Biotic Predictors Research Articles

Growing-Season Precipitation Is a Key Driver of Plant Leaf Area to Sapwood Area Ratio at the Global Scale.

Leaf area to sapwood area ratio (AL/AS) influences carbon sequestration, community composition, and ecosystem functioning in terrestrial vegetation and is closely related to leaf economics and hydraulics. However, critical predictors of AL/AS are not well understood. We compiled an AL/AS data set with 1612 species-site combinations (1137 species from 285 sites worldwide) from our field experiments and published literature. We found the global mean AL/AS to be 0.63 m2 cm-2, with its variation largely driven by growing-season precipitation (Pgs), which accounted for 18% of the variation in AL/AS. Species in tropical rainforests exhibited the highest AL/AS (0.82 m2 cm-2), whereas desert species showed the lowest AL/AS (0.16 m2 cm-2). Soil factors such as soil nitrogen and soil organic carbon exhibited positive effects on AL/AS, whereas soil pH was negatively correlated with AL/AS. Tree density accounted for 7% of the variation in AL/AS. All biotic and abiotic predictors collectively explained up to 45% of the variation in AL/AS. Additionally, AL/AS was positively correlated to the net primary productivity (NPP) of the ecosystem. Our study provides insights into the driving factors of AL/AS at the global scale and highlights the importance of AL/AS in ecosystem productivity. Given that Pgs is the most critical driver of AL/AS, alterations in global precipitation belts, particularly seasonal precipitation, may induce changes in plant leaf area on the branches.

Evaluating Abiotic and Biotic Predictors of Coastal Giant Salamander (Dicamptodon tenebrosus) Populations in Fish-Bearing Headwaters of the Oregon Coast Range

Evaluating Abiotic and Biotic Predictors of Coastal Giant Salamander (Dicamptodon tenebrosus) Populations in Fish-Bearing Headwaters of the Oregon Coast Range

Phylogenetic structure of moth communities (Geometridae, Lepidoptera) along a complete rainforest elevational gradient in Papua New Guinea.

We use community phylogenetics to elucidate the community assembly mechanisms for Geometridae moths (Lepidoptera) collected along a complete rainforest elevational gradient (200-3700 m a.s.l) on Mount Wilhelm in Papua New Guinea. A constrained phylogeny based on COI barcodes for 604 species was used to analyse 1390 species x elevation occurrences at eight elevational sites separated by 500 m elevation increments. We obtained Nearest Relatedness Index (NRI), Nearest Taxon Index (NTI) and Standardised Effect Size of Faith's Phylogenetic Diversity (SES.PD) and regressed these on temperature, plant species richness and predator abundance as key abiotic and biotic predictors. We also quantified beta diversity in the moth communities between elevations using the Phylogenetic Sorensen index. Overall, geometrid communities exhibited phylogenetic clustering, suggesting environmental filters, particularly at higher elevations at and above 2200 m a.s.l and no evidence of overdispersion. NRI, NTI and SES.PD showed no consistent trends with elevation or the studied biotic and abiotic variables. Change in community structure was driven by turnover of phylogenetic beta-diversity, except for the highest 2700-3200 m elevations, which were characterised by nested subsets of lower elevation communities. Overall, the elevational signal of geometrid phylogeny was weak-moderate. Additional insect community phylogeny studies are needed to understand this pattern.

Alterations of throughfall and stemflow chemistry by vegetation in China: Insights from a machine learning based meta-analysis

