The art and science of weed mapping

Characterizing the environmental drivers of the abundance and distribution of Alopecurus myosuroides on a national scale

BACKGROUND

Arable weeds threaten farming and food production, impacting on productivity. Large-scale data on weed populations are typically lacking, and changes are frequently undocumented until they reach problem levels. Managing the future spread of weeds requires that we understand the factors that influence current densities and distributions. In doing so, one of the challenges is to measure populations at a large enough scale to be able to accurately measure changes in densities and distributions. Here we analyse the density and distribution of a major weed (Alopecurus myosuroides) on a large scale. Our objectives were to (i) develop a methodology for rapid measurement of occurrence and abundance, (ii) test hypotheses about the roles of soils and climate variation in determining densities, and (iii) use this information to identify areas in which occurrence could increase in the future.

RESULTS

Populations were mapped through England over 4 years in 4631 locations. We also analysed UK atlas data published over the past 50 years. Densities of populations show significant interannual variability, but historical data show that the species has spread. We find significant impacts of soil and rainfall on densities, which increase with the proportion of heavy soils, but decrease with increasing rainfall. Compared with independent atlas data we found that our statistical models provide good predictions of large-scale occupancy and we provide maps of current and potential densities.

CONCLUSION

Models of spread highlight the localised nature of colonisation, and this emphasises the need for management to limit dispersal. Comparisons of current, historical and potential distributions suggest sizeable habitable areas in which increases in abundance are still possible. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Evaluating the potential of Unmanned Aerial Systems for mapping weeds at field scales: a case study with Alopecurus myosuroides

Mapping weed densities within crops has conventionally been achieved either by detailed ecological monitoring or by field walking, both of which are time-consuming and expensive. Recent advances have resulted in increased interest in using Unmanned Aerial Systems (UAS) to map fields, aiming to reduce labour costs and increase the spatial extent of coverage. However, adoption of this technology ideally requires that mapping can be undertaken automatically and without the need for extensive ground-truthing. This approach has not been validated at large scale using UAS-derived imagery in combination with extensive ground-truth data. We tested the capability of UAS for mapping a grass weed, Alopecurus myosuroides, in wheat crops. We addressed two questions: (i) can imagery accurately measure densities of weeds within fields and (ii) can aerial imagery of a field be used to estimate the densities of weeds based on statistical models developed in other locations? We recorded aerial imagery from 26 fields using a UAS. Images were generated using both RGB and Rmod (Rmod 670-750 nm) spectral bands. Ground-truth data on weed densities were collected simultaneously with the aerial imagery. We combined these data to produce statistical models that (i) correlated ground-truth weed densities with image intensity and (ii) forecast weed densities in other fields. We show that weed densities correlated with image intensity, particularly Rmod image data. However, results were mixed in terms of out of sample prediction from field-to-field. We highlight the difficulties with transferring models and we discuss the challenges for automated weed mapping using UAS technology.

Citizen Science as a Tool in Biological Recording—A Case Study of Ailanthus altissima (Mill.) Swingle

Non-native invasive species frequently appear in urban and non-urban ecosystems and may become a threat to biodiversity. Some of these newcomers are introduced accidentally, and others are introduced through a sequence of events caused by conscious human decisions. Involving the general public in biodiversity preservation activities could prevent the negative consequences of these actions. Accurate and reliable data collecting is the first step in invasive species management, and citizen science can be a useful tool to collect data and engage the public in science. We present a case study of biological recording of tree of heaven (Ailanthus altissima (Mill.) Swingle) using a participatory citizen model. The first goal in this case study was to develop a cheap, widely accessible, and effective inventory method, and to test it by mapping tree of heaven in Croatia. A total of 90.61 km of roads and trails was mapped; 20 single plants and 19 multi-plant clusters (mapped as polygons) were detected. The total infested area was 2610 m2. The second goal was to educate citizens and raise awareness of this invasive species. The developed tool and suggested approach aided in improving invasive risk management in accordance with citizen science principles and can be applied to other species or areas.

