German Centre for Integrative Biodiversity Research

While both species richness and ecosystem stability increase with area, how these scaling patterns are linked remains unclear. Our theoretical and empirical analyses of plant and fish communities show that the spatial scaling of ecosystem stability is determined primarily by the scaling of species asynchrony, which is in turn driven by the scaling of species richness. In wetter regions, plant species richness and ecosystem stability both exhibit faster accumulation with area, implying potentially greater declines in biodiversity and stability following habitat loss. The decline in ecosystem stability after habitat loss can be delayed, creating a stability debt mirroring the extinction debt of species. By unifying two foundational scaling laws in ecology, our work underscores that ongoing biodiversity loss may destabilize ecosystems across spatial scales.

Despite advances in theory and experiments, how biodiversity influences the structure and functioning of natural ecosystems remains debated. By applying new theory to data on 84,695 plant, animal, and protist assemblages, we show that the general positive effect of species richness on stocks of biomass, as well as much of the variation in the strength and sign of this effect, is predicted by a fundamental macroecological quantity: the scaling of species abundance with body mass. Standing biomass increases with richness when large-bodied species are numerically rare but is independent of richness when species size and abundance are uncoupled. These results suggest a new fundamental law in the structure of ecological communities and show that the impacts of changes in species richness on biomass are predictable.

Herbivores sharing host plants are often temporally and spatially separated, limiting direct interactions between them. Nevertheless, as observed in numerous aboveground study systems, they can reciprocally influence each other via systemically induced plant responses. In contrast, examples of such plant-mediated interactions between belowground herbivores are scarce; however, we postulated that they similarly occur, given the large diversity of root-interacting soil organisms. To test this hypothesis, we analyzed the performance of cabbage root fly (Delia radicum) larvae feeding on the main roots of field mustard (Brassica rapa) plants whose fine roots were infected by the root-knot nematode (Meloidogyne incognita). Simultaneously, we studied the effects of M. incognita on D. radicum-induced defense responses and the accumulation of primary metabolites in the main root. We observed that almost 1.5 times as many D. radicum adults emerged from nematode-infected plants, indicating a facilitation effect of M. incognita infection. Although we observed increases in the accumulation of proteins and two essential amino acids, the strongest effect of nematode infection was visible in the defense response to D. radicum. We observed a 1.5 times higher accumulation of the defense-related phytohormone JA-Ile in response to D. radicum on nematode-infected plants, coinciding with a 75% increase in indole glucosinolate concentrations. Contrastingly, concentrations of aliphatic glucosinolates, secondary metabolites negatively affecting D. radicum, were 10-25% lower in nematode-infected plants. We hypothesize that the attenuated aliphatic glucosinolate concentrations result from antagonistic interactions between biosynthetic pathways of both glucosinolate classes, which was reflected in the expression of key biosynthesis genes. Our results provide explicit evidence of plant-mediated interactions between belowground organisms, likely via systemically induced responses in roots.

Variation in life histories influences demographic processes, from adaptive changes to population declines leading to extinction. Among life history traits, generation length offers a critical feature to forecast species' demographic trajectories such as population declines (widely used by the IUCN Red List) and adaptability to environmental change over time. Therefore, estimates of generation length are crucial to monitor demographic stability or predict future changes in highly threatened organisms, particularly amphibians and reptiles, which are particularly threatened among vertebrates and for which uncertainty in future impacts remains high. Despite its importance, generation length for amphibians and reptiles is largely missing. Here, we aim to fill in this gap by modeling generation lengths for amphibians, squamates and testudines as a function of species size, climate, life history and phylogeny using generalized additive models and phylogenetic generalized least squares. We estimated generation lengths for 5059 (57%) amphibians, 8722 (73%) squamates and 117 (32%) testudines. Our models performed well for most families (e.g. Bufonidae among amphibians, Lacertidae and Colubridae among squamates, and Geoemydidae among testudines) while we found high uncertainty around the prediction of a few families, notably Chamaeleonidae. Species' body size and mean temperature were the main predictors of generation length in all groups. Although our estimates are not meant to substitute robust and validated measurements from field studies or natural history museums, they can help reduce existing biases in conservation assessments until field data is comprehensively available.