In vegetated ecosystems, the chemical composition of rainwater is strongly altered after gross rainfall (GR) partitioning into throughfall (TF) and stemflow (SF) before entering the under-canopy soil. However, quantitative syntheses of the chemical alteration are lacking, rendering an insufficient comprehension of its overarching patterns and magnitudes. Here, we furnish a comprehensive dataset related to the chemistry of rainfall partitioning by vegetation in China compiled from 131 peer-reviewed papers published between 1988 and 2023. A meta-analysis was conducted to assess the chemical alterations as GR partitions into TF and SF. We discerned a marked acidification effect in SF but not in TF, with the pH being significantly lower in SF than in GR. Additionally, we observed a persistent concentration-based chemical enrichment of total nitrogen (TN) and total phosphorous (TP) as well as 12 commonly reported nutrient-ions (except for Cu2+) in both TF and SF, with SF generally exhibiting a greater degree of chemical enrichment than TF. Using a machine learning method (boosted regression trees), we identified the key drivers from twelve biotic and abiotic predictor variables on the effect sizes of pH, TN, TP, and each nutrient-ion, and further elucidated the prevalent non-linear partial effects of these key drivers on the magnitude of chemical alterations. For instance, three predictor variables were identified as key drivers for the effect sizes of TN for SF, with a descending relative influence of diameter at breast height (56.9 %; positive effect), Human Footprint (16.5 %; positive), and bark texture (14.0 %; stands with mixed bark textures exerting a greater impact). Our study has significant implications for an accurate estimation of nutrient budgets in vegetated ecosystems throughout China, contributes to a deeper understanding of the intricate ways in which biotic and abiotic predictors influence chemical alterations in rainfall partitioning, and aids in evaluating forest health and developing sensible forest management strategies.

Using mobile acoustic monitoring and false‐positive N‐mixture models to estimate bat abundance and population trends

AbstractEstimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false‐positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white‐nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats (Perimyotis subflavus) and little brown bats (Myotis lucifugus), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population‐wide declines probably have not occurred in big brown bats (Eptesicus fuscus), a species known to be less affected by WNS. This study provides the first abundance‐based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false‐negative and false‐positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.

Local hydrography and interactions with benthic megafauna explain fish assemblage structure on the shallow inner shelf of Paraná, southern Brazil

An important objective of fish ecology is to understand pure and shared effects of abiotic, biotic, and spatial predictors of fish assemblages’ structure on continental shelves. This study aims to assess the structure of the bottom-associated fish assemblage on the shallow inner continental shelf of Paraná, southern Brazil, in response to physicochemical, biological, and spatial factors. We hypothesize that this structure is associated with the benthic megafauna, particularly with the distribution of the Brazilian seabob shrimp Xiphopenaeus kroyeri, an abundant fishing resource on the shallow coast of Paraná. We used uni and multivariate analyses to examine the distribution of abundance, richness, and composition of ichthyofauna in the region. Using variance partitioning analysis, we determined the pure and shared effect of spatial (latitude and longitude), physicochemical, and spatiotemporal distribution patterns of crustaceans, echinoderms, and mollusks as potential predictors of the structure of the fish assemblage. The data were obtained monthly from a one-year bottom trawl survey, comprising five transects and four depths (six, nine, 12 and 15 m) in a 60 km long section of the inner continental shelf, under the influence of estuaries. We captured 57578 demersal fish, comprising 37 families and 88 species. The average abundance of fish per haul was 387 individuals at a depth of 6 m, decreasing to 114 individuals per haul at 15 m. Average richness also decreased along the bathymetric profile, from 15 to 10 species per haul. Fish abundance and richness responded positively to a seasonal estuarine discharge pattern that was stronger in summer and autumn. High biomass of juvenile seabob shrimp in shallower regions also explained the higher abundances and richness. The results also suggest the existence of two fish assemblages in the region, associated with the distribution of the megafauna and segregated by depth, water surface transparency, and sediment particle size composition. Despite significant seasonal variations, controlled by precipitation and bottom water temperature, the structure of fish assemblages responded to the pure and shared effects of the three predictive components in all seasons. In particular, local coastal hydrography and the prey-predator relationship between fish and seabob shrimp were ecologically relevant processes for structuring fish assemblages at the inner continental shelf of Paraná. This study highlights the complexity of ecological interactions within demersal fish assemblages and the importance of considering biotic and abiotic factors in understanding fish distribution patterns at the spatial scale of inner continental shelves that are influenced by estuaries.

