Jointly modeling marine species to inform the effects of environmental change on an ecological community in the Northwest Atlantic

Single species distribution models (SSDMs) are typically used to understand and predict the distribution and abundance of marine fish by fitting distribution models for each species independently to a combination of abiotic environmental variables. However, species abundances and distributions are influenced by abiotic environmental preferences as well as biotic dependencies such as interspecific competition and predation. When species interact, a joint species distribution model (JSDM) will allow for valid inference of environmental effects. We built a joint species distribution model of marine fish and invertebrates of the Northeast US Continental Shelf, providing evidence on species relationships with the environment as well as the likelihood of species to covary. Predictive performance is similar to SSDMs but the Bayesian joint modeling approach provides two main advantages over single species modeling: (1) the JSDM directly estimates the significance of environmental effects; and (2) predicted species richness accounts for species dependencies. An additional value of JSDMs is that the conditional prediction of species distributions can use not only the environmental associations of species, but also the presence and abundance of other species when forecasting future climatic associations.

A triad of kicknet sampling, eDNA metabarcoding, and predictive modeling to assess aquatic macroinvertebrate biodiversity

Monitoring freshwater biodiversity is essential to understand the impacts of human activities and for effective management of ecosystems. Thereby, biodiversity can be assessed through direct collection of targeted organisms, through indirect evidence of their presence (e.g. signs, environmental DNA, camera trap, etc.), or through extrapolations from species distribution models (SDM). Differences in approaches used in biodiversity assessment, however, may come with individual challenges and hinder cross-study comparability. In the context of rapidly developing techniques, we compared a triad of approaches in order to understand assessment of aquatic macroinvertebrate biodiversity. Specifically, we compared the community composition and species richness of three orders of aquatic macroinvertebrates (mayflies, stoneflies, and caddisflies, hereafter EPT) obtained via eDNA metabarcoding and via traditional in situ kicknet sampling to catchment-level based predictions of a species distribution model. We used kicknet data from 24 sites in Switzerland and compared taxonomic lists to those obtained using eDNA amplified with two different primer sets. Richness detected by these methods was compared to the independent predictions made by a statistical species distribution model using landscape-level features to estimate EPT diversity. Despite the ability of eDNA to consistently detect some EPT species found by traditional sampling, we found important discrepancies in community composition between the two approaches, particularly at local scale. Overall, the more specific set of primers, namely fwhF2/EPTDr2n, was most efficient for the detection of target species and for characterizing the diversity of EPT. Moreover, we found that the species richness measured by eDNA was poorly correlated to the richness measured by kicknet sampling and that the richness estimated by eDNA and kicknet were poorly correlated with the prediction of the statistical model. Overall, however, neither eDNA nor the traditional approach had strong links to the predictive models, indicating inherent limitations in upscaling species richness estimates. Future challenges include improving the accuracy and sensitivity of each approach individually yet also acknowledge their respective limitations, in order to best meet stakeholder demands addressing the biodiversity crisis we are facing.

Global invasion risk of Apocephalus borealis, a honey bee parasitoid

Apocephalus borealis is a parasitoid of hymenopterans native to North America that also attacks introduced honey bees (Apis mellifera). Parasitism by this species has been associated with infested bees absconding the hive and dying outside. The flies can also harbour viral infections and nosematosis. Recently, nucleotide sequences identical to A. borealis were reported from bulk screenings of honey bees from Belgium and South Korea, although no adult flies have been collected. To predict the potential invasion risk of A. borealis across the world, we constructed a MaxEnt species distribution model based on occurrence data from North America submitted to the citizen science project ZomBee Watch (zombeewatch.org) and from museum specimens. The results have shown that extensive parts of Europe, the Mediterranean Basin, Asia Minor, southern Africa, eastern Asia, Australasia, and North and South America have high degrees of climatic suitability for invasion, suggesting that the fly could establish in these regions. The potential invasion range is expected to stay similar under different climate change scenarios. We discuss the status of A. borealis as an invasive species and measures that can be taken to reduce the risk of its introduction outside of North America. Our results highlight A. borealis as a potential threat to honey bee health worldwide that requires urgent attention of international veterinary bodies to prevent its spread.