Trees are an important carbon sink as they accumulate biomass through photosynthesis¹. Identifying tree species that grow fast is therefore commonly considered to be essential for effective climate change mitigation through forest planting. Although species characteristics are key information for plantation design and forest management, field studies often fail to detect clear relationships between species functional traits and tree growth². Here, by consolidating four independent datasets and classifying the acquisitive and conservative species based on their functional trait values, we show that acquisitive tree species, which are supposedly fast-growing species, generally grow slowly in field conditions. This discrepancy between the current paradigm and field observations is explained by the interactions with environmental conditions that influence growth. Acquisitive species require moist mild climates and fertile soils, conditions that are generally not met in the field. By contrast, conservative species, which are supposedly slow-growing species, show generally higher realized growth due to their ability to tolerate unfavourable environmental conditions. In general, conservative tree species grow more steadily than acquisitive tree species in non-tropical forests. We recommend planting acquisitive tree species in areas where they can realize their fast-growing potential. In other regions, where environmental stress is higher, conservative tree species have a larger potential to fix carbon in their biomass.

There is considerable interest in understanding patterns of β‐diversity that measure the amount of change in species composition through space or time. Most hypotheses for β‐diversity evoke nonrandom processes that generate spatial and temporal within‐species aggregation; however, β‐diversity can also be driven by random sampling processes. Here, we describe a framework based on rarefaction curves that quantifies the nonrandom contribution of species compositional differences across samples to β‐diversity. We isolate the effect of within‐species spatial or temporal aggregation on beta‐diversity using a coverage standardized metric of β‐diversity (βC). We demonstrate the utility of our framework using simulations and an empirical case study examining variation in avian species composition through space and time in engineered versus natural riparian areas. The primary strengths of our approach are that it provides an intuitive visual null model for expected patterns of biodiversity under random sampling that allows integrating analyses across α‐, γ‐, and β‐scales. Importantly, the method can accommodate comparisons between communities with different species pool sizes, and it can be used to examine species turnover both within and between meta‐communities.

Ongoing ecosystem change and biodiversity decline across the Afrotropics call for tools to monitor the state of biodiversity or ecosystem elements across extensive spatial and temporal scales. We assessed relationships in the co‐occurrence patterns between great apes and other medium to large‐bodied mammals to evaluate whether ape abundance serves as a proxy for mammal diversity across broad spatial scales. We used camera trap footage recorded at 22 research sites, each known to harbor a population of chimpanzees, and some additionally a population of gorillas, across 12 sub‐Saharan African countries. From ~350,000 1‐min camera trap videos recorded between 2010 and 2016, we estimated mammalian community metrics, including species richness, Shannon diversity, and mean animal mass. We then fitted Bayesian Regression Models to assess potential relationships between ape detection rates (as proxy for ape abundance) and these metrics. We included site‐level protection status, human footprint, and precipitation variance as control variables. We found that relationships between detection rates of great apes and other mammal species, as well as animal mass were largely positive. In contrast, relationships between ape detection rate and mammal species richness were less clear and differed according to site protection and human impact context. We found no clear association between ape detection rate and mammal diversity. Our findings suggest that chimpanzees hold potential as indicators of specific elements of mammalian communities, especially population‐level and composition‐related characteristics. Declines in chimpanzee populations may indicate associated declines of sympatric medium to large‐bodied mammal species and highlight the need for improved conservation interventions.Changes in chimpanzee abundance likely precede extirpation of sympatric mammals.

Forbs (“wildflowers”) are important contributors to grassland biodiversity but are vulnerable to environmental changes. In a factorial experiment at 94 sites on 6 continents, we test the global generality of several broad predictions: (1) Forb cover and richness decline under nutrient enrichment, particularly nitrogen enrichment. (2) Forb cover and richness increase under herbivory by large mammals. (3) Forb richness and cover are less affected by nutrient enrichment and herbivory in more arid climates, because water limitation reduces the impacts of competition with grasses. (4) Forb families will respond differently to nutrient enrichment and mammalian herbivory due to differences in nutrient requirements. We find strong evidence for the first, partial support for the second, no support for the third, and support for the fourth prediction. Our results underscore that anthropogenic nitrogen addition is a major threat to grassland forbs, but grazing under high herbivore intensity can offset these nutrient effects.

