Richness Patterns Research Articles

Similarity-Based Compression in Working Memory: Implications for Decay and Refreshing Models

AbstractThe ability to compress information is a fundamental cognitive function. It allows working memory (WM) to overcome its severely limited capacity. Recent evidence suggests that the similarity between items can be used to compress information, leading to a rich pattern of behavioral results. This work presents a series of simulations showing that this rich pattern of WM performance is captured using the principles of TBRS*, a decay and refreshing architecture. By assuming that similar items are compressed, the architecture can explain the beneficial effect of similarity on the items themselves. The architecture also explains the fact that when similar items are mixed with dissimilar items, this provides a proactive—but no retroactive—benefit on WM performance. In addition, the model captures fine-grained patterns of transposition errors recently reported. Several analyses are reported showing the robustness of the model’s predictions. We reached the conclusion that decay and refreshing theories provide a plausible explanation for compression effects in WM. These conclusions are discussed in light of recent experimental results. The importance of computational modeling for testing theories is emphasized.

The impact of a massive wildfire event on avian species richness and abundance in an arid African savanna ecosystem

Fire shapes ecological diversity, but its influence on bird communities is poorly understood. Fire is often considered crucial to management of savanna systems. Here, we assess effects of fire on bird species richness and density after a wildfire in September 2021, in an arid South African savanna where fire is infrequent. Point counts initially surveyed in 2004 now occurred in burnt and unburnt habitats, and were revisited five times across seasons from December 2021 to January 2023. This enabled identification of interaction effects of change over time and with fire from linear models using site and survey as predictor variables in a Before-After Control-Impact (BACI) framework. Patterns of bird species richness were explored using subsets of repeated points and those of summer to autumn surveys. To explore fire impacts on bird density, we used ‘Distance Sampling’ using additional surveys. Although total species richness during counts did not differ between survey periods and burnt and unburnt areas, densities of all birds were ca. 50% higher in unburnt veld relative to burnt, with this difference driven mostly by granivores. Three of 24 common species were significantly negatively influenced by fire. Fires may profoundly affect this arid zone bird community, a finding that would be overlooked if only species richness had been considered, and densities not accounted for. Fire, although potentially positive for biodiversity in mesic savannas, may negatively impact multiple taxa in arid savannas, which have a very different evolutionary and ecological history to that of more mesic savannas. These differences must be considered in management strategies.

Eight years of weekly eDNA monitoring in the North‐Western Pacific

AbstractDetection of marine organisms based on environmental DNA (eDNA) has expanded the knowledge of species richness, distribution, and appearance patterns. We used eDNA data from a single location sampled weekly over 8 years to detect as many species as possible, and investigate their appearance patterns and associations with biotic and abiotic factors. We detected >2500 operational taxonomic units (OTUs) associated with unique species from nine supergroups. The dominating currents that differ significantly in temperature, salinity, and nutrient concentrations structured the richness of the OTUs. We further detected sporadic and seasonal or continuous presence among the OTUs connected with a single species. This study shows that the long‐term eDNA‐based monitoring approach provides comprehensive knowledge of the species present as well as their associations with biotic and abiotic factors.

Inter-annual and habitat variation of the mayfly assemblage in grassland and pine afforested headwater streams (Córdoba, Central Argentina)

Mayflies are widely used as indicators of stream conditions since their presence and abundance are closely associated with changes in environmental characteristics. We aim to analyze the richness and abundance patterns of mayflies in headwater streams of the Ctalamochita river basin in central Argentina, where some sub-basins have been fully afforested with exotic pines. We sampled 3 grassland streams and 3 pine afforested streams in 2008, 2012, 2014, and 2015. We took Surber samples in riffles and in 2012 we also sampled 2 additional habitats: pools and macrophyte patches. We found ten mayfly genera. Americabaetis and Leptohyphes were the most frequent and abundant and Cloeodes and Tricorythodes were rare, with a relative frequency < 3%. Richness and abundance were higher in grassland streams (ANOVA, p < 0.05), but the abundance pattern of different mayfly genera varied. In 2012 mayfly abundance was higher in pools and macrophytes in the afforested streams (ANOVA, p < 0.05), but it was similar among habitats in grassland streams. Different species dominated different habitats and stream types, and some shared the same habitat. In riffles and pools, the dominant genera was similar for afforested and grassland streams, while in macrophyte the dominance varied according to the riparian vegetation. Distribution patterns are influenced by microhabitat characteristics, which may be conditioned by changes in riparian land uses.

