Improving prediction of rare species' distribution from community data

Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network

The occurrence and regularity of macrofungal fruitbody formation are influenced by meteorological conditions; however, there is a scarcity of data about the use of machine-learning techniques to estimate their occurrence based on meteorological indicators. Therefore, we employed an artificial neural network (ANN) to forecast fruitbody occurrence in mycorrhizal species of Russula and Amanita, utilizing meteorological factors and validating the accuracy of the forecast of fruitbody formation. Fungal data were collected from two locations in Western Hungary between 2015 and 2020. The ANN was the commonly used algorithm for classification problems: feed-forward multilayer perceptrons with a backpropagation algorithm to estimate the binary (Yes/No) classification of fruitbody appearance in natural and undisturbed forests. The verification indices resulted in two outcomes: however, development is most often studied by genus level, we established a more successful, new model per species. Furthermore, the algorithm is able to successfully estimate fruitbody formations with medium to high accuracy (60-80%). Therefore, this work was the first to reliably utilise the ANN approach of estimating fruitbody occurrence based on meteorological parameters of mycorrhizal specified with an extended vegetation period. These findings can assist in field mycological investigations that utilize sporocarp occurrences to ascertain species abundance.

Models of spatial analysis for vector-borne diseases studies: A systematic review

Background and Aim: Vector-borne diseases (VBDs) constitute a global problem for humans and animals. Knowledge related to the spatial distribution of various species of vectors and their relationship with the environment where they develop is essential to understand the current risk of VBDs and for planning surveillance and control strategies in the face of future threats. This study aimed to identify models, variables, and factors that may influence the emergence and resurgence of VBDs and how these factors can affect spatial local and global distribution patterns. Materials and Methods: A systematic review was designed based on identification, screening, selection, and inclusion described in the research protocols according to the preferred reporting items for systematic reviews and meta-analyses guide. A literature search was performed in PubMed, ScienceDirect, Scopus, and SciELO using the following search strategy: Article type: Original research, Language: English, Publishing period: 2010–2020, Search terms: Spatial analysis, spatial models, VBDs, climate, ecologic, life cycle, climate variability, vector-borne, vector, zoonoses, species distribution model, and niche model used in different combinations with "AND" and "OR." Results: The complexity of the interactions between climate, biotic/abiotic variables, and non-climate factors vary considerably depending on the type of disease and the particular location. VBDs are among the most studied types of illnesses related to climate and environmental aspects due to their high disease burden, extended presence in tropical and subtropical areas, and high susceptibility to climate and environment variations. Conclusion: It is difficult to generalize our knowledge of VBDs from a geospatial point of view, mainly because every case is inherently independent in variable selection, geographic coverage, and temporal extension. It can be inferred from predictions that as global temperatures increase, so will the potential trend toward extreme events. Consequently, it will become a public health priority to determine the role of climate and environmental variations in the incidence of infectious diseases. Our analysis of the information, as conducted in this work, extends the review beyond individual cases to generate a series of relevant observations applicable to different models.

A framework to integrate innovations in invasion science for proactive management

Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.

Small- and large-scale processes including anthropogenic pressures as drivers of gastropod communities in the NE Atlantic coast: A citizen science based approach

Rocky-shores are among the most threatened coastal habitats, particularly under human pressures. While rocky-shore communities have been increasingly used to evaluate local anthropogenic perturbations such as water eutrophication for instance, large-scale variability in relation to both natural and anthropogenic pressures is still overlooked. Here, using citizen science-based data, we applied a Random Forest modelling approach to assess the relative impact of both small-and large-scale processes (including natural and some anthropogenic pressures) on intertidal gastropod communities as well as taxa-specific gastropod abundances over more than 1000 km of the North-East Atlantic coast. Our model results demonstrate that small-scale conditions (i.e. within shore) are determinant in shaping gastropod communities. However, community responses are mainly driven by taxon-specific effects. Among large-scale predictors, high concentrations of inorganic nutrients and total suspended matter, as found in large river plumes, are the main drivers impacting the gastropod communities by depleting both taxon abundances and richness. According to models, the decline in abundance of the yet most prevalent taxa (Steromphala umbilicalis, Patella spp., S. pennanti and Phorcus lineatus) is expected to be between 65% and 85%, while Littorina littorea was the only taxon which may be favoured by high concentrations of inorganic nutrients and total suspended matter. Such results provide relevant insights in the context of ever-increasing needs for bioindicators of coastal ecosystems. Finally, this work sheds light on the value of a citizen science program resulting from a consultation between professional and citizen volunteers as a useful and efficient tool to investigate large-scale processes.

A review of invasive species reporting apps for citizen science and opportunities for innovation

Smartphone apps have enhanced the potential for monitoring of invasive alien species (IAS) through citizen science. They now have the capacity to massively increase the volume and spatiotemporal coverage of IAS occurrence data accrued in centralised databases. While more reporting apps are developed each year, innovation across diverse functionalities and data management in this field are occurring separately and simultaneously amongst numerous research groups with little attention to trends, priorities and opportunities for improvement. This creates the risk of duplication of effort and missed opportunities for implementing new and existing functionalities that would directly benefit IAS research and management. Using a literature search of Early Detection and Rapid Response implementation, smartphone app development and invasive species reporting apps, we developed a rubric for quantitatively assessing the functionality of IAS reporting apps and applied this rubric to 41 free, English-language IAS reporting apps, available via major mobile app stores in North America. The five highest performing apps achieved scores of 61.90% to 66.35% relative to a hypothetical maximum score, indicating that many app features and functionalities, acknowledged to be useful for IAS reporting in literature, are not present in sampled apps. This suggests that current IAS reporting apps do not make use of all available and known functionalities that could maximise their efficacy. Major implementation gaps, highlighted by this rubric analysis, included limited implementation in user engagement (particularly gamification elements and social media compatibility), ancillary information on search effort, detection method, the ability to report absences and local habitat characteristics. The greatest advancement in IAS early detection would likely result from app gamification. This would make IAS reporting more engaging for a growing community of non-professional contributors and encourage frequent and prolonged participation. We discuss these implementation gaps in relation to the increasingly urgent need for Early Detection and Rapid Response frameworks. We also recommend future innovations in IAS reporting app development to help slow the spread of IAS and curb the global economic and biodiversity extinction crises. We also suggest that further funding and investment in this and other implementation gaps could greatly increase the efficacy of current IAS reporting apps and increase their contributions to addressing the contemporary biological invasion threat.

Automatic variable selection in ecological niche modeling: A case study using Cassin's Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max's Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin's Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim's Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species' bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA's Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

The use of singlebeam echo-sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo-sounder depth

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%-10% of the world's seafloor has been mapped at high resolution, as it is a time-consuming and expensive process. Multibeam echo-sounders (MBES) can produce high-resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo-sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full-coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo-sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource-limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation.

Protecting Biodiversity (in All Its Complexity): New Models and Methods

We are facing a biodiversity crisis at the same time as we are acquiring an unprecedented view of the world's biodiversity. Vast new datasets (e.g., species distributions, traits, phylogenies, and interaction networks) hold knowledge to better comprehend the depths of biodiversity change, reliably anticipate these changes, and inform conservation actions. To harness this information for conservation, we need to integrate the largely independent fields of biodiversity modeling and conservation. We highlight new developments in each respective field, early examples of how they are being brought together, and ideas for a future synthesis such that conservation decisions can be made with fuller awareness of the biodiversity at stake.