The increasing strength of positive biodiversity effects on plant community productivity, observed in long‐term biodiversity experiments, relates to mixed responses at the species level. However, it is still not well understood if the observed mixed responses are adaptations to the different selection pressures in plant communities of different diversity or plastic adjustments. We conducted a transplant experiment for nine plant species in a 17‐year‐old biodiversity experiment (Jena Experiment). We used offspring of plants selected in the biodiversity experiment and from plants without selection in the experiment (naïve). In a Community History Experiment, offspring of selected plants were planted in three test environments: their original plant communities with old soil (of the long‐term Jena Experiment), newly assembled plant communities with old soil, and newly assembled plant communities with new soil. In a Selection Experiment, we compared selected plants with naïve plants, both grown in the selected plants' original environment. In all test environments, increasing species richness was associated with a decrease in plant individual biomass, reproductive output, relative growth rate, plant height, leaf greenness, and leaf nitrogen concentration, and an increase in specific leaf area (SLA). In the Selection Experiment, selected plants had a weaker decline in biomass, taller stature, and higher leaf carbon and nitrogen concentrations than naïve plants with increasing species richness. In the Community History Experiment, survival was lower, while plant height, SLA, leaf nitrogen, and carbon concentrations were highest in the test environment with new plants and soil. However, in high‐diversity communities, individuals produced more biomass, grew taller, and had higher leaf greenness in their original environment. Overall, we found that, despite the crucial role of phenotypic plasticity for trait adjustments to the actual environment, selection in the biodiversity experiment produced adaptive phenotypic responses, largely explained by plant community history and positive plant–soil feedbacks established over time.

16S rRNA gene metabarcoding, the study of amplicon sequences of the 16S rRNA gene from mixed environmental samples, is an increasingly popular and accessible method for assessing bacterial communities across a wide range of environments. As metabarcoding sequence data archives continue to grow, data reuse will likely become an important source of novel insights into the ecology of microbes. While recent work has demonstrated the benefits of longer read lengths for the study of microbial communities from 16S rRNA gene segments, no studies have explored the use of shorter (< 200 bp) read lengths in the context of data reuse. Nevertheless, this information is essential to improve the reuse and comparability of metabarcoding data across existing datasets. This study reanalyzed nine 16S rRNA datasets targeting aquatic, animal‐associated and soil microbiomes, and evaluated how processing the sequence data across a range of read lengths affected the resulting taxonomic assignments, biodiversity metrics and differential (i.e., before‐after treatment) analyses. Short read lengths successfully recovered ecological patterns and allowed for the use of more sequences. Limited increases in resolution were observed beyond 150 bp reads across environments. Furthermore, abundance‐weighted diversity metrics (e.g., Inverse Simpson index, Morisita‐Horn dissimilarities or weighted Unifrac distances) were more robust to variation in read lengths. Read lengths alone contributed to consistent increases in the total number of ASVs detected, highlighting the need to consider metabarcoding‐derived diversity estimates within the context of the bioinformatics parameters selected. This study provides evidence‐based guidelines for the processing of short reads.

The paper describes the production and evaluation of annual livestock densities of cattle, horses, sheep and goats (including per-pixel 95% probability prediction intervals) at 1 km spatial resolution for the 2000—2022 period using spatiotemporal Machine Learning. A compilation of subnational livestock census data has been imported, harmonized and used as reference data (52,883 census polygons and 678,266 individual data points; covering 86% of the potential land for livestock production) to build predictive models using correlation with a large stack of multi-source harmonized gridded/raster spatial layers (307 individual raster spatial layers harmonized at 1 km spatial resolution). Models were fitted using scikit-learn library with Recursive Feature Elimination and Poisson criteria to represent the distribution of the target variable. Intermediate layers estimating potential land for livestock production based on grassland and cropland extent, along with biophysical and socioeconomic predictors, were used to estimate the spatial domain of livestock. The final predictions at 1 km were further adjusted to annual headcounts based on FAOSTAT national statistics to ensure consistency. Model benchmarking based on 10% hold-out samples and cross-validation with refitting shows that Random Forest outperforms Gradient Boosting Tree for predicting livestock densities, with hold-out validation yielding R-square values of 0.437, 0.53, 0.574, 0.552, and RMSE values of 124.38, 4.01, 42.89 and 23.20 (heads per km-square) for cattle, horses, sheep and goats, respectively. Variable importance analysis shows that the key predictors include socio-economic layers, such as travel time to the nearest ports and cities, annual sub-national Gross Domestic Product (GDP) and religious population distribution. Further evaluation of maps shows that predictions suffer from large gaps in training data in parts of Africa and Asia; the spatial domain of livestock (active grazing/forage areas) is often difficult to validate, with many countries having very specific management cultures that can not be seamlessly represented using existing global raster layers, hence modeling distribution of livestock per country could help increase accuracy. The modeling pipeline is open source and available on Github (https://github.com/wri/global-pasture-watch) with output maps (ML predictions and FAOSTAT-adjusted values) publicly available under CC-BY license on Zenodo (https://doi.org/10.5281/zenodo.14933636)

