Although habitat loss is the predominant factor leading to biodiversity loss in the Anthropocene
, exactly how this loss manifests-and at which scales-remains a central debate
. The 'passive ...sampling' hypothesis suggests that species are lost in proportion to their abundance and distribution in the natural habitat
, whereas the 'ecosystem decay' hypothesis suggests that ecological processes change in smaller and more-isolated habitats such that more species are lost than would have been expected simply through loss of habitat alone
. Generalizable tests of these hypotheses have been limited by heterogeneous sampling designs and a narrow focus on estimates of species richness that are strongly dependent on scale. Here we analyse 123 studies of assemblage-level abundances of focal taxa taken from multiple habitat fragments of varying size to evaluate the influence of passive sampling and ecosystem decay on biodiversity loss. We found overall support for the ecosystem decay hypothesis. Across all studies, ecosystems and taxa, biodiversity estimates from smaller habitat fragments-when controlled for sampling effort-contain fewer individuals, fewer species and less-even communities than expected from a sample of larger fragments. However, the diversity loss due to ecosystem decay in some studies (for example, those in which habitat loss took place more than 100 years ago) was less than expected from the overall pattern, as a result of compositional turnover by species that were not originally present in the intact habitats. We conclude that the incorporation of non-passive effects of habitat loss on biodiversity change will improve biodiversity scenarios under future land use, and planning for habitat protection and restoration.
Human activities are fundamentally altering biodiversity. Projections of declines at the global scale are contrasted by highly variable trends at local scales, suggesting that biodiversity change may ...be spatially structured. Here, we examined spatial variation in species richness and composition change using more than 50,000 biodiversity time series from 239 studies and found clear geographic variation in biodiversity change. Rapid compositional change is prevalent, with marine biomes exceeding and terrestrial biomes trailing the overall trend. Assemblage richness is not changing on average, although locations exhibiting increasing and decreasing trends of up to about 20% per year were found in some marine studies. At local scales, widespread compositional reorganization is most often decoupled from richness change, and biodiversity change is strongest and most variable in the oceans.
Because biodiversity is multidimensional and scale‐dependent, it is challenging to estimate its change. However, it is unclear (1) how much scale‐dependence matters for empirical studies, and (2) if ...it does matter, how exactly we should quantify biodiversity change. To address the first question, we analysed studies with comparisons among multiple assemblages, and found that rarefaction curves frequently crossed, implying reversals in the ranking of species richness across spatial scales. Moreover, the most frequently measured aspect of diversity – species richness – was poorly correlated with other measures of diversity. Second, we collated studies that included spatial scale in their estimates of biodiversity change in response to ecological drivers and found frequent and strong scale‐dependence, including nearly 10% of studies which showed that biodiversity changes switched directions across scales. Having established the complexity of empirical biodiversity comparisons, we describe a synthesis of methods based on rarefaction curves that allow more explicit analyses of spatial and sampling effects on biodiversity comparisons. We use a case study of nutrient additions in experimental ponds to illustrate how this multi‐dimensional and multi‐scale perspective informs the responses of biodiversity to ecological drivers.
While human activities are known to elicit rapid turnover in species composition through time, the properties of the species that increase or decrease their spatial occupancy underlying this turnover ...are less clear. Here, we used an extensive dataset of 238 metacommunity time series of multiple taxa spread across the globe to evaluate whether species that are more widespread (large-ranged species) differed in how they changed their site occupancy over the 10-90 years the metacommunities were monitored relative to species that are more narrowly distributed (small-ranged species). We found that on average, large-ranged species tended to increase in occupancy through time, whereas small-ranged species tended to decrease. These relationships were stronger in marine than in terrestrial and freshwater realms. However, in terrestrial regions, the directional changes in occupancy were less extreme in protected areas. Our findings provide evidence for systematic decreases in occupancy of small-ranged species, and that habitat protection could mitigate these losses in the face of environmental change.