The geographic distribution, and the biotic and abiotic predictors of select zoonotic pathogen detections in Canadian polar bears

Increasing Arctic temperatures are facilitating the northward expansion of more southerly hosts, vectors, and pathogens, exposing naïve populations to pathogens not typical at northern latitudes. To understand such rapidly changing host–pathogen dynamics, we need sensitive and robust surveillance tools. Here, we use a novel multiplexed magnetic-capture and droplet digital PCR (ddPCR) tool to assess a sentinel Arctic species, the polar bear (Ursus maritimus; n = 68), for the presence of five zoonotic pathogens (Erysipelothrix rhusiopathiae, Francisella tularensis, Mycobacterium tuberculosis complex, Toxoplasma gondii and Trichinella spp.), and observe associations between pathogen presence and biotic and abiotic predictors. We made two novel detections: the first detection of a Mycobacterium tuberculosis complex member in Arctic wildlife and the first of E. rhusiopathiae in a polar bear. We found a prevalence of 37% for E. rhusiopathiae, 16% for F. tularensis, 29% for Mycobacterium tuberculosis complex, 18% for T. gondii, and 75% for Trichinella spp. We also identify associations with bear age (Trichinella spp.), harvest season (F. tularensis and MTBC), and human settlements (E. rhusiopathiae, F. tularensis, MTBC, and Trichinella spp.). We demonstrate that monitoring a sentinel species, the polar bear, could be a powerful tool in disease surveillance and highlight the need to better characterize pathogen distributions and diversity in the Arctic.

A Method Based on Artificial Intelligence That Predicts Arabica Coffee Yield by Analyzing Abiotic Factors and the Prevalence of Coffee Leaf Rust

Coffee is a perennial crop that harbors infections throughout the plant, which may worsen illness under favorable circumstances. Coffee leaf rust, a widespread coffee disease and Arabica is susceptible to leaf rust. Disease incidence and severity depend on abiotic variables. Cloudy and constant South-West monsoon weather (June–September) promotes coffee leaf rust growth. In this five-model study, an Extra Tree and Gradient Boosting regression model predicted coffee crop output in Chikamagaluru, Karnataka, with the least error utilizing biotic and abiotic factors. We investigated additional tree, gradient boosting, RF, Decision Tree, and KNN models using biotic and abiotic predictors. Used the independent testing dataset's MSE, MAE, RMSE Root mean square errors, and R-squared errors to compare model performance. The extra tree (R²=0.98 kg/ha ˉ¹ and RMSE = 7.96 kg/ha ˉ¹) and gradient boosting (R²=0.96 kg/ha ˉ¹ and RMSE = 10.96 kg/ha ˉ¹) regression models used Group 1 and 2 characteristics as predictor variables and different parameter fine tuning functions to estimate coffee yield most accurately. Compared to the less precise probabilistic models utilized in this work, such as Random Forest, decision tree, and KNN models, shown in the results section. The optimum weather parameter for coffee production forecasts and biotic-CLR incidence data outperformed random forest, decision tree, and K-Nearest Neighbor models.

The mid-domain effect in flowering phenology

The timing of flowering is an important driver of species distribution and community assembly patterns. However, we still have much to learn about the factors that shape flowering diversity (i.e., number of species flowering per period) in plant communities. One potential explanation of flowering diversity is the mid-domain effect, which states that geometric constraints on species ranges within a bounded domain (space or time) will yield a mid-domain peak in diversity regardless of ecological factors. Here, we determine whether the mid-domain effect explains peak flowering time (i.e., when most species of communities are flowering) across China. We used phenological data of 16,267 herbaceous and woody species from the provincial Flora in China and species distribution data from the Chinese Vascular Plant Distribution Database to determine relationships between the observed number of species flowering and the number of species flowering as predicted by the mid-domain effect model, as well as between three climatic variables (mean minimum monthly temperature, mean monthly precipitation, and mean monthly sunshine duration). We found that the mid-domain effect explained a significant proportion of the temporal variation in flowering diversity across all species in China. Further, the mid-domain effect explained a greater proportion of variance in flowering diversity at higher latitudes than at lower latitudes. The patterns of flowering diversity for both herbaceous and woody species were related to both the mid-domain effect and environmental variables. Our findings indicate that including geometric constraints in conjunction with abiotic and biotic predictors will improve predictions of flowering diversity patterns.