An iterative and targeted sampling design informed by habitat suitability models for detecting focal plant species over extensive areas

Prioritizing areas for management of non-native invasive plants is critical, as invasive plants can negatively impact plant community structure. Extensive and multi-jurisdictional inventories are essential to prioritize actions aimed at mitigating the impact of invasions and changes in disturbance regimes. However, previous work devoted little effort to devising sampling methods sufficient to assess the scope of multi-jurisdictional invasion over extensive areas. Here we describe a large-scale sampling design that used species occurrence data, habitat suitability models, and iterative and targeted sampling efforts to sample five species and satisfy two key management objectives: 1) detecting non-native invasive plants across previously unsampled gradients, and 2) characterizing the distribution of non-native invasive plants at landscape to regional scales. Habitat suitability models of five species were based on occurrence records and predictor variables derived from topography, precipitation, and remotely sensed data. We stratified and established field sampling locations according to predicted habitat suitability and phenological, substrate, and logistical constraints. Across previously unvisited areas, we detected at least one of our focal species on 77% of plots. In turn, we used detections from 2011 to improve habitat suitability models and sampling efforts in 2012, as well as additional spatial constraints to increase detections. These modifications resulted in a 96% detection rate at plots. The range of habitat suitability values that identified highly and less suitable habitats and their environmental conditions corresponded to field detections with mixed levels of agreement. Our study demonstrated that an iterative and targeted sampling framework can address sampling bias, reduce time costs, and increase detections. Other studies can extend the sampling framework to develop methods in other ecosystems to provide detection data. The sampling methods implemented here provide a meaningful tool when understanding the potential distribution and habitat of species over multi-jurisdictional and extensive areas is needed for achieving management objectives.

The impacts of an invasive species citizen science training program on participant attitudes, behavior, and science literacy

Citizen science can make major contributions to informal science education by targeting participants' attitudes and knowledge about science while changing human behavior towards the environment. We examined how training associated with an invasive species citizen science program affected participants in these areas. We found no changes in science literacy or overall attitudes between tests administered just before and after a one-day training program, matching results from other studies. However, we found improvements in science literacy and knowledge using context-specific measures and in self-reported intention to engage in pro-environmental activities. While we noted modest change in knowledge and attitudes, we found comparison and interpretation of these data difficult in the absence of other studies using similar measures. We suggest that alternative survey instruments are needed and should be calibrated appropriately to the pre-existing attitudes, behavior, and levels of knowledge in these relatively sophisticated target groups.

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.

Assessing an exotic plant surveying program in the Mojave Desert, Clark County, Nevada, USA

Exotic species can threaten native ecosystems and reduce services that ecosystems provide to humans. Early detection of incipient populations of exotic species is a key step in containing exotics before explosive population growth and corresponding impacts occur. We report the results of the first three years of an exotic plant early detection and treatment program conducted along more than 3,000 km of transportation corridors within an area >1.5 million ha in the Mojave Desert, USA. Incipient populations of 43 exotic plant species were mapped using global positioning and geographic information systems. Brassica tournefortii (Sahara mustard) infested the most soil types (47% of 256) surveyed in the study area, while Nicotiana glauca (tree tobacco) and others currently occupy less than 5% of soil types. Malcolmia africana (African mustard) was disproportionately detected on gypsum soils, occurring on 59% of gypsum soil types compared to 27% of all surveyed soils. Gypsum soils constitute unique rare plant habitat in this region, and by conventional wisdom were not previously considered prone to invasion. While this program has provided an initial assessment of the landscape-scale distribution of exotic species along transportation corridors, evaluations of both the survey methods and the effectiveness of treating incipient populations are needed. An exotic plant information system most useful to resource mangers will likely include integrating planning oriented coarse-scale surveys, more detailed monitoring of targeted locations, and research on species life histories, community invasibility, and treatment effectiveness.