Research Papers

The global biogeography and environmental drivers of fairy circles

Fairy circles (FCs) are intriguing regular vegetation patterns that have only been described in Namibia and Australia so far. We conducted a global and systematic assessment of FC-like vegetation patterns and discovered hundreds of FC-like locations on three continents. We also characterized the range of environmental conditions that determine their presence, which is restricted to narrow and specific soil and climatic conditions. Areas showing FC-like vegetation patterns also had more stable productivity over time than surrounding areas having non-FC patterns. Our study provides insights into the ecology and biogeography of these fascinating vegetation patterns and the first atlas of their global distribution.

Species Distribution Models predict abundance and its temporal variation in a steppe bird population.

Habitat Suitability Index (HSI) derived from Species Distribution Model (SDM) has been used to infer or predict local demographic properties such as abundance for many species. Across species studied, HSI has either been presented as a poor predictor of abundance or as a predictor of potential rather than realized abundance. The main explanation of the lack of relationship between HSI and abundance is that the local abundance of a species varies in time due to various ecological processes that are not integrated into correlative SDM. To better understand the HSI-abundance relationship, in addition to the study of the association between HSI and mean abundance, we explored its variation over time. We used data from 10-years monitoring of a Houbara bustard (Chlamydotis undulata undulata) population in Morocco. From various occurrence data we modelled the HSI. From (independent) count data we calculated four local abundance indices: mean abundance, maximum abundance, the temporal trend of abundance and the coefficient of variation of abundance over the study period. We explored the relationship between HSI and abundance indices using linear, polynomial and quantile regressions. We found a triangular relationship between local abundance (mean and maximum) and HSI, indicating that the upper limit of mean and maximum abundance increased with HSI. Our results also indicate that sites with the highest HSI were associated with least variation in local abundance, the highest variation being observed at intermediate HSI. Our results provide new empirical evidence supporting the generalization of the triangular relationship between HSI and abundance. Overall, our results support the hypothesis that HSI obtained from SDMs can reflect the local abundance potentialities of a species and emphasize the importance of investigating this relationship using temporal variation in abundance.

Environmental and human factors drive the subtropical marine forests of Gongolaria abies-marina to extinction.

Large brown macroalgae are foundational threatened species in coastal ecosystems from the subtropical northeastern Atlantic, where they have exhibited a drastic decline in recent years. This study describes the potential habitat of Gongolaria abies-marina, its current distribution and conservation status, and the major drivers of population decline. The results show a strong reduction of more than 97% of G. abies-marina populations in the last thirty years and highlight the effects of drivers vary in terms of spatial heterogeneity. A decrease in the frequency of high waves and high human footprint are the principal factors accounting for the long-term decline in G. abies-marina populations. UV radiation and sea surface temperature have an important correlation only in certain locations. Both the methodology and the large amount of data analyzed in this study provide a valuable tool for the conservation and restoration of threatened macroalgae.

Human practices behind the aquatic and terrestrial ecological decoupling to climate change in the tropical Andes.

Anthropogenic climate change and landscape alteration are two of the most important threats to the terrestrial and aquatic ecosystems of the tropical Americas, thus jeopardizing water and soil resources for millions of people in the Andean nations. Understanding how aquatic ecosystems will respond to anthropogenic stressors and accelerated warming requires shifting from short-term and static to long-term, dynamic characterizations of human-terrestrial-aquatic relationships. Here we use sediment records from Lake Llaviucu, a tropical mountain Andean lake long accessed by Indigenous and post-European societies, and hypothesize that under natural historical conditions (i.e., low human pressure) vegetation and aquatic ecosystems' responses to change are coupled through indirect climate influences—that is, past climate-driven vegetation changes dictated limnological trajectories. We used a multi-proxy paleoecological approach including drivers of terrestrial vegetation change (pollen), soil erosion (Titanium), human activity (agropastoralism indicators), and aquatic responses (diatoms) to estimate assemblage-wide rates of change and model their synchronous and asynchronous (lagged) relationships using Generalized Additive Models. Assemblage-wide rate of change results showed that between ca. 3000 and 400 calibrated years before present (cal years BP) terrestrial vegetation, agropastoralism and diatoms fluctuated along their mean regimes of rate of change without consistent periods of synchronous rapid change. In contrast, positive lagged relationships (i.e., asynchrony) between climate-driven terrestrial pollen changes and diatom responses (i.e., asynchrony) were in operation until ca. 750 cal years BP. Thereafter, positive lagged relationships between agropastoralism and diatom rates of changes dictated the lake trajectory, reflecting the primary control of human practices over the aquatic ecosystem prior European occupation. We interpret that shifts in Indigenous practices (e.g., valley terracing) curtailed nutrient inputs into the lake decoupling the links between climate-driven vegetation changes and the aquatic community. Our results demonstrate how rates of change of anthropogenic and climatic influences can guide dynamic ecological baselines for managing water ecosystem services in the Andes.