Lateral Transmission of Yeast Symbionts Among Lucanid Beetle Taxa

To deepen understanding the evolutionary process of lucanid–yeast association, the lateral transmission process of yeast symbionts among stag beetle genera Platycerus and Prismognathus around the border between Japan and South Korea was estimated based on molecular analyses and species distribution modelings. Phylogenetic analyses were based on yeast ITS and IGS sequences and beetle COI sequences using Prismognathus dauricus from the Tsushima Islands and Pr. angularis from Kyushu, Japan, as well as other sequence data from our previous studies. The range overlap based on the species distribution model (SDM) and differentiation in ecological space were analyzed. Based on the IGS sequences, Clade II yeast symbionts were shared by Platycerus hongwonpyoi and Pr. dauricus in South Korea and the Tsushima Islands, and Platycerus viridicuprus in Japan. Clade III yeasts were shared by Pr. dauricus from the Tsushima Islands and Pr. angularis in Japan. During the Last Interglacial period when the land bridge between Japan and the Korean Peninsula existed, range overlap was predicted to occur between Pl. viridicuprus and Pr. dauricus in Kyushu and between Pr. dauricus and Pr. angularis in Kyushu and the Tsushima Islands. The ecological space of Pl. hongwonpyoi was differentiated from that of Pl. viridicuprus and Pr. angularis. We demonstrated the paleogeographical lateral transmission process of Scheffersomyces yeast symbionts among lucanid genera and species: putative transmission of yeasts from Pr. dauricus to Pl. viridicuprus in Kyushu and from Pr. angularis to Pr. dauricus in Kyushu or the Tsushima Islands. We also found that the yeast symbionts are likely being replaced in Pr. dauricus on the Tsushima Islands. We present novel estimates of the lateral transmission process of microbial symbionts based on phylogenetic, SDM and environmental analyses among lucanid beetles.

Community Confounding In Joint Species Distribution Models

Joint species distribution models have become ubiquitous for studying species-habitat relationships and dependence among species. Accounting for community structure often improves predictive power, but can also alter inference on species-habitat relationships. Modulated species-habitat relationships are indicative of community confounding: The situation in which interspecies dependence and habitat effects compete to explain species distributions. We discuss community confounding in a case study of mammalian responses to the Colorado bark beetle epidemic in the subalpine forest by comparing the inference from independent single species distribution models and a joint species distribution model. We present a method for measuring community confounding and develop a restricted version of our hierarchical model that orthogonalizes the habitat and species random effects. Our results indicate that variables associated with the severity and duration of the bark beetle epidemic suffer from community confounding. This implies that mammalian responses to the bark beetle epidemic are governed by interconnected habitat and community effects. Disentangling habitat and community effects can improve our understanding of the ecological system and possible management strategies. We evaluate restricted regression as a method for alleviating community confounding and distinguish it from other inferential methods for confounded models.

Updated range metrics and a global population estimate for the Critically Endangered Philippine Eagle using a spatial ensemble habitat model

Many range-restricted taxa are currently experiencing severe population declines yet lack fundamental biological information regarding distribution and population size. Establishing baseline estimates for both these key biological parameters is however critical for directing long-term monitoring and conservation planning for at-risk range-restricted species. The International Union for the Conservation of Nature (IUCN) Red List uses three spatial range metrics that define species distributions and inform extinction risk assessments: extent of occurrence (EOO), area of occupancy (AOO) and area of habitat (AOH). However, calculating all three metrics using standard IUCN approaches relies on a geographically representative sample of locations, which for rare species is often spatially biased. Here, we apply model-based interpolation using an ensemble Species Distribution Model (SDM), correlating occurrences with remote-sensing derived environmental covariates, to calculate IUCN range metrics and a global population estimate for the Critically Endangered Philippine Eagle (Pithecophaga jefferyi). Our ensemble averaged SDM had high predictive accuracy and was able to identify key areas of Philippine Eagle habitat across the species global range. We estimated an AOH = 49,426 km2 and from this metric calculated a maximum EOO = 609,697 km2 and a minimum EOO = 273,794 km2, with an AOO = 54,695 occupied cells. Based on inferred habitat from the AOH metric and territorial habitat area from home range estimates, we provide an updated global population estimate of 677 breeding pairs (range: 549-772 pairs), or 1354 mature individuals, across the entire Philippine Eagle range. We demonstrate that even when occurrence sampling is geographically biased, robust habitat models can be built which enable quantification of IUCN range metrics and a baseline population size estimate. In the absence of adequate location data for many rare and threatened taxa, our method is a promising spatial modelling tool with widespread applications, in particular for island endemics facing high extinction risk.