Habitat fragmentation generally reduces biodiversity at the patch scale (α diversity)¹. However, there is ongoing debate about whether such negative effects can be alleviated at the landscape scale (γ diversity) if among-patch diversity (β diversity) increases as a result of fragmentation2, 3, 4, 5–6. This controversial view has not been rigorously tested. Here we use a dataset of 4,006 taxa across 37 studies from 6 continents to test the effects of fragmentation on biodiversity across scales by explicitly comparing continuous and fragmented landscapes. We find that fragmented landscapes consistently have both lower α diversity and lower γ diversity. Although fragmented landscapes did tend to have higher β diversity, this did not translate into higher γ diversity. Our findings refute claims that habitat fragmentation can increase biodiversity at landscape scales, and emphasize the need to restore habitat and increase connectivity to minimize biodiversity loss at ever-increasing scales.

The concept of growing degree days (GDDs) is commonly used to predict phenological events in plants, assuming that plants develop proportionally to the accumulated temperature. Two species‐specific parameters, TBase and t0 (minimum temperature above which and start date when GDDs begin to accumulate), are considered for the calculation. However, species‐specific optimised thresholds of wild herbaceous species remain sparse, and therefore the reliability of the models is questionable. By employing several modelling approaches using phenological records of leaf unfolding and flowering onset of 87 wild herbaceous species collected in six European botanical gardens between 2019 and 2024, we assessed the reliability of GDD models across a diverse array of species. We further examined whether thresholds of TBase and t0 for calculating GDD can be optimised for a large set of species and for single species. We aimed to estimate and evaluate these thresholds and the reliability of GDD models using species' temporal niche and bud traits to see whether for specific groups of species, specific GDD models work better. Our analyses revealed that GDD models for leaf unfolding and flowering onset performed better than the null model (i.e. mean date across years and species) for 84% and 70% of the species, respectively. Our results showed that species with intermediate temporal niches were less dependent on the selection of TBase and t0. Overall, we found better performance of the models using a TBase around 4°C for most of the species. By considering optimised thresholds, we found that predictions of leaf unfolding dates were more accurate in early‐growing species, and regarding the start date for temperature accumulation, we found that larger values for t0 are suitable for predictions for species with later leaf unfolding or flowering onset. Our results emphasise that simple temperature accumulating GDD models can be optimised by using the temporal niches of the studied species to approximate the underlying model parameters or by applying thresholds that are valid for many species. The use of simple but optimised GDD models can be advantageous for small datasets that would otherwise be overfitted with more complex models. Read the free Plain Language Summary for this article on the Journal blog.

Tropical forest canopies are the biosphere’s most concentrated atmospheric interface for carbon, water and energy1,2. However, in most Earth System Models, the diverse and heterogeneous tropical forest biome is represented as a largely uniform ecosystem with either a singular or a small number of fixed canopy ecophysiological properties³. This situation arises, in part, from a lack of understanding about how and why the functional properties of tropical forest canopies vary geographically⁴. Here, by combining field-collected data from more than 1,800 vegetation plots and tree traits with satellite remote-sensing, terrain, climate and soil data, we predict variation across 13 morphological, structural and chemical functional traits of trees, and use this to compute and map the functional diversity of tropical forests. Our findings reveal that the tropical Americas, Africa and Asia tend to occupy different portions of the total functional trait space available across tropical forests. Tropical American forests are predicted to have 40% greater functional richness than tropical African and Asian forests. Meanwhile, African forests have the highest functional divergence—32% and 7% higher than that of tropical American and Asian forests, respectively. An uncertainty analysis highlights priority regions for further data collection, which would refine and improve these maps. Our predictions represent a ground-based and remotely enabled global analysis of how and why the functional traits of tropical forest canopies vary across space.