On the analysis of a heterogeneous coupled network of memristive Chialvo neurons

We perform a numerical study on the application of electromagnetic flux on a heterogeneous network of Chialvo neurons represented by a ring-star topology. Heterogeneities are realized by introducing additive noise modulations on both the central–peripheral and the peripheral–peripheral coupling links in the topology not only varying in space but also in time. The variation in time is understood by two coupling probabilities, one for the central–peripheral connections and the other for the peripheral–peripheral connections, respectively, that update the network topology with each iteration in time. We have further reported various rich spatiotemporal patterns like two-cluster states, chimera states, coherent, and asynchronized states that arise throughout the network dynamics. We have also investigated the appearance of a special kind of asynchronization behavior called “solitary nodes” that have a wide range of applications pertaining to real-world nervous systems. In order to characterize the behavior of the nodes under the influence of these heterogeneities, we have studied two different metrics called the “cross-correlation coefficient” and the “synchronization error.” Additionally, to capture the statistical property of the network, for example, how complex the system behaves, we have also studied a measure called “sample entropy.” Various two-dimensional color-coded plots are presented in the study to exhibit how these metrics/measures behave with the variation of parameters.

Modeling tree species richness patterns and their environmental drivers across Hyrcanian mountain forests

Perception of the way tree species richness distribution varies along the elevational gradient is necessary for applying conservation strategies in favor of biodiversity hotspots. Hyrcanian Mountain Forests of Iran are among the most biodiverse forests in the world and are inscribed in the United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage List. Here, the Bayesian Additive Regression Tree (BART) and Stack-Species Distribution Model (S-SDMs) were used to understand tree diversity patterns of the Hyrcanian Mountain Forests. For all the tree species, we implemented the BART model with nine environmental variables, including six bioclimatic and three topographic variables, to create a habitat suitability map. We used the area under the curve (AUC), True skill statistic (TSS), type I error, and type II error to evaluate the BART model for each tree species. All models were then stacked to estimate tree species richness. Important variables affecting the distribution of tree species in northern Iran were different for each species but, in general, topographic variables such as slope and altitude played a very important role. BART model mapped all the 29 tree species distribution effectively with AUC 0.83–0.98, TSS 0.51–0.92; and low type I and type II errors. A very important point was the high ability of this model to predict species with low presence data. The achievement of this study was identifying the biodiversity hotspots based on the tree distribution pattern along the elevational gradient. We mapped regional richness at 1 km spatial resolution and it was the first effort to predict macroecological patterns in the Hyrcanian forests of Iran. The hotspots were mainly scattered from sea level and areas with 1000 m elevation, which were close to residential areas. In conclusion, these results help forest manager to plan new conservation strategies based on tree richness hotspots.

Consistent imprints of elevation, soil temperature and moisture on plant and arthropod communities across two subarctic landscapes

Abstract Factors shaping arthropod and plant community structure at fine spatial scales are poorly understood. This includes microclimate, which likely plays a large role in shaping local community patterns, especially in heterogeneous landscapes characterised by high microclimatic variability in space and in time. We explored differences in local microclimatic conditions and regional species pools in two subarctic regions: Kilpisjärvi in north‐west Finland and Varanger in north‐east Norway. We then investigated the relationship between fine‐scale climatic variation and local community characteristics (species richness and abundance) among plants and arthropods, differentiating the latter into two groups: flying and ground‐dwelling arthropods collected by Malaise and pitfall traps, respectively. Arthropod taxa were identified through DNA metabarcoding. Finally, we examined if plant richness can be used to predict patterns in arthropod communities. Variation in soil temperature, moisture and snow depth proved similar between regions, despite differences in absolute elevation. For each group of organisms, we found that about half of the species were shared between Kilpisjärvi and Varanger, with a quarter unique to each region. Plants and arthropods responded largely to the same drivers. The richness and abundance of both groups decreased as elevation increased and were positively correlated with higher soil moisture and temperature values. Plant species richness was a poor predictor of local arthropod richness, in particular for ground‐dwelling arthropods. Our results reveal how microclimatic variation within each region carves pronounced, yet consistent patterns in local community richness and abundance out of a joint species pool.