A distribution model for Glossina brevipalpis and Glossina austeni in Southern Mozambique, Eswatini and South Africa for enhanced area-wide integrated pest management approaches

Background Glossina austeni and Glossina brevipalpis (Diptera: Glossinidae) are the sole cyclical vectors of African trypanosomes in South Africa, Eswatini and southern Mozambique. These populations represent the southernmost distribution of tsetse flies on the African continent. Accurate knowledge of infested areas is a prerequisite to develop and implement efficient and cost-effective control strategies, and distribution models may reduce large-scale, extensive entomological surveys that are time consuming and expensive. The objective was to develop a MaxEnt species distribution model and habitat suitability maps for the southern tsetse belt of South Africa, Eswatini and southern Mozambique. Methodology/Principal findings The present study used existing entomological survey data of G. austeni and G. brevipalpis to develop a MaxEnt species distribution model and habitat suitability maps. Distribution models and a checkerboard analysis indicated an overlapping presence of the two species and the most suitable habitat for both species were protected areas and the coastal strip in KwaZulu-Natal Province, South Africa and Maputo Province, Mozambique. The predicted presence extents, to a small degree, into communal farming areas adjacent to the protected areas and coastline, especially in the Matutuíne District of Mozambique. The quality of the MaxEnt model was assessed using an independent data set and indicated good performance with high predictive power (AUC > 0.80 for both species). Conclusions/Significance The models indicated that cattle density, land surface temperature and protected areas, in relation with vegetation are the main factors contributing to the distribution of the two tsetse species in the area. Changes in the climate, agricultural practices and land-use have had a significant and rapid impact on tsetse abundance in the area. The model predicted low habitat suitability in the Gaza and Inhambane Provinces of Mozambique, i.e., the area north of the Matutuíne District. This might indicate that the southern tsetse population is isolated from the main tsetse belt in the north of Mozambique. The updated distribution models will be useful for planning tsetse and trypanosomosis interventions in the area.

Evaluation of species distribution models for estimating animal dark diversity

Aim Applying wide and effective sampling of animal communities is rarely possible due to the associated costs and the use of techniques that are not always efficient. Thus, many areas have a faunistic hidden diversity we denote Animal Dark Diversity (ADD), defined as the diversity that is present but not yet detected plus the diversity defined by Pärtel et al. (2011) that is not (yet) present despite the area’s favourable habitat conditions. We evaluated different species distribution model types (SDM techniques) on the basis of three requirements for ADD estimate reliability: 1) estimated spatial patterns of ADD do not differ significantly from other SDM techniques; 2) good predictive performances; and 3) low overfitting. Location Iberian Peninsula. Taxon Chiroptera and Noctuoidea (Lepidoptera) Methods We used distribution data for 25 species of bats and 352 species of moths. We evaluated eleven SDM techniques using biomod2 package implemented in the R software environment. We fitted the various SDM techniques to the data for each species and compared the resulting ADD estimates for the two animal groups under three threshold types. Results The results demonstrated that estimated ADD spatial patterns vary significantly between SDM techniques and depend on the threshold type. They also showed that SDM techniques with overfitting tend to generate smaller ADD sizes, thus reducing the possible species presence estimates. Among the SDMs studied, the ensemble models delivered ADD geographic patterns more like the other techniques while also presenting a high predictive performance for both faunal groups. However, the Ensemble Model Committee Average (ECA) performed much better on the sensitivity metric than all other techniques under any of the thresholds tested. In addition, ECA stood out clearly from the other ensemble model techniques in displaying low-medium overfitting. Main conclusions SDM techniques should no differ among each other in their ADD estimations, have good predictive performances and exhibit low overfitting. Furthermore, to reduce estimate uncertainty it is suggested that the threshold type be one that transforms high values of presences probabilities into binary information and furthermore that the SDM technique have a sensitivity bias, as otherwise the estimates will perform better for species absence in cases where it is not in fact known whether a species is truly absent.

Temperature Characteristics of Two Fomitiporia Fungi Determine Their Geographical Distributions in Japan

Two morphologically similar fungi, Fomitiporia torreyae and Fomitiporia punctata, are causal fungi of various tree diseases in Japan and are speculated to be distributed in different climatic zones. Clarifying their distribution ranges and climatic preferences would contribute to the prediction of disease occurrences and consideration of controls. In this study, we predicted the present geographical distributions of F. torreyae and F. punctata in Japan using a Maxent species distribution model to analyze our data and previously published collection records. In addition, we examined the importance of temperature on these predictions via jackknife analysis and evaluated the effects of temperature on mycelial growth and survival to elucidate determinants of their distribution. The predicted potential distributions showed that F. torreyae is mainly distributed in warmer areas compared to F. punctata. Jackknife analysis indicated the high importance of temperature variables for each fungal prediction. The two fungi were usually found at locations within upper or lower temperature limits for the growth and survival of each species. These results suggest that temperature is a key determinant of their distributions in Japan. This is the first report to predict fungal distribution based on species distribution modeling and evaluation of fungal physiological characteristics. This study indicates that the projected global warming will influence the future ranges of the two fungal species.