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.

Does the Chemodiversity of Bacterial Exometabolomes Sustain the Chemodiversity of Marine Dissolved Organic Matter?

Marine dissolved organic matter (DOM) is a complex mixture of chemical compounds. At 750 Pg C, it is one of the biggest pools of reduced carbon on Earth. It has been proposed that the diversity of DOM is responsible for its recalcitrance. We hypothesize that the chemodiversity of marine DOM is a reflection of the chemodiversity of bacterial exometabolomes. To test this, we incubated two model strains of the Roseobacter group; Phaeobacter inhibens and Dinoroseobacter shibae in pure culture using three different simple organic compounds as sole carbon sources (glutamate, glucose, and acetate and succinate for P. inhibens and D. shibae, respectively). The exometabolome of the model organisms was characterized using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) and ecological diversity measures. We detected thousands of molecular masses in the exometabolomes of P. inhibens and D. shibae (21,105 and 9,386, respectively), reflecting the capability of single bacterial strains to diversify simple organic compounds. The chemical composition of the exometabolomes changed with growth phase and also differed according to the strain incubated and the utilized substrate. We mimicked a higher diversity of substrates, bacterial species and heterogeneous growth (different growth phases) to approach the complexity of natural environments, by computationally creating combinations of detected exometabolomes. We compared the chemodiversity of these combinations, indicative for chemodiversity of freshly produced microbial DOM to that of refractory DOM from one of the oldest oceanic water masses (North Equatorial Pacific Intermediate Water). Some combinations of exometabolomes showed higher richness than the deep ocean refractory DOM, and all the combinations showed higher functional diversity. About 15% of the 13,509 molecular formulae detected in exometabolomes and refractory oceanic DOM were shared, i.e., occurred in Roseobacter exometabolomes and in deep water samples. This overlap provides further support for our hypothesis that marine bacteria from the Roseobacter group contribute to the sustainability of marine DOM chemodiversity and stability.