A dark side of conservation biology: Protected areas fail in representing subterranean biodiversity

Abstract Biodiversity conservation is a central imperative of the 21st century. Subterranean ecosystems deliver critical nature's contributions to people and harbour a broad diversity of poorly understood specialised organisms. However, the subterranean biome is still largely overlooked in global biodiversity targets. We assessed how well subterranean biodiversity is represented in protected areas (Natura 2000 and Emerald networks) in two global hotspots of subterranean biodiversity (the Pyrenees and the Alps). For this, we used two comprehensive databases of terrestrial subterranean taxa, that is, leiodids (beetles) from the Pyrenees and spiders from the Alps, and identified priority areas in each region using both species richness and geographic rarity patterns. Our results show the incapacity of surface‐protected area networks to represent subterranean biodiversity, as more than 70% and 90% of the identified priority areas (and 40% and 22% of the species) are not effectively covered by protected areas in the Pyrenees and the Alps, respectively. These findings call for developing an urgent plan for subterranean biodiversity conservation within the European Biodiversity Strategy for 2030.

Are the same factors determining biodiversity in cities across different regions? Comparing drivers of urban bird richness patterns in Southern Asia vs. Western Europe

AbstractAccording to general urban ecological understanding, bird species richness declines in highly urban areas due to the increasing extent of built-up areas, and decreasing proportions of green areas. However, this hypothesis is mainly based on studies conducted in cities located in the Global North and rarely in the Global South. We aimed to assess whether existing conceptual understandings of drivers of bird species richness patterns within cities are similar across different regions, specifically Southern Asia (in the Global South) vs. Western Europe (in the Global North). Using avian occurrence data drawn from GBIF (gbif.org), we estimated resident bird species richness in 943 selected grid cells (each cell corresponding to approximately 1 km2 area) distributed in 24 cities, 13 in Southern Asia and 11 in Western Europe. We applied generalised linear mixed models to relate resident bird richness with different explanatory variables of gradients of urbanisation, habitat and climatic factors using the selected grid cells as sampling units. Our results showed that bird richness declined with increasing human built-up and settlement extent (imperviousness) in both regions, but this relationship was stronger in Southern Asia compared to Western Europe. Bird richness also sharply declined in urban areas located far from inland waterbodies, but only in Southern Asia. Our findings suggest that high imperviousness drives bird richness decline, but this process appears to be more pronounced in regions where rapid urbanisation is ongoing. Urban planning integrating green spaces throughout cities is crucial in Southern Asia, as it is in Western Europe, to benefit both people and biodiversity.

Precipitation gradients drive high tree species turnover in the woodlands of eastern and southern Africa

Savannas cover one‐fifth of the Earth's surface, harbour substantial biodiversity, and provide a broad range of ecosystem services to hundreds of millions of people. The community composition of trees in tropical moist forests varies with climate, but whether the same processes structure communities in disturbance‐driven savannas remains relatively unknown. We investigate how biodiversity is structured over large environmental and disturbance gradients in woodlands of eastern and southern Africa. We use tree inventory data from the Socio‐Ecological Observatory for Studying African Woodlands (SEOSAW) network, covering 755 ha in a total of 6780 plots across nine countries of eastern and southern Africa, to investigate how alpha, beta, and phylogenetic diversity varies across environmental and disturbance gradients. We find strong climate‐richness patterns, with precipitation playing a primary role in determining patterns of tree richness and high turnover across these savannas. Savannas with greater rainfall contain more tree species, suggesting that low water availability places distributional limits on species, creating the observed climate‐richness patterns. Both fire and herbivory have minimal effects on tree diversity, despite their role in determining savanna distribution and structure. High turnover of tree species, genera, and families is similar to turnover in seasonally dry tropical forests of the Americas, suggesting this is a feature of semiarid tree floras. The greater richness and phylogenetic diversity of wetter plots shows that broad‐scale ecological patterns apply to disturbance‐driven savanna systems. High taxonomic turnover suggests that savannas from across the regional rainfall gradient should be protected if we are to maximise the conservation of unique tree communities.

