Sampling and modelling rare species: Conceptual guidelines for the neglected majority

Upscaling biodiversity monitoring: Metabarcoding estimates 31,846 insect species from Malaise traps across Germany

Mitigating ongoing losses of insects and their key functions (e.g. pollination) requires tracking large-scale and long-term community changes. However, doing so has been hindered by the high diversity of insect species that requires prohibitively high investments of time, funding and taxonomic expertise when addressed with conventional tools. Here, we show that these concerns can be addressed through a comprehensive, scalable and cost-efficient DNA metabarcoding workflow. We use 1815 samples from 75 Malaise traps across Germany from 2019 and 2020 to demonstrate how metabarcoding can be incorporated into large-scale insect monitoring networks for less than 50 € per sample, including supplies, labour and maintenance. We validated the detected species using two publicly available databases (GBOL and GBIF) and the judgement of taxonomic experts. With an average of 1.4 M sequence reads per sample we uncovered 10,803 validated insect species, of which 83.9% were represented by a single Operational Taxonomic Unit (OTU). We estimated another 21,043 plausible species, which we argue either lack a reference barcode or are undescribed. The total of 31,846 species is similar to the number of insect species known for Germany (~35,500). Because Malaise traps capture only a subset of insects, our approach identified many species likely unknown from Germany or new to science. Our reproducible workflow (~80% OTU-similarity among years) provides a blueprint for large-scale biodiversity monitoring of insects and other biodiversity components in near real time.

Using mangrove and field observation data to identify fine-scale species distributions: a case study in bockadams (Serpentes: Homalopsidae: Cerberus)

Characterization of species distributions is a fundamental challenge in biodiversity science, with particular significance for downstream evolutionary studies, conservation efforts, field-based faunal studies and estimates of species diversity. Checklists and phylogenetic studies often focus on poorly known, rare taxa with limited ranges. However, studies of widely distributed, ecologically important species that are abundant in their preferred microhabitats are also important for systematics and local conservation efforts, but less often studied. We collected novel natural history data during fieldwork (2019-2023) for Philippine populations of bockadams (Homalopsidae: Cerberus), one of the most abundant vertebrates in Southeast Asian aquatic systems. Considered a coastal snake, many studies report Cerberus inland. We report the frequency of encounters of Cerberus schneiderii, and the IUCN data-deficient, Philippine-endemic Cerberus microlepis during six expeditions (62 days; 1041 person-hours). We report new occurrence data for 69 C. schneiderii and 6 C. microlepis for coastal and inland populations, water measurements and dietary observations. Regression analyses and ecological niche models show the importance of coastal and mangrove habitats for Cerberus. Our study is the most comprehensive assessment of Philippine Cerberus populations to date and provides critical baseline natural history data for downstream research on widespread and range-restricted species of Southeast Asian snakes.

Seasonality of primary production explains the richness of pioneering benthic communities

A pattern of increasing species richness from the poles to the equator is frequently observed in many animal taxa. Ecological limits, determined by the abiotic conditions and biotic interactions within an environment, are one of the major factors influencing the geographical distribution of species diversity. Energy availability is often considered a crucial limiting factor, with temperature and productivity serving as empirical measures. However, these measures may not fully explain the observed species richness, particularly in marine ecosystems. Here, through a global comparative approach and standardised methodologies, such as Autonomous Reef Monitoring Structures (ARMS) and DNA metabarcoding, we show that the seasonality of primary production explains sessile animal richness comparatively or better than surface temperature or primary productivity alone. A Hierarchical Generalised Additive Model (HGAM) is validated, after a model selection procedure, and the prediction error is compared, following a cross-validation approach, with HGAMs including environmental variables commonly used to explain animal richness. Moreover, the linear effect of production magnitude on species richness becomes apparent only when considered jointly with seasonality, and, by identifying world coastal areas characterized by extreme values of both, we postulate that this effect may result in a positive relationship in environments with lower seasonality.