The decline of semi-natural open ecosystems after land abandonment is a conservation issue in many industrialized countries. Large herbivores, such as horses (Equus ferus), are excellent candidates for rewilding activities, as they can contribute to reducing loss of open landscapes. However, their presence could affect the spatio-temporal distribution of sympatric species, especially if the reintroduction is unplanned and uncontrolled. La Calvana, central Italy, is a protected area with a mammalian community that has never been systematically monitored, and its grasslands, which are a high conservation priority, are disappearing. The area hosts a population of feral horses that originated about 40 years ago from a few released domestic individuals, and their unplanned presence could represent a unique rewilding opportunity for the restoration of the abandoned landscape. Yet nothing is known about their distribution or relationships with sympatric mammals. By deploying 40 cameras in May-July 2022, we systematically monitored the area to investigate spatio-temporal patterns of feral horses and their relationships with environmental, biotic, and anthropogenic factors. We detected 12 wild mammal species and estimated that horses were present in 40% of the study area. None of the environmental variables tested affected the occupancy of horses, although modeling of site-use intensity revealed that this species used upper-ridge grasslands more frequently. This suggests the area is suitable to support the population and that their presence at higher elevations can be an asset to preserving grasslands by limiting forest and shrub encroachment. Horses occupancy was not related to the relative abundance of wild ungulates, suggesting minimal competition for resources at present. However, the lower temporal overlap at sites with greater vegetation cover during the hottest hours indicated dominance of horses. Feral horses seem unaffected by human proximity, although they are occasionally subject to poaching. Lastly, the 7-year-long population census revealed a 12% annual growth rate that may lead to exceeding the carrying capacity of the ecosystem in the future. We recommend continued monitoring of this population and implementation of conservation and management programs.

Global conservation targets aim to expand protected areas and maintain species’ genetic diversity. Whether protected areas capture genetic diversity is unclear. We examined this question using a global sample of nuclear population‐level microsatellite data comprising genotypes from 2513 sites, 134,183 individuals, and 176 mammal and marine fish species. The genetic diversity and differentiation of samples inside and outside protected areas were similar, with some evidence for higher diversity in protected areas for small‐bodied mammals. Mammal populations, particularly large species, tended to be more genetically diverse when near multiple protected areas, regardless of whether samples were collected in or outside protected areas. Older marine protected areas tended to capture more genetically diverse fish populations. However, limited data availability in many regions hinders the systematic incorporation of genetic diversity into protected area design. Focusing on minimizing population decline and maintaining connectivity between protected areas remain essential proxies for maintaining genetic diversity.

Studying the interaction between macroevolutionary and ecological factors is critical for understanding the principles of diversity regulation and predicting the effects of human activities. Here, we use the geological chronology of the Hawaiian archipelago as a testbed to examine the interaction between island age and climatic factors (i.e., precipitation) on contemporary patterns of tree taxonomic diversity. To this end, we estimated patterns of tree species diversity from 375 forest plots spread across steep precipitation gradients and different substrate ages on a younger island (Hawai‘i; ~ 0.5 million years old), an intermediate-aged island (Maui Nui complex; ~ 2 million years old), and an older island (O‘ahu; ~ 3 million years old). We found a clear positive relationship between precipitation and diversity on the oldest island (O‘ahu), but no such relationship on the two younger islands (islands in the Maui Nui complex and Hawai‘i). We also found high species turnover between drier and wetter environments on the oldest island, which suggests ecological specialization on these habitat types, but not on the younger islands. However, when we included plots that were highly invaded by alien species, the effect varied and precipitation had a larger effect on diversity and turnover on the younger islands. This could be because the younger islands may be more vulnerable to invasions. Our results suggest that the response of diversity to climate variation differs substantially across the Hawaiian Islands, possibly because of differences in the age of the islands; however, biological invasions are degrading this signature. Highlights Local diversity responses to a steep precipitation gradient are stronger on older Hawaiian Islands, likely due to longer timescales for macroevolutionary processes. Species turnover to distinct precipitation conditions varies across islands of the Hawaiian archipelago, with older islands exhibiting greater precipitation-driven ecological specialization. Alien species alter local diversity responses to precipitation, particularly on the youngest island of the archipelago. The presence of alien species is modifying the pattern of species turnover across distinct precipitation conditions, with dry and mesic habitats on intermediate-age islands showing higher species turnover. Biological invasions are currently reshaping plant diversity patterns in the Hawaiian archipelago.