Markus Neteler

Background: Globally, research and policy groups often lack the expertise to develop models to analyze invasive and native species of agroecological and veterinary/human health importance; models to inform management and policy under extant and climate change scenarios at various geographic scales. Species distribution models (SDMs) correlate weather and other variables to species presence records and are currently the mainstay for predicting the geographic distribution of species, but SDMs do not predict the underpinning dynamics required to develop policy and management strategies. Weather-driven physiologically based demographic models (PBDMs) of single and multi-trophic dynamics based on sound biological data can bridge much of this gap. The development of web platform software is proposed to assist non-experts in formulating PBDMs to help solve agroecological and veterinary/human health pest problems. Results: PBDMs are time-varying life tables (TVLTs) that capture the weather-driven biology of per capita resource acquisition and allocation to growth and reproduction in age-mass structured trophic settings independent of time and place. Two approaches are used to parameterize PBDMs: (1) mass/energy acquisition and allocation, and (2) biodemographic functions fitted to vital rates estimated from age-specific life table studies conducted under a range of abiotic conditions; vital rates that result from resource acquisition and allocation under experimental conditions. To outline the development and demonstrate the utility of this approach as web platform software for nonexperts, PBDMs for thirteen species of agroecological and medical/veterinary importance to Africa are developed and used to predict prospectively their geographic distribution, relative abundance, and dynamics across the continent. Where possible, PBDM predictions are compared to published studies and references are made to their use in management and policy development. Conclusions: The development and utility of web platform software based on the PBDM paradigm is outlined, which aims to guide non-experts in formulating realistic models and gathering the biological data to parameterize them while providing links to relevant weather data for running the models and tools for GIS mapping and statistical analysis of model output for policy and management development. Numerous published heritage PBDMs that the web platform software would make available are summarized in the Supplementary Information.

Studies on the phylogeographic structure of species (strains) have yielded insights into their geographic distribution but tell less about strain's capacity to invade novel environments. Extensive age‐specific life table data on two strains of the invasive Palearctic alfalfa weevil, Hypera postica (Gyllenhal) (i.e., Ebro Valley, Spain (S) and Hamadan, Iran (I) strains) having disparate vital rates, are used to develop weather‐driven physiologically based demographic models (PBDMs) of their biology. The PBDMs are used to explore prospectively their invasive potential across much of the Holarctic. Using N.E. Gilbert's theory that fecundity is selected to the level appropriate for the population in its environment, we explore the interacting effects of multiples of observed fecundity, temperature‐dependent mortality and density‐dependent population growth constraints on stabilizing adaptation in strains S and I to weather in their native area. Aestivating adults () bridge the critical winter period, and their annual production is used as a metric of adaptation. Maximization of in the native environments supports Gilbert's supposition. The S and I strains have wide invasive potential, and we posit stabilizing selection for maximizing would occur after successful invasion of novel environments. We propose that the evolving adaptation of an invasive strain to extant and climate change weather can be examined by periodic updates of the biodemographic biology in the weather‐driven PBDMs.

Any place the human species inhabits is inevitably modified by them. One of the first features that appear everywhere, in urban areas as well as in the countryside or deep forests, are roads. Further, roads and streets in general reflect their omnipresent and significant role in our lives through the flow of goods, people, and even culture and information. However, their contribution to the public is highly influenced by their surface. Yet, research on automated road surface classification from remotely sensed data is peculiarly scarce. This work investigates the capacities of chosen convolutional neural networks (fully convolutional network (FCN), U-Net, SegNet, DeepLabv3+) on this task. We find that convolutional neural network (CNN) are capable of distinguishing between compact (asphalt, concrete) and modular (paving stones, tiles) surfaces for both roads and sidewalks on aerial data of spatial resolution of 10 cm. U-Net proved its position as the best-performing model among the tested ones, reaching an overall accuracy of nearly 92%. Furthermore, we explore the influence of adding a near-infrared band to the basic red green blue (RGB) scenes and stress where it should be used and where avoided. Overfitting strategies such as dropout and data augmentation undergo the same examination and clearly show their pros and cons. Convolutional neural networks are also compared to single-pixel based random forests and show indisputable advantage of the context awareness in convolutional neural networks, U-Net reaching almost 25% higher accuracy than random forests. We conclude that convolutional neural networks and U-Net in particular should be considered as suitable approaches for automated semantic segmentation of road surfaces on aerial imagery, while common overfitting strategies should only be used under particular conditions.