Mapping soil trace metal distribution using remote sensing and multivariate analysis

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Assessing the utility of SoilGrids250 for biogeographic inference of plant populations

Inclusion of edaphic conditions in biogeographical studies typically provides a better fit and deeper understanding of plant distributions. Increased reliance on soil data calls for easily accessible data layers providing continuous soil predictions worldwide. Although SoilGrids provides a potentially useful source of predicted soil data for biogeographic applications, its accuracy for estimating the soil characteristics experienced by individuals in small-scale populations is unclear. We used a biogeographic sampling approach to obtain soil samples from 212 sites across the midwestern and eastern United States, sampling only at sites where there was a population of one of the 22 species in Lobelia sect. Lobelia. We analyzed six physical and chemical characteristics in our samples and compared them with predicted values from SoilGrids. Across all sites and species, soil texture variables (clay, silt, sand) were better predicted by SoilGrids (R 2: .25-.46) than were soil chemistry variables (carbon and nitrogen, R 2 ≤ .01; pH, R 2: .19). While SoilGrids predictions rarely matched actual field values for any variable, we were able to recover qualitative patterns relating species means and population-level plant characteristics to soil texture and pH. Rank order of species mean values from SoilGrids and direct measures were much more consistent for soil texture (Spearman r S = .74-.84; all p < .0001) and pH (r S = .61, p = .002) than for carbon and nitrogen (p > .35). Within the species L. siphilitica, a significant association, known from field measurements, between soil texture and population sex ratios could be detected using SoilGrids data, but only with large numbers of sites. Our results suggest that modeled soil texture values can be used with caution in biogeographic applications, such as species distribution modeling, but that soil carbon and nitrogen contents are currently unreliable, at least in the region studied here.

Incorporating eco-evolutionary information into species distribution models provides comprehensive predictions of species range shifts under climate change

As climate changes increasingly influence species distributions, ecosystem functions, and biodiversity, the urgency to understand how species' ranges shift under those changes is great. Species distribution models (SDMs) are vital approaches that can predict species distributions under changing climates. However, SDMs based on the species' current occurrences may underestimate the species' climatic tolerances. Integrating species' realized niches at different periods, also known as multi-temporal calibration, can provide an estimation closer to its fundamental niche. Based on this, we further proposed an integrated framework that combines eco-evolutionary data and SDMs (phylogenetically-informed SDMs) to provide comprehensive predictions of species range shifts under climate change. To evaluate our approach's performance, we applied it to a group of related species, the Chrysanthemum zawadskii species complex (Anthemidae, Asteracee). First, we investigated the niche differentiation between species and intraspecific lineages of the complex and estimated their rates of niche evolution. Next, using both standard SDMs and our phylogenetically-informed SDMs, we generated predictions of suitability areas for all species and lineages and compared the results. Finally, we reconstructed the historical range dynamics for the species of this complex. Our results showed that the species and intraspecific lineages of the complex had varying degrees of niche differentiation and different rates of niche evolution. Lineage-level SDMs can provide more realistic predictions for species with intraspecific differentiation than species-level models can. The phylogenetically-informed SDMs provided more complete environmental envelopes and predicted broader potential distributions for all species than the standard SDMs did. Range dynamics varied among the species that have different rates of niche evolution. Our framework integrating eco-evolutionary data and SDMs contributes to a better understanding of the species' responses to climate change and can help to make more targeted conservation efforts for the target species under climate change, particularly for rare species.

N, Subedi S, Gautam S, Ayer S, Bayne E. Gharial (Gavialis gangeticus) conservation in Bardia National Park, Nepal: Assessing population structure and habitat characteristics along the river channel amidst infrastructure development

Nepal initiated numerous hydropower and irrigation-related infrastructure projects to enhance and promote green energy, water security, and agricultural productivity. However, these projects may pose risks to natural habitats and the well-being of aquatic fauna, leading to significant effects on delicate ecosystems. To understand these potential impacts, it is crucial to gather reliable baseline data on the population status and habitat characteristics of species. This study specifically focuses on Gharials (Gavialis gangeticus), a critically endangered species. We recorded data on pre-determined habitat variables at stations spaced 500 m apart along the two major river streams of Bardia National Park, as well as at locations where Gharials were sighted between February and March 2023. We used binary logistic regression with a logit link function to investigate the habitat characteristics related to the occurrence of Gharials. The presence/absence of Gharials at sampling points served as the dependent variable, while 10 other predetermined variables (ecological variables and disturbance variables) served as independent variables. Our study recorded 23 Gharials, comprising 14 adults, six sub-adults, and three juveniles, with a sex ratio of 55.56 males per 100 females. Most individuals (83%) were found basking. Among the 10 habitat predictors, three variables (mid-river depth, river width, and water temperature) were significantly correlated (p < .05) with the probability of Gharial occurrence. The model shows that Gharial detection probability increases with greater mid-river depth and width and lower water temperature. This study establishes a population baseline for Gharials within the river system before the construction of large infrastructure projects, such as dams and irrigation canals. It also recommends continuous monitoring of Gharial populations after water release and/or diversion to evaluate the impact of large infrastructure projects on the population and their associated habitat characteristics. This will help enable more informed and targeted conservation efforts.