Explaining variation in stream fish productivity with biotic and abiotic variables across wadeable rivers in eastern North America

Abstract Biomass production is a key ecosystem process that provides insight into ecological processes such as growth, reproduction, mortality, and energy distribution. Previous studies have considered various fish response measures such as species richness, abundance, and/or biomass as response variables for river ecosystems or the productivity of particular species. However, few studies have investigated how total fish productivity of riverine systems is affected by environmental variables. Here, we identified important abiotic and biotic predictors of fish productivity in wadeable, temperate riverine systems. We investigated the relationships between total stream fish productivity and multiple abiotic and biotic variables in wadeable stream reaches across Ontario, Canada. Variance partitioning was used to evaluate the relative importance of the biotic, landscape, climatic, and geologic variables on total stream fish productivity. A modified bootstrap approach was used for the model‐selection process and to parameterise an empirical fish productivity model. We found that biotic predictors explained more variation in productivity relative to the abiotic variables. The best empirical model included day‐of‐year, growing degree days, latitude, salmonid presence/absence, species richness, and upstream catchment area. Our findings indicate that a combination of both biotic and abiotic variables can provide valuable insight into how ecological processes, such as fish productivity, differ across ecosystems. Species richness and differences in assemblage characteristics may be key determinants of the overall fish productivity in stream systems. Our model can estimate productivity from salmonid presence/absence data and total species richness, instead of fish abundance data, which require larger sampling efforts.

Factors acting on the floristic variability of the arboreal stratum: a review for the atlantic forest biodiversity hotspot

This study highlights the main factors influencing the distribution and composition of tree species in the Atlantic Forest. It aims to understand the influence of climatic factors in shaping composition patterns, as well as the influence of soil, topography and other environmental factors on the structure and dynamics of this biodiversity hotspot. The results of this systematic review revealed the important contribution of climatic factors such as rainfall and temperature to floristic differentiation in different regions of the Atlantic Rainforest. Edaphic and topographic factors, such as soil pH, soil water stress and altitude, also made important contributions to the distribution of tree species, both locally and on larger scales. Another component identified in the research was the complex interaction of environmental and spatial factors in composition patterns. This is a very recurrent component in the Atlantic Forest, due to latitudinal and longitudinal gradients, where specific ecological conditions create different niches for species. Despite advances, much of the variation in tree species composition remains unexplained, suggesting the existence of unknown factors or limitations in current analytical methods. Thus, this study highlights the need to further explore knowledge about the impact of climate change and anthropogenic activities on Atlantic Forest fragments. In addition, it suggests identifying new determinants and understanding the intrinsic interactions between vegetation and biotic predictors to aid effective management and conservation strategies for this unique and threatened ecosystem.

Species richness of Orthoptera declines with elevation while elevational range of individual species peaks at mid elevation.

Species richness has been shown to decrease, and elevational range increase (the Rapoport effect), with elevation as a consequence of biotic and abiotic factors, but patterns are inconsistent across taxonomic groups. Despite being an important indicator taxon and a component of local communities, Orthoptera distributions at higher elevations in Europe remain unclear. We investigated the relationship of Orthoptera species richness and elevational range with elevation in the Pyrenees mountains, Europe. We conducted sweepnetting surveys supplemented by hand-sampling, at 28 sites stratified by elevation, across three study areas. Using generalised linear models, we found that species richness declined with elevation. Elevation was an important predictor of species richness, but sampling effort and vegetation structure (height and cover) also contributed to estimates of species richness. Using a nonlinear regression to model the elevational range of species over the elevational gradient, we did not observe a Rapoport effect, with elevational range peaking at mid-elevation instead. Smaller elevational ranges of species found at high elevations may be due to a combination of sampling over a restricted elevational range and the presence of specialist high-elevation species. We argue that our findings are useful for understanding species distributions with elevation at the interface between local and regional scales. Clarifying the biotic and abiotic predictors of species distribution is important for informing conservation efforts and predicting consequences of climate change.