Macroecology is the study of the mechanisms underlying general patterns of ecology across scales. Research in microbial ecology and macroecology have long been detached. Here, we argue that it is ...time to bridge the gap, as they share a common currency of species and individuals, and a common goal of understanding the causes and consequences of changes in biodiversity. Microbial ecology and macroecology will mutually benefit from a unified research agenda and shared datasets that span the entirety of the biodiversity of life and the geographic expanse of the Earth.
Macroecology is the study of the mechanisms underlying general patterns of ecology across scales. A major focus of research within macroecology is understanding biodiversity patterns and their underlying processes. The field of macroecology has been biased towards charismatic macroorganisms (also known as macrobes), and has largely ignored insights and breadth that can be gained by considering microorganisms.
We argue that microbial ecology and macroecology are united by common currencies (individuals and species), as well as by comparable challenges of documenting their distributions and abundances.
Future directions that would lead to a unified macroecology include: expansion of spatial and temporal scales to encompass the diversity of microbes; synthesis-driven, systematic comparisons of macrobial and microbial macroecological patterns and processes; and support of interdisciplinary approaches in training, publishing, and funding to equitably value macrobial and microbial insights into understanding the rules and exceptions of life.
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.
摘要
估计生物多样性变化对于生态系统的管理和保护至关重要。准确的估计依赖于选择代表性位点, 但监测却往往集中于研究者特别感兴趣的位点。这种位点选择的偏倚如何影响对生物多样性变化的估计, 在很大程度上仍不清楚。位点选择偏倚可能出现在四种来源的生物多样性数据中, 按可能性从大到小排序依次为公民科学、博物馆、国家公园监测和学术研究。我们将位点选择偏倚定义为种群密度大 (即丰度偏倚) 和物种丰富 (丰富度偏倚) 两种类型。本研究在虚拟景观中模拟了生物多样性的变化, 并在一个采样点持续记录观察到的生物多样性。位点是随机选择或有偏倚地选择的。接下来, 我们使用基于个体的简单空间解析模型来预测个体在选定采样位点内外的移动或扩散。与随机选择位点相比, 偏倚性地选择位点对生物多样性损失的估计平均夸大了 300‐400% 。根据我们的模拟, 位点选择偏倚会导致正趋势被估计为负趋势: 在随机选择的位点, 丰富度增加被估计为 0.1, 而偏倚性选择的位点, 丰富度平均变化为 −0.1到−0.2 。因此, 位点选择偏倚可能错误地表明生物多样性减少。我们改变了采样设计和物种特征, 结果发现在时间序列短、粒度小、生物扩散能力低、区域物种库较大和空间聚集较强的情况下, 位点选择偏倚最明显。基于以上发现, 为了最大程度地减少位点选择偏倚, 我们建议采用系统的位点选择方案、最大化采样区域、累积计算地块间的生物多样性, 以及使用对稀有物种不敏感的生物多样性指标, 例如有效物种数量。在生物多样性监测、生物多样性变化研究设计以及对现有数据的解释中, 都应认识到位点选择偏倚的潜在影响。翻译: 胡怡思; 审校: 聂永刚
Article impact statement: Nonsubjective site selection is important for accurate estimation of the direction of biodiversity change.
Little consensus has emerged regarding how proximate and ultimate drivers such as productivity, disturbance and temperature may affect species richness and other aspects of biodiversity. Part of the ...confusion is that most studies examine species richness at a single spatial scale and ignore how the underlying components of species richness can vary with spatial scale.
We provide an approach for the measurement of biodiversity that decomposes changes in species rarefaction curves into proximate components attributed to: (a) the species abundance distribution, (b) density of individuals and (c) the spatial arrangement of individuals. We decompose species richness by comparing spatial and nonspatial sample‐ and individual‐based species rarefaction curves that differentially capture the influence of these components to estimate the relative importance of each in driving patterns of species richness change.
We tested the validity of our method on simulated data, and we demonstrate it on empirical data on plant species richness in invaded and uninvaded woodlands. We integrated these methods into a new r package (mobr).
The metrics that mobr provides will allow ecologists to move beyond comparisons of species richness in response to ecological drivers at a single spatial scale toward a dissection of the proximate components that determine species richness across scales.
