Frontiers of Biogeography

Caterpillars (larval Lepidoptera) are an essential link in trophic networks of forest ecosystems, as they serve as herbivores of vegetation and a food source for many organisms. Phenological mismatches between caterpillars, host plants, or predators may have negative effects across multiple trophic levels. Seasonal timing of caterpillar emergence and peak occurrence may be impacted by climate change, however, studying caterpillar phenology at broad spatial scales is challenging due to lack of data availability. Here, we examine two sources of caterpillar observations, opportunistic records from iNaturalist and structured surveys of forest caterpillars, and compare whether phenology patterns in these datasets are consistent across larval datasets and with more numerous records of adult butterflies. Despite substantial taxonomic differences between these three datasets, we found concurrence in patterns of early and late years in spring onset between datasets. However, the datasets do differ in how well they capture phenological responses to warmer spring temperatures. More data-rich iNaturalist caterpillar and adult butterfly records may provide a reasonable proxy of interannual deviations in forest caterpillars, however, expansions in structured survey efforts are needed to capture changing patterns in other ecologically important measures such as abundance and biomass.

Demonstrating the absence of a species has always been a challenge for natural sciences, which are more used to documenting their presence; however, both forms of data are of equal scientific significance. The horned sea star Protoreaster nodosus is said, in the scientific literature, to be present throughout the whole Indo-Pacific region, from eastern Africa to the Pacific Ocean islands. However, a review of the scientific literature, along with a critical bibliographical study, citizen science surveys, web-based pictures analyses, and field studies suggests that the presence of this species could instead be restricted to the western Pacific Ocean, from Thailand to Samoa and from Japan to New Caledonia, with no reliable record in the Indian Ocean. Such a huge and long-running mistake on a very common and conspicuous species exemplifies the importance of a critical approach towards species distribution data, which appears too often based on layers of reproduction of never-reassessed data, turning hypotheses into commonly shared truth.

Blue space in the urban environment can positively contribute to public health and well-being. Many urban freshwater systems, however, are exposed to anthropogenic stressors, resulting in deteriorated water quality and biodiversity. Private garden ponds are essential elements of this blue space but little is known about their water quality since they are not monitored by water professionals. The Dutch citizen science initiative Waterdiertjes.nl focusses on biological water quality assessment using macroinvertebrates and launched a campaign to investigate garden ponds in 2021. The campaign yielded macroinvertebrate recordings in 60 garden ponds and limited additional information on dimensions, and presence of water vegetation and fish. Volunteers also investigated 92 public urban ponds in 2021. Combining both data allowed for evaluation of the similarity between both pond types. Analyses were also performed to discover the importance of pond dimensions and presence of aquatic vegetation or fish for the macroinvertebrate biodiversity and water quality. Multivariate analyses showed a considerable overlap in macroinvertebrate community composition between garden and public urban ponds. Results showed high variability in taxon richness, total macroinvertebrate density and water quality scores A significant, nonlinear relation was observed between pond depth and macroinvertebrate taxon richness with the highest richness at a depth of 0.5-1m. Submerged vegetation correlated significantly with taxon richness with little vegetation having the lowest richness. Presence of fish or floating vegetation was not significantly related to taxon richness, total density and water quality scores possibly due to unnatural conditions that underlie in garden ponds. Since water professionals and academic scientists do not monitor garden ponds, citizen science may provide valuable data on water quality and biodiversity in these waters. The present initiative showed that volunteers can successfully monitor understudied ecosystems and gather data on temporal and spatial scales complementary to professional data that lead to new (ecological) insights.

The introduction of new species can have unpredictable effects on native communities. Understanding the potential for competitive interactions between widow spiders (Theridiidae: Latrodectus), and how they may have shaped species’ geographic distributions, is critical for predicting the impacts of biological invasions in this historically cryptic group. North America is home to three native widows (L. hesperus, L. mactans, L. variolus) and one invasive widow (L. geometricus) with distributions that are at least partly sympatric. Given the relative novelty of L. geometricus in native communities as they expand their range, it is unclear if and how they share resources with their congeners, and competition for climatic resources (space) could result in ecological impacts to native widows. Here we aim to model niche differentiation and niche dynamics between native widows, that have coevolved over time, and between native and brown widows to assess the potential for ecological impact. We investigated the potential for climatic niche partitioning to shape sympatric geographic distributions of native widows compared to each other and to L. geometricus. We aggregated photographed occurrences from social media communities and online repositories to quantify climatic niche overlap for all four species on a continental scale, to assess niche dynamics among native species and between invasive and nativespecies. We found that native species had distributions that were more strongly partitioned, showing weaker niche overlap, except for the two eastern widows, which showed strong overlap and niche equivalency. Conversely, we found greater niche overlap between L. geometricus and native widows, possibly because it is too soon to see the effect of competition on species’ geographic distributions, or because differences in diet or partitioning of urban versus nonurban microhabitats promote coexistence.

Citizen science programs are effective methods to collect large volumes of data to assist researchers in monitoring ecological environments. As species shift their distributions globally due to climate change, the use of citizen science data to detect these shifts is increasing. Using targeted citizen science programs to collect data on these species could provide information on range edges to inform species distribution modelling. Currently, species distribution models (SDMs) often rely on large data repositories that may lack observations, and hence ability, to detect changes at the range edge. Here, we developed a SDM to compare traditional data repository observations with targeted citizen science data at the southern distribution limit of two recreationally important marine fish in Tasmania, Australia to investigate the potential change in spatial predictions at their range edge. The SDM using the targeted citizen science data in addition to traditional observation data improved the representation of species by 2.3 and 52.7% and increased the southern distribution of the species by 277 and 438 km, for snapper and King George whiting, respectively. Future (centred around 2050 under IPCC RCP 8.5) habitat suitability was predicted to increase more over the winter season, with implications for species overwintering and persistence of populations. The use of citizen science data allowed for the modelling of historical and future change for two range-extending species, an outcome possible due to the collaboration of two citizen science programs that collected observational data on the target species. Species range shifts will require ongoing monitoring and we have demonstrated that complimentary citizen science initiatives are effective in capturing occurrences of species at their range edge. Increasing collaboration between programs may further increase data collection efforts and provide the knowledge to create a hub for these data to be used more efficiently in the future.

Species–area models now frequently include habitat heterogeneity. These models often fit real-world data better than those that exclude this factor. However, such models usually link the effects of habitat heterogeneity and study area. Critically, we show that difficulties in quantifying habitat heterogeneity within these models can lead to distortions of the apparent effect of area on species richness. Here, we derive a model that minimises these distortions by partitioning the influence of habitat heterogeneity from that of area, without compromising ease of application. This ‘jigsaw model’ achieves this by assuming that different habitats within an area can support similar numbers of species. We compare the behaviour of this model to that of existing models of similar complexity using both simulated island ecosystems and 40 published empirical datasets. The effects of habitat heterogeneity and area on species richness vary independently in our simulations, and these independent effects are recovered by the jigsaw model. This flexibility, however, is not present when the same data are analysed using other models of similar complexity. When applied to real-world data, the jigsaw model demonstrates that the relative importance of area and habitat heterogeneity varies depending on the study system. The jigsaw model provides the best fit to real-world data (according to AICc) of all tested models in logarithmic form, and the second best fit, after the choros model, in power-law form. Our results demonstrate the importance of partitioning the effects of habitat heterogeneity and area on species richness in biogeographic models. The jigsaw model is a simple but powerful tool for such partitioning. It has the potential to elucidate the underlying drivers of species richness patterns, and to be used as a tool in biological conservation projects, where data are often incomplete.