A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring

To efficiently detect aquatic invasive species early in an invasion when control may still be possible, predictions about which locations are likeliest to be occupied are needed at fine scales but are rarely available. Occupancy modeling could provide such predictions given data of sufficient quality and quantity. We assembled a data set for the macroalga starry stonewort (Nitellopsis obtusa) across Minnesota and Wisconsin, USA, where it is a new and high-priority invader. We used these data to construct a multi-season, single-species spatial occupancy model that included biotic, abiotic, and movement-related predictors. Distance to the nearest access was an important occurrence predictor, highlighting the likely role boats play in spreading starry stonewort. Fetch and water depth also predicted occupancy. We estimated an average detection probability of 63% at sites with mean non-N. obtusa plant cover, declining to ~ 38% at sites with abundant plant cover, especially that of other Characeae. We recommend that surveyors preferentially search for starry stonewort in areas of shallow depth and high fetch close to boat accesses. We also recommend searching during late summer/early fall when detection is likelier. This study illustrates the utility of fine-scale occupancy modeling for predicting the locations of nascent populations of difficult-to-detect species.

A shifting carnivore’s community: habitat modeling suggests increased overlap between the golden jackal and the Eurasian lynx in Europe

IntroductionThe European large carnivore community is recently showing positive trends. Beyond those, other species are expanding in Europe. In recent decades, the golden jackal (Canis aureus), a medium-sized canid, has rapidly expanded to western and northern Europe, after being historically confined to the Balkans. The Eurasian lynx (Lynx lynx), an apex predator once widely distributed throughout Europe, was nearly eradicated in the 19th century. Recent reintroductions, legal protection, and its natural expansion brought the lynx to recover, though some populations still face extinction. As the carnivores’ community across Europe is rearranging, ecological interactions are changing as well. Indeed, recent observations reported the golden jackal to kleptoparasitize the lynx. Our research aims to predict the potentially suitable habitats for the golden jackal and the Eurasian lynx, detailing the areas where those are expected to coexist soon.MethodsWe model their distribution in Europe for current and future scenarios, using both citizen-science and literature data, using the latter to buffer the possible biases of the former. For this aim, we included not only climatic variables, but also several biotic and abiotic predictors relevant to both species. Moreover, given his strong influence on the golden jackal, we compare our results with the predicted distribution of the grey wolf (Canis lupus).ResultsOur predictions show an increase in habitat suitability for the golden jackal, while an opposite trend is predicted for the Eurasian lynx. Also, we predict the target species to share a large portion of their range in the future, with most of these areas suitable for wolves, too.DiscussionOur results may have several impacts in the context of large-scale management, helping to predict further expansion of the golden jackal, and to identify critical areas for lynx conservation, supporting the management of possible reintroductions, also shedding light on the large carnivores’ changing community

Environmental drivers and species traits of mesophication and xerophication in forests of western New York State

Forests of the eastern US have exhibited widespread taxonomic shifts involving decreasing abundances of many species of Quercus, Pinus and Carya and increasing abundances of many species of Acer, Fagus, Prunus as well as other genera. These taxonomic changes have been called mesophication and explained variously as decreased tolerance of fire, drought, and heat; decreased palatability for deer browsing; and increased tolerance of shade. The major driver of those shifts in taxonomy and ecological traits has been attributed to decreased surface burning due to Native American depopulation, cessation of settler-colonial land clearance, larger deer populations, and decreased drought, among other drivers. We endeavored to test which traits exhibited the most mesophication and which environmental factors best predicted that mesophication. We collected size-stratified taxonomic data for 2488 trees from 160 plots, converted that into measures of mesophyticness for each trait in each plot, and then inferred mesophication in each plot as the difference between the mesophyticness of small trees and large trees. Mesophyticness of each trait ranged from zero for a xerophytic trait to one for a mesophytic trait. We also collected site-specific data for 22 potential environmental predictors of trait changes (including abiotic, biotic, anthropogenic, and spatial predictors). All traits exhibited strong mesophication; these are, in descending order of percent of sites with strong mesophication: fire tolerance, white-tailed deer browse preference, Native American diet tree, cold tolerance, shade tolerance, drought tolerance, and general browse preference. For all traits, mesophication of small relative to large trees was greatest in plots where large trees were xerophytic, and least where large trees were mesophytic. We further found that mesophication was similar for plots regardless of the abundance or presence of Quercus, indicating that mesophication is not the same as oak decline. A diverse set of predictors best explained mesophication of taxonomic and ecological traits, but three were consistently chosen: cooler summer temperature, higher small tree density, and shorter distance to pre-1700 Native American towns. In addition to mesophication, there was also some evidence of xerophication, most strongly for an increase in heat tolerance as might be expected due to recent climate change. Our results show that forest mesophication is a complex syndrome involving changes in independent species traits that are driven by many environmental factors, creating a challenge for land managers.