Globally, research and policy groups often lack the expertise to develop appropriate models to analyze agroecological and invasive species problems holistically to inform management and quarantine policy development under extant and climate change over wide geographic landscapes. Off-the-shelf species distribution models (SDM) correlate weather and other variables to records of species presence and have become the mainstay for predicting the geographic distribution and favorability of invasive species (Elith 2017). However, SDM analyses lack the capacity to examine the underpinning dynamics of agroecosystems required to inform policy and develop management strategies. We propose that age-structured physiologically based demographic models (PBDMs) can solve important aspects of this challenge as they can be used to examine prospectively species dynamics locally as well as their potential geographic distribution and relative abundance across vast areas independent of presence records. PBDMs fall under the ambit of time-varying life tables (TVLTs; cf. Gilbert et al . 1976) and capture the weather driven biology, dynamics, and interactions of species, and can be used to examine the system from the perspective of any of the interacting species. Here, we use the PBDM structure to examine the dynamics across Africa of thirteen invasive species from various taxa having diverse biology and trophic interactions (see Gutierrez 1996, Gutierrez and Ponti 2013a). PBDMs are perceived to be difficult to develop, hence the raison d’être is to show this is not the case and illustrate their utility invasive and endemic agricultural and medical/veterinary pest species at the local and the large geographic scale of Africa. We note that PBDMs provide a structure for continued model improvements. The development of open access software is proposed to facilitate PBDM development by non-experts emphasizing the crucial role of sound biological data on species responses to weather and to other species in a multi-trophic, interactions, and provide a guide for collecting the appropriate biological data. While the emphasis is on plant/arthropod interactions, models of diseases can be accommodated. The Supplemental Materials summarizes a large array of heritage PBDMs reported in the literature based on the methods outline herein, noting that the same model structure can be used to analyze and manage non pest species.

The Open Source Geospatial Foundation (OSGeo) is an international community, which maintains and develops a growing number of quality-assured open source geospatial software tools. OSGeo supports Open Science and the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The open source software projects of OSGeo are drivers for innovation, but also benefit from technical advances made in other fields to foster FAIR. We report on the of adoption of persistent identifiers (PID) by OSGeo communities, which is accelerating and diversifying. Persistent referencing by PID was introduced to OSGeo for scientific video in 2014. Since 2017 it is being adopted by a growing number of OSGeo software projects to enable scientific citation of software repositories, individuals software releases and involved persons. This enables tangible scientific credit for OSGeo volunteer work and is expected to affect OSGeo workflows, such as the incubation and graduation process. For illustration, examples from the OSGeo projects GRASS GIS and actinia are provided.

Tropical fruit flies are considered among the most economically important invasive species detected in temperate areas of the United States and the European Union. Detections often trigger quarantine and eradication programs that are conducted without a holistic understanding of the threat posed. Weather-driven physiologically-based demographic models are used to estimate the geographic range, relative abundance, and threat posed by four tropical tephritid fruit flies (Mediterranean fruit fly, melon fly, oriental fruit fly, and Mexican fruit fly) in North and Central America, and the European-Mediterranean region under extant and climate change weather (RCP8.5 and A1B scenarios). Most temperate areas under tropical fruit fly propagule pressure have not been suitable for establishment, but suitability is predicted to increase in some areas with climate change. To meet this ongoing challenge, investments are needed to collect sound biological data to develop mechanistic models to predict the geographic range and relative abundance of these and other invasive species, and to put eradication policies on a scientific basis.

The capacity to assess invasion risk from potential crop pests before invasion of new regions globally would be invaluable, but this requires the ability to predict accurately their potential geographic range and relative abundance in novel areas. This may be unachievable using de facto standard correlative methods as shown for the South American tomato pinworm Tuta absoluta, a serious insect pest of tomato native to South America. Its global invasive potential was not identified until after rapid invasion of Europe, followed by Africa and parts of Asia where it has become a major food security problem on solanaceous crops. Early prospective assessment of its potential range is possible using physiologically based demographic modeling that would have identified knowledge gaps in T. absoluta biology at low temperatures. Physiologically based demographic models (PBDMs) realistically capture the weather-driven biology in a mechanistic way allowing evaluation of invasive risk in novel areas and climes including climate change. PBDMs explain the biological bases for the geographic distribution, are generally applicable to species of any taxa, are not limited to terrestrial ecosystems, and hence can be extended to support ecological risk modeling in aquatic ecosystems. PBDMs address a lack of unified general methods for assessing and managing invasive species that has limited invasion biology from becoming a more predictive science.