Density-dependence of reproductive success in a Houbara bustard population.

Although density-dependent processes and their impacts on population dynamics are key issues in ecology and conservation biology, empirical evidence of density-dependence remains scarce for species or populations with low densities, scattered distributions, and especially for managed populations where densities may vary as a result of extrinsic factors (such as harvesting or releases). Here, we explore the presence of density-dependent processes in a reinforced population of North African Houbara bustard (Chlamydotis undulata undulata). We investigated the relationship between reproductive success and local density, and the possible variation of this relationship according to habitat suitability using three independent datasets. Based on eight years of nests monitoring (more than 7000 nests), we modeled the Daily Nest Survival Rate (DNSR) as a proxy of reproductive success. Our results indicate that DNSR was negatively impacted by local densities and that this relationship was approximately constant in space and time: (1) although DNSR strongly decreased over the breeding season, the negative relationship between DNSR and density remained constant over the breeding season; (2) this density-dependent relationship did not vary with the quality of the habitat associated with the nest location. Previous studies have shown that the demographic parameters and population dynamics of the reinforced North African Houbara bustard are strongly influenced by extrinsic environmental and management parameters. Our study further indicates the existence of density-dependent regulation in a low-density, managed population.

Fourteen years of continuous soil moisture records from plant and biocrust-dominated microsites.

Drylands cover ~41% of the terrestrial surface. In these water-limited ecosystems, soil moisture contributes to multiple hydrological processes and is a crucial determinant of the activity and performance of above- and belowground organisms and of the ecosystem processes that rely on them. Thus, an accurate characterisation of the temporal dynamics of soil moisture is critical to improve our understanding of how dryland ecosystems function and are responding to ongoing climate change. Furthermore, it may help improve climatic forecasts and drought monitoring. Here we present the MOISCRUST dataset, a long-term (2006–2020) soil moisture dataset at a sub-daily resolution from five different microsites (vascular plants and biocrusts) in a Mediterranean semiarid dryland located in Central Spain. MOISCRUST is a unique dataset for improving our understanding on how both vascular plants and biocrusts determine soil water dynamics in drylands, and thus to better assess their hydrological impacts and responses to ongoing climate change.

Biogeography of global drylands

Here we synthesize the biogeography of key organisms (vascular and non‐vascular vegetation and soil microorganisms), attributes (functional traits, spatial patterns, plant‐plant and plant‐soil interactions) and processes (productivity and land cover) across global drylands. We finish our review discussing major research gaps, which include: i) studying regular vegetation spatial patterns, ii) establishing large‐scale plant and biocrust field surveys assessing individual‐level trait measurements, iii) knowing whether plant‐plant and plant‐soil interactions impacts on biodiversity are predictable and iv) assessing how elevated CO2 modulates future aridity conditions and plant productivity.

Ecological Diversity within Rear-Edge: A Case Study from Mediterranean Quercus pyrenaica Willd.

Understanding the ecology of populations located in the rear edge of their distribution is key to assessing the response of the species to changing environmental conditions. Here, we focus on rear-edge populations of Quercus pyrenaica in Sierra Nevada (southern Iberian Peninsula) to analyze their ecological and floristic diversity. We perform multivariate analyses using high-resolution environmental information and forest inventories to determine how environmental variables differ among oak populations, and to identify population groups based on environmental and floristic composition.

Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution models

Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions.

Comment on “A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate”, author Coro

In this letter we present comments on the article “A global-scale ecological niche model to predict SARS-CoV-2 coronavirus” by Coro published in 2020.