fertilisation and herbivore loss - are affected by species pool size and spatial compositional heterogeneity. Fertilisation led to higher rates of local extinction, whereas turnover in herbivore exclusion plots was driven by species replacement. Overall, sites with more spatially heterogeneous composition showed significantly higher rates of annual turnover, independent of species pool size and treatment. Taking into account spatial biodiversity aspects will therefore improve our understanding of consequences of global and anthropogenic change on community dynamics.

High wind speeds prevent formation of a distinct bacterioneuston community in the sea-surface microlayer.

The sea-surface microlayer (SML) at the boundary between atmosphere and hydrosphere represents a demanding habitat for bacteria. Wind speed is a crucial but poorly studied factor for its physical integrity. Increasing atmospheric burden of CO2, as suggested for future climate scenarios, may particularly act on this habitat at the air-sea interface. We investigated the effect of increasing wind speeds and different pCO2 levels on SML microbial communities in a wind-wave tunnel, which offered the advantage of low spatial and temporal variability. We found that enrichment of bacteria in the SML occurred solely at a U10 wind speed of ≤5.6 m s-1 in the tunnel and ≤4.1 m s-1 in the Baltic Sea. High pCO2 levels further intensified the bacterial enrichment in the SML during low wind speed. In addition, low wind speed and pCO2 induced the formation of a distinctive bacterial community as revealed by 16S rRNA gene fingerprints and influenced the presence or absence of individual taxonomic units within the SML. We conclude that physical stability of the SML below a system-specific wind speed threshold induces specific bacterial communities in the SML entailing strong implications for ecosystem functioning by wind-driven impacts on habitat properties, gas exchange and matter cycling processes.

Phytoplankton responses to temperature increases are constrained by abiotic conditions and community composition.

Effects of temperature changes on phytoplankton communities seem to be highly context-specific, but few studies have analyzed whether this context specificity depends on differences in the abiotic conditions or in species composition between studies. We present an experiment that allows disentangling the contribution of abiotic and biotic differences in shaping the response to two aspects of temperature change: permanent increase of mean temperature versus pulse disturbance in form of a heat wave. We used natural communities from six different sites of a floodplain system as well as artificially mixed communities from laboratory cultures and grew both, artificial and natural communities, in water from the six different floodplain lakes (sites). All 12 contexts (2 communities × 6 sites) were first exposed to three different temperature levels (12, 18, 24 °C, respectively) and afterward to temperature pulses (4 °C increase for 7 h day(-1)). Temperature-dependent changes in biomass and community composition depended on the initial composition of phytoplankton communities. Abiotic conditions had a major effect on biomass of phytoplankton communities exposed to different temperature conditions, however, the effect of biotic and abiotic conditions together was even more pronounced. Additionally, phytoplankton community responses to pulse temperature effects depended on the warming history. By disentangling abiotic and biotic effects, our study shows that temperature-dependent effects on phytoplankton communities depend on both, biotic and abiotic constraints.

Environmental and trait variability constrain community structure and the biodiversity-productivity relationship.

There is still considerable debate about which mechanisms drive the relationship between biodiversity and ecosystem function (BEF). Although most scientists agree on the existence of two underlying mechanisms, complementarity and selection, experimental studies keep producing contrasting results on the relative contributions of the two effects. We present a spatially explicit resource competition model and investigate how the strength of these effects is influenced by trait and environmental variability, resource distribution, and species pool size. Our results demonstrate that the increase of biomass production with increasing species numbers depends on the concurrence of environmental and trait variability: BEF relationships are stronger if functionally different species coexist in a landscape with heterogeneous resource supply. These large biodiversity effects arise from complementarity effects, whereas selection effects are maximized when broad trait ranges coincide with narrow ranges of resource supply ratios. Our results will therefore help to resolve the debate on complementarity and selection mechanisms.

Unifying ecological stoichiometry and metabolic theory to predict production and trophic transfer in a marine planktonic food web.

Two ecological frameworks have been used to explain multitrophic interactions, but rarely in combination: (i) ecological stoichiometry (ES), explaining consumption rates in response to consumers' demand and prey's nutrient content; and (ii) metabolic theory of ecology (MTE), proposing that temperature and body mass affect metabolic rates, growth and consumption rates. Here we combined both, ES and MTE to investigate interactive effects of phytoplankton prey stoichiometry, temperature and zooplankton consumer body mass on consumer grazing rates and production in a microcosm experiment. A simple model integrating parameters from both frameworks was used to predict interactive effects of temperature and nutrient conditions on consumer performance. Overall, model predictions reflected experimental patterns well: consumer grazing rates and production increased with temperature, as could be expected based on MTE. With decreasing algal food quality, grazing rates increased due to compensatory feeding, while consumer growth rates and final biovolume decreased. Nutrient effects on consumer biovolume increased with increasing temperature, while nutrient effects on grazing rates decreased. Highly interactive effects of temperature and nutrient supply indicate that combining the frameworks of ES and MTE is highly important to enhance our ability to predict ecosystem functioning in the context of global change.

Species richness and biomass explain spatial turnover in ecosystem functioning across tropical and temperate ecosystems.

Predicting ecosystem functioning at large spatial scales rests on our ability to scale up from local plots to landscapes, but this is highly contingent on our understanding of how functioning varies through space. Such an understanding has been hampered by a strong experimental focus of biodiversity-ecosystem functioning research restricted to small spatial scales. To address this limitation, we investigate the drivers of spatial variation in multitrophic energy flux-a measure of ecosystem functioning in complex communities-at the landscape scale. We use a structural equation modelling framework based on distance matrices to test how spatial and environmental distances drive variation in community energy flux via four mechanisms: species composition, species richness, niche complementarity and biomass. We found that in both a tropical and a temperate study region, geographical and environmental distance indirectly influence species richness and biomass, with clear evidence that these are the dominant mechanisms explaining variability in community energy flux over spatial and environmental gradients. Our results reveal that species composition and trait variability may become redundant in predicting ecosystem functioning at the landscape scale. Instead, we demonstrate that species richness and total biomass may best predict rates of ecosystem functioning at larger spatial scales.