‘Modi’-fied Beliefs: Assessing Cognitive Dissonance in Populist Voting Behaviour in India

Voting is a multifaceted decision that involves choosing a leader/party in spite of not agreeing with all that they represent. How then do voters deal with the aspects that they do not like? Using a cognitive dissonance approach, this research looks to study how educated secular voters who vote for the Hindu nationalist Narendra Modi in India reduce the dissonance arising out of the communal repercussions of their vote. I find that voters both adjust their conceptions of secularism and their perceptions of communalism to justify their vote and reduce dissonance. This can have significant repercussions on democracy as voters who adjust their preferences become citizens who hold modified standards of accountability. This paper presents novel insights into voter decision-making, especially on how they may navigate between aspects they like and those they may not when taking voting decisions. It also provides rich patterns of political psychology for other researchers to explore.

Beyond the Hydro-Regime: Differential Regulation of Plant Functional Groups in Seasonal Ponds

Plant community assembly can be influenced by many environmental factors. In seasonal wetlands, most studies focus on the considerable variation that occurs from the hydro-regime (patterns of inundation and desiccation). Other factors and their interactions also influence plants but are seldom studied, including nutrient and thatch levels. Furthermore, the responses of aquatic and terrestrial functional groups can provide important insights into patterns of cover and richness. The aim of this study was to evaluate how algae and plant functional groups (aquatic and terrestrial plants) respond to variation in hydro-regime (stable and unstable), nutrient addition (none and added), and thatch (none, native plants, and exotic plants) addition. We measured algal cover, total species richness, and the cover of the functional groups over two years. Algal cover increased with unstable hydroperiods and the addition of nutrients. Algae were also negatively associated with aquatic plant cover and positively associated with terrestrial plant cover. Aquatic plant cover increased with a stable hydro-regime and decreased with increased thatch. Terrestrial plant cover increased with an unstable hydro-regime and decreased with thatch addition. Thatch accumulation and excess nutrients can be linked to human activities, which directly and indirectly alter plant community composition. The interactions of these factors with the hydro-regime should be considered when evaluating a plant community’s response to changing environmental conditions. Overall, these results are necessary for the conservation and management of essential wetland functions and services.

Patterns of species richness and beta diversity of vascular plants along elevation gradient in Brazilian páramo

Patterns of species richness and beta diversity of vascular plants along elevation gradient in Brazilian páramo

Aggregated Contextual Transformations for High-Resolution Image Inpainting.

Image inpainting that completes large free-form missing regions in images is a promising yet challenging task. State-of-the-art approaches have achieved significant progress by taking advantage of generative adversarial networks (GAN). However, these approaches can suffer from generating distorted structures and blurry textures in high-resolution images (e.g., 512×512). The challenges mainly drive from (1) image content reasoning from distant contexts, and (2) fine-grained texture synthesis for a large missing region. To overcome these two challenges, we propose an enhanced GAN-based model, named Aggregated COntextual-Transformation GAN (AOT-GAN), for high-resolution image inpainting. Specifically, to enhance context reasoning, we construct the generator of AOT-GAN by stacking multiple layers of a proposed AOT block. The AOT blocks aggregate contextual transformations from various receptive fields, allowing to capture both informative distant image contexts and rich patterns of interest for context reasoning. For improving texture synthesis, we enhance the discriminator of AOT-GAN by training it with a tailored mask-prediction task. Such a training objective forces the discriminator to distinguish the detailed appearances of real and synthesized patches, and in turn facilitates the generator to synthesize clear textures. Extensive comparisons on Places2, the most challenging benchmark with 1.8 million high-resolution images of 365 complex scenes, show that our model outperforms the state-of-the-art. A user study including more than 30 subjects further validates the superiority of AOT-GAN. We further evaluate the proposed AOT-GAN in practical applications, e.g., logo removal, face editing, and object removal. Results show that our model achieves promising completions in the real world. We release codes and models in https://github.com/researchmm/AOT-GAN-for-Inpainting.