Malagasy Amphibian Wildlife Trade Revisited: Improving Management Knowledge of the Trade

Madagascar is a biodiversity hotspot with a long history of trading in its wildlife, especially its hyper-diverse amphibian taxa. Due to globally raised concerns over the conservation of harvested species, CITES was introduced as a global mechanism with which to monitor and regulate the trade. Utilising data collated from the CITES Trade database, this study sought to investigate the trade and CITES' effectiveness in managing the trade with respect to Madagascar. Over a 28-year period, 20 known amphibian species were exported from Madagascar, constituting a total of nearly 271,000 individuals. Formal descriptions of Malagasy amphibian species have increased and continue to increase greatly over time. However, there was no longitudinal relationship regarding the numbers of individuals traded as new species were described. Overall, the number of individuals traded has declined over time, but where assessments were provided by the IUCN Redlist, population declines were reported in all but one species of Malagasy amphibian. Mantella (97.5%) continues to be the predominantly traded genus, with certain, high-conservation-concern, species continuing to be traded. Despite initial concerns over the effectiveness of CITES's actions, after concerted efforts, it appears that CITES' actions were having positive impacts on regulating the trade. However, going forward, concerns remain over the appropriateness of the quotas set and the robustness of their underpinning NDFs. Furthermore, with the increase in the number of recognised species, the potential for incorrect species labelling on the CITES permits increases and requires greater attention.

Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China

Climate has critical roles in the origin, pathogenesis and transmission of infectious zoonotic diseases. However, large-scale epidemiologic trend and specific response pattern of zoonotic diseases under future climate scenarios are poorly understood. Here, we projected the distribution shifts of transmission risks of main zoonotic diseases under climate change in China. First, we shaped the global habitat distribution of main host animals for three representative zoonotic diseases (2, 6, and 12 hosts for dengue, hemorrhagic fever, and plague, respectively) with 253,049 occurrence records using maximum entropy (Maxent) modeling. Meanwhile, we predicted the risk distribution of the above three diseases with 197,098 disease incidence records from 2004 to 2017 in China using an integrated Maxent modeling approach. The comparative analysis showed that there exist highly coincident niche distributions between habitat distribution of hosts and risk distribution of diseases, indicating that the integrated Maxent modeling is accurate and effective for predicting the potential risk of zoonotic diseases. On this basis, we further projected the current and future transmission risks of 11 main zoonotic diseases under four representative concentration pathways (RCPs) (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in 2050 and 2070 in China using the above integrated Maxent modeling with 1,001,416 disease incidence records. We found that Central China, Southeast China, and South China are concentrated regions with high transmission risks for main zoonotic diseases. More specifically, zoonotic diseases had diverse shift patterns of transmission risks including increase, decrease, and unstable. Further correlation analysis indicated that these patterns of shifts were highly correlated with global warming and precipitation increase. Our results revealed how specific zoonotic diseases respond in a changing climate, thereby calling for effective administration and prevention strategies. Furthermore, these results will shed light on guiding future epidemiologic prediction of emerging infectious diseases under global climate change.

A quixotic view of spatial bias in modelling the distribution of species and their diversity

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling.

'Small Data' for big insights in ecology

Big Data science has significantly furthered our understanding of complex systems by harnessing large volumes of data, generated at high velocity and in great variety. However, there is a risk that Big Data collection is prioritised to the detriment of 'Small Data' (data with few observations). This poses a particular risk to ecology where Small Data abounds. Machine learning experts are increasingly looking to Small Data to drive the next generation of innovation, leading to development in methods for Small Data such as transfer learning, knowledge graphs, and synthetic data. Meanwhile, meta-analysis and causal reasoning approaches are evolving to provide new insights from Small Data. These advances should add value to high-quality Small Data catalysing future insights for ecology.

From design to analysis: A roadmap for predicting distributions of rare species

Rare species are challenging to study, in part because rarity can take many forms. Jeliazkov et al. guide us through the multiple decisions to be made-from sampling designs to field methods and analytical, integrated models. Improved monitoring methods are needed to improve our understanding of rare species importance for ecosystem structure and functions. This is a commentary on Jeliazkov et al., 2022, https://doi.org/10.1111/gcb.16114.