Depensation in fish recruitment driven by context-dependent interactions with another predator

Recruitment depensation describes elevated juvenile mortality with declining adult population size which can prevent or delay stock recovery. Understanding the factors influencing when a population undergoes depensation provides resource agencies with targets for management action. Using estimates of depensation from 28 walleye (Sander vitreus, Percidae) populations in Wisconsin identified by Sass et al., (2021), we tested for potential abiotic and biotic predictors of walleye recruitment depensation. The best fitting model contained covariates for climate, land cover, and fish community composition, all interacting with the relative abundance of largemouth bass (Micropterus salmoides, Centrarchidae). The consistent interaction effect of largemouth bass across the other covariates suggests a key role of this species in regulating walleye recruitment dynamics at low population size. The risk of depensation was negatively correlated with largemouth bass abundance in our dataset, pointing towards continued challenges for walleye populations given the increasingly favorable social and environmental conditions for largemouth bass. Using the model, vulnerability to depensation was predicted for an additional 115 walleye lakes with insufficient data to directly estimate the risk of depensation. Predictions suggested that 73 prediction lakes are vulnerable to depensatory recruitment should population sizes significantly decrease. This predictive framework could be used to prioritize lakes for different management actions based on depensation strength and average adult population size. Lakes with low walleye abundances, but low risk of depensation, may be more likely to respond positively to management efforts and are likely better candidates than those where depensation effects are likely strong when abundance is low.

Dynamic distribution modelling using a native invasive species, the mountain pine beetle

Efforts to minimize impacts of highly mobile insect pests in a warming environment are complicated by the dynamicity and uncertainty of their distributions. Tools that aid such management efforts are urgently required for successful outcomes. Dynamic species distribution models (DSDM) represent temporal variations in the environment that match the timing and location of species occurrence leading to improved predictions of species distributions while reducing over prediction. In contrast, static SDMs treat the environment and ecological niche of the species as essentially invariant, such as climatic data averaged over long reference periods, which can result in inaccurate assessments of a species' ecological niche. We modified the traditional static SDM framework such that it incorporates both dynamic biotic and abiotic predictors to account for temporal variability in the environment. We used the native-invasive mountain pine beetle (MPB; Dendroctonus ponderosae, Hopkins) as a model species. MPB is an irruptive forest insect native to western North America; however, epidemic populations have recently crossed the Rocky Mountains and invaded the province of Alberta, Canada, giving rise to concern that the beetle will spread into the transcontinental boreal forest. We compared the ability of static and dynamic SDMs to predict the potential distribution of MPB, including the impact of host tree availability, degree of native-novel host tree introgression and interannual variation in climatic variables. Evaluation results using independent test data revealed higher predictive capacity by DSDMs when compared with SDMs. DSDMs provided robust temporal trends of MPB potential distributions while generating predictions that support the known MPB distributional shifts. The DSDM framework will help species distribution modellers in understanding the distributional dynamics of mobile species in climatically variable regions.