Biodiversity in cities: the impact of biodiversity data across spatial scales on diversity estimates

Abstract The assessment and monitoring of biodiversity in urban areas has been shown to have enormous potential to inform integrative urban planning in cities. In this context, digital biodiversity repositories such as the Global Biodiversity Information Facility (GBIF) has been promoted for its central role in gathering and harmonizing biodiversity data worldwide, thereby facilitating these assessments and monitoring efforts. While GBIF data has been investigated for its potential at a large scale and in natural ecosystems, the question remains as to what extent, and in which context, is GBIF data applicable to urban biodiversity assessment and monitoring? In this study, we assessed the spatial patterns of biodiversity, by exploring species richness patterns in relation to land use types for three taxonomic groups (birds, mammals and arthropods) in three cities in The Netherlands (Rotterdam, Amsterdam and Groningen) at multiple spatial scales. We found significant variation in the effect of land uses on the species richness patterns, in terms of taxonomic group, spatial configuration and land cover type, and across spatial scales. Our study demonstrates the potential of GBIF data while highlighting the importance of the careful selection of one or multiple spatial scales, especially in relation to the taxonomic group characteristics and ecology and the spatial configuration of the cities studied.

Phytodiversity of trees and shrubs in Vindhyabasni Sacred grove, Girwan, Banda, Uttar Pradesh

Sacred groves are the storehouse of medicinal plants, trees, shrubs, and important useful plants for the locals. The present study was conducted to study the diversity of woody vegetation across the topography (pediment, slope and hilltop.) in Vindhyavasini sacred grove, Girwan Banda Uttar Pradesh. The vegetation data were quantitatively analyzed for community characteristics such as species richness, dominance, diversity and distribution pattern by using the quadrat method. A total of 68 sample plots of 32.5×32.5m² for trees and 136 sub-plots of 5×5m² for shrubs were laid down. A total of 32 plant species (18 trees and 14 shrubs) belonging to 20 families were recorded and identified during the study. The highest species diversity and species richness for both trees (0.93, 14) and shrubs (1.77, 11) were recorded in pediment and species richness of the family Combretaceae was the highest (79.56) followed by Lamiaceae (36.78) and Apocynaceae (29.26). Terminalia pendula (IVI -187.80) and Carissa spinarum (IVI-86.89) were the most dominant tree and shrub species respectively. This sacred grove may work as a vital pool for the preservation of endemic and local plant species.

Elevational Patterns of Tree Species Richness and Forest Biomass on Two Subtropical Mountains in China

Increasing evidence shows that both abiotic and biotic factors affect species richness and stand biomass in forests, yet the relative and interactive impacts of these factors remain debated in different forest ecosystems. We sampled 55 forest plots (600 m2 per plot) on two subtropical mountains with distinct diversity levels in China to explore the elevational patterns of tree species richness and stand biomass and examined how they were affected by climate, stand structure, and dominance of mycorrhizal types. The tree species richness of both mountains decreased with elevation, while the stand biomass exhibited unimodal or no apparent trends. On both mountains, the tree species richness was strongly shaped by climatic factors, especially the mean annual temperature, whereas the stand biomass was mainly affected by the stand structure. Specifically, on the mountain with higher species richness, both the tree height variation and maximum tree size were strongly correlated with the stand biomass. Meanwhile, on the species-poor mountain with higher elevations, only the maximum tree size correlated with the stand biomass. The dominance of ectomycorrhizal trees also had positive effects on the stand biomass of both mountains. These results suggest that climate, stand structure, and mycorrhizal dominance may jointly drive the decoupling between tree species richness and stand biomass, which should be given more attention in further research and forest management to achieve the climate change mitigation goals.