Zusammenfassung
Es herrscht nur wenig Konsens darüber, auf welche Weise unmittelbare und mittelbare Faktoren wie Produktivität, Störung und Temperatur die Artenzahl und andere Aspekte der Biodiversität beeinflussen. Zum Teil rührt diese Unklarheit daher, dass die meisten Studien die Artenzahl nur auf einer einzigen räumlichen Skala betrachten und dabei außer Acht lassen, wie die zugrundeliegenden Komponenten der Artenzahl mit der räumlichen Skala variieren können.
Hier stellen wir unseren Ansatz “measurement of biodiversity” vor, mit dem Unterschiede zwischen Rarefaction‐Kurven auf die unmittelbaren Komponenten der Artenzahl zurückgeführt werden können. Dies sind: (a) Die Abundanzverteilung der Arten, (b) die Individuendichte und (c) die räumliche Anordnung der Individuen. Um den relativen Beitrag dieser Komponenten an der Änderung der Artenzahl einzuschätzen, teilen wir diese mithilfe von räumlichen und nicht‐räumlichen, Stichproben‐ und Individuen‐basierten Rarefaction‐Kurven auf, die den Einfluss der Komponenten auf unterschiedliche Weise widerspiegeln.
Wir haben unsere Methode mit simulierten Daten validiert und zeigen ihre Anwendung an einem empirischen Fallbeispiel zur Artenzahl in Wäldern mit und ohne invasive Arten. Unsere Methode wird in einem neuen r‐paket (mobr) zur Verfügung gestellt.
Die Biodiversitätsmetriken, die von mobr ausgegeben werden, erlauben es Ökologen, einen differenzierteren Blick auf Biodiversitätsmuster zu werfen: Statt die Änderung von Artenzahl auf einer einzigen räumlichen Skala zu betrachten, kann der Effekt von ökologischen Faktoren auf die unmittelbaren Komponenten der Artentenzahl skalenübergreifend analysiert werden.
Biodiversity metrics often integrate data on the presence and abundance of multiple species. Yet our understanding of covariation between changes to the numbers of individuals, the evenness of ...species relative abundances, and the total number of species remains limited. Using individual‐based rarefaction curves, we show how expected positive relationships among changes in abundance, evenness and richness arise, and how they can break down. We then examined interdependencies between changes in abundance, evenness and richness in more than 1100 assemblages sampled either through time or across space. As predicted, richness changes were greatest when abundance and evenness changed in the same direction, and countervailing changes in abundance and evenness acted to constrain the magnitude of changes in species richness. Site‐to‐site differences in abundance, evenness, and richness were often decoupled, and pairwise relationships between these components across assemblages were weak. In contrast, changes in species richness and relative abundance were strongly correlated for assemblages varying through time. Temporal changes in local biodiversity showed greater inertia and stronger relationships between the component changes when compared to site‐to‐site variation. Overall, local variation in assemblage diversity was rarely due to repeated passive samples from an approximately static species abundance distribution. Instead, changing species relative abundances often dominated local variation in diversity. Moreover, how changing relative abundances combined with changes to total abundance frequently determined the magnitude of richness changes. Embracing the interdependencies between changing abundance, evenness and richness can provide new information to better understand biodiversity change in the Anthropocene.
While land use intensification is a major driver of biodiversity change in streams, the nature of such changes, and at which scales they occur, have not been synthesized. To synthesize how land use ...change has altered multiple components of stream biodiversity across scales, we compiled data from 37 studies where comparative data were available for species’ total and relative abundances from multiple locations including reference (less impacted) streams to those surrounded by different land use types (urban, forestry, and agriculture). We found that each type of land use reduced multiple components of within-stream biodiversity across scales, but that urbanization consistently had the strongest effects. However, we found that β-diversity among streams in modified landscapes did not differ from β-diversity observed among reference streams, suggesting little evidence for biotic homogenization. Nevertheless, assemblage composition did experience considerable species turnover between reference and modified streams. Our results emphasize that to understand how anthropogenic factors such as land use alter biodiversity, multiple components of biodiversity within and among sites must be simultaneously considered at multiple scales.
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