Evidence for Conductivity- and Macroinvertebrate-Driven Segregation of Ostracod Assemblages in Endorheic Depression Wetlands in North West Province of South Africa

Our knowledge of the ecology of non-marine Ostracoda inhabiting endorheic wetlands (pans) of the semi-arid regions of South Africa is very scarce. The present study investigates the distribution of ostracod species in grass, open, and salt pans in the central part of the North West province and tests ostracod response to abiotic and biotic predictor variables operating at a local scale. Distance-based linear models revealed three variables (pan type, water electrical conductivity and abundance of macroinvertebrate predators, and collector-gatherers) that best explained variation in the ostracod dataset. Ostracod assemblages from the three studied pan types differed by the dominance structure rather than by the species composition. Salt pans with high conductivity and high ratio of predaceous macroinvertebrates were dominated by Heterocypris giesbrechti, with accessory presence of Plesiocypridopsis newtoni. In open pans with low conductivities and the lowest ratio of predators (but highest ratio of collector-gatherers) Potamocypris mastigophora was typically a dominant species, while in grass pans, all the three mentioned species had similar relative abundances. Although our findings lend provisional support to some models of ostracod assemblage diversity across different pan types, more studies replicating endorheic depression wetlands in other regions are required before generalizations can be made.

Both biotic and abiotic predictors explain significant variation in cyanobacteria biomass across lakes from temperate to subarctic zones

AbstractThe development of cyanobacteria blooms is of increasing concern in many lakes worldwide, and as a result, modeling their predictors is vital for understanding where and why they occur. In this study, we developed and analyzed a 640‐lake data set that spans Canada and 12 ecozones to identify the drivers of cyanobacteria biomass and of several key toxin‐ and bloom‐forming genera (Microcystis, Aphanizomenon, and Dolichospermum). The database consisted of an exhaustive list of potential predictors (n = 55), including water chemistry, land‐use, and zooplankton variables. We applied a series of empirical modeling approaches to identify significant predictors and thresholds (generalized linear and additive models, mixed effect regression trees), all while accounting for ecozone variability. Across all modeling approaches, and ecozones total phosphorus was identified as the most important predictor of total cyanobacterial and focal genera biomass. In addition, cyanobacteria across Canada showed significant associations with increasing dissolved organic and inorganic carbon, and several ions. Despite the widely held notion that cyanobacteria are often toxic and/or a poor food source for zooplankton, we found a positive relationship between cyanobacteria and zooplankton, particularly with daphnid and copepod biomass. Localized top‐down forces and evolutionary adaptations resulting from long‐term exposure in eutrophic lakes are among the possible explanations for this observed positive association. By considering a suite of complementary modeling approaches, we found that nonlinear models provided greater predictive power and the random ecozone effect was minor due to the overarching importance of local abiotic and biotic factors.

On the phenology of soil organisms: Current knowledge and future steps.

Phenology is the study of timing of periodic activities in biological life cycles. It describes an inherent component of ecosystem dynamics, and shifts in biological activity have been increasingly recognized as an indicator of global change. Although phenology is mainly studied above the ground, major ecosystem processes, such as decomposition, mineralization, and nutrient cycling, are soil-dependent. Therefore, the phenology of soil organisms is a crucial, but understudied, aspect of terrestrial ecosystem functioning. We performed a systematic review of 96 studies, which reported 228 phenological observations, to evaluate the current knowledge of soil microbial and animal phenology. Despite the increasing number of soil phenology reports, most research is still concentrated in a few countries (centered in the Northern Hemisphere) and taxa (microbiota), with significant gaps in the most diverse regions of the globe (i.e., tropics) and important taxa (e.g., ants, termites, and earthworms). Moreover, biotic predictors (e.g., biodiversity and species interactions) have rarely been considered as possible drivers of soil organisms' phenology. We present recommendations for future soil phenology research based on an evaluation of the reported geographical, taxonomic, and methodologic trends that bias current soil phenology research. First, we highlight papers that depict good soil phenology practice, either regarding the research foci, methodological approaches, or results reporting. Then, we discuss the gaps, challenges, and opportunities for future research. Overall, we advocate that focusing both on highly diverse ecosystems and key soil organisms, while testing for the direct and indirect effects of biodiversity loss and climatic stressors, could increase our knowledge of soil functioning and enhance the accuracy of predictions depicting the effects of global change on terrestrial ecosystem functioning as a whole.