Marine and estuarine crustacean diversity and assemblage structure in eastern Brazil three years after the Fundão mining dam failure

The present study investigates the spatial and temporal patterns of species richness and assemblage structure of decapod and stomatopod crustaceans in the area impacted by the Fundão dam collapse (Rio Doce basin, Brazil) in November 2015. From October 2018 to September 2019, monthly bottom-trawl samples were collected in estuarine and marine waters of the Rio Doce (directly impacted area) and three other localities. Forty-one crustacean species were recorded, with the overall community structure dominated by six species. The seabob shrimp Xiphopenaeus kroyeri was the most common species, with the coastal marine area near to the Rio Doce mouth being an important fishery ground. Species composition changed significantly among localities, habitats, and seasons, with marine and estuarine assemblages shaped by freshwater discharge. Distribution patterns of species were significantly influenced by the environmental variables potentially altered by tailings, such as turbidity and pH. Species richness and abundance differed among localities; as expected, they were generally higher on the inner shelf than in estuaries and higher during the dry season than the rainy season. Due to the lack of information before the disaster, the low number of species found in the Rio Doce estuary cannot be solely attributed to the disaster but also its freshwater condition. This is the first broad study of the estuarine and coastal marine decapod and stomatopod crustaceans in the region, and we suggest the need for long-term monitoring considering that the effects of this disaster are not easy to detect and probably need multi-year study to be evidenced.

Anticipating where are unknown aquatic insects in Europe to improve biodiversity conservation

AbstractAimUnderstanding biodiversity patterns is crucial for prioritizing future conservation efforts and reducing the current rates of biodiversity loss. However, a large proportion of species remain undescribed (i.e. unknown biodiversity), hindering our ability to conduct this task. This phenomenon, known as the ‘Linnean shortfall’, is especially relevant in highly diverse, yet endangered, taxonomic groups, such as insects. Here we explore the distributions of recently described freshwater insect species in Europe to (1) infer the potential location of unknown biodiversity hotspots and (2) determine the variables that can anticipate the distribution of unknown biodiversity.LocationThe European continent, including western Russia, Cyprus and Turkey.MethodsGeoreferenced information of all sites where new aquatic insect species were described across Europe from 2000 to 2020 was compiled. In order to understand the observed spatial patterns in richness of recently described species, spatial units were defined (level 6 of HydroBASINS) and associated with a combination of a set of socioeconomic, environmental and sampling effort descriptors. A zero‐inflated Poisson regression approach was used to model the richness of newly described species within each spatial unit.ResultsNine hundred and sixty‐six recently described species were found: 398 Diptera, 362 Trichoptera, 105 Coleoptera, 66 Plecoptera, 28 Ephemeroptera, 3 Neuroptera, 2 Lepidoptera and 2 Odonata. The Mediterranean Basin was the region with the highest number of recently described species (74%). The richness of recently described species per spatial unit across Europe was highest at mid‐elevation areas (between 400 and 1000 m), latitudes between 40 and 50° and in areas with yearly average precipitation levels of 500–1000 mm, a medium intensity of sampling effort and low population density. The percentage of protected areas in each study unit was not significantly related to the richness of recently described species. In fact, 70% of the species were found outside protected areas.Main conclusionsThe results highlight the urgent need to concentrate conservation efforts in freshwater ecosystems located at mid‐altitude areas and out of protected areas across the Mediterranean Basin. The highest number of newly described species in those areas indicates that further monitoring efforts are required to ensure the aquatic biodiversity is adequately known and managed within a context of growing human impacts in freshwater ecosystems.

Learning Dynamic Temporal Relations with Continuous Graph for Multivariate Time Series Forecasting (Student Abstract)

The recent advance in graph neural networks (GNNs) has inspired a few studies to leverage the dependencies of variables for time series prediction. Despite the promising results, existing GNN-based models cannot capture the global dynamic relations between variables owing to the inherent limitation of their graph learning module. Besides, multi-scale temporal information is usually ignored or simply concatenated in prior methods, resulting in inaccurate predictions. To overcome these limitations, we present CGMF, a Continuous Graph learning method for Multivariate time series Forecasting (CGMF). Our CGMF consists of a continuous graph module incorporating differential equations to capture the long-range intra- and inter-relations of the temporal embedding sequence. We also introduce a controlled differential equation-based fusion mechanism that efficiently exploits multi-scale representations to form continuous evolutional dynamics and learn rich relations and patterns shared across different scales. Comprehensive experiments demonstrate the effectiveness of our method for a variety of datasets.