Understanding the connections between species distribution models for presence-background data

Predicting the potential suitable habitats of genus Nymphaea in India using MaxEnt modeling

Modeling and mapping the distribution of suitable habitats of aquatic plants are critical for assessing the impact of factors like changing climate on species habitat range shifts, declines, and expansions. Nymphaea is an aquatic perennial herb considered valuable because of its ornamental, economic, medicinal, and ecological importance. In India, the geographical distribution of Nymphaea is diverse, and the suitable habitats of individual species are vulnerable to the changing climate and global warming effects. Despite its increased vulnerability, only a few limited conservation efforts in aquatic environments are being made to date. In several places, the distribution of Nymphaea has been impacted by both anthropogenic and climate-related disturbances. A comprehensive strategy will be needed to meet the socio-ecological challenge of Nymphaea conservation. In this study, we employed maximum entropy (MaxEnt) method to assess how climate change affects the distribution of Nymphaea suitable habitat. The occurrence records of Nymphaea were collected from primary surveys, Global Biodiversity Information Facility (GBIF), and published works. Bioclimatic variables obtained from the Coupled Model Intercomparison Project (CMIP6) were employed as predictor variables in distribution modeling. The projections were made using three SSPs (stringent mitigation scenarios) for the future period of 2050. Our results showed shifts in the suitability ranges of Nymphaea under different projection scenarios. The study provides information about the distribution of suitable habitats for Nymphaea in India, which may be helpful for ongoing efforts to conserve and manage the aquatic plants, particularly in areas that are losing suitable climate conditions.

Integration of phlebotomine ecological niche modelling, and mapping of cutaneous leishmaniasis surveillance data, to identify areas at risk of under-estimation

INTRODUCTION

Passive surveillance systems are thought to under-estimate the true incidence of American cutaneous leishmaniasis (ACL) by two- to five-fold. Ecological niche models based on remotely sensed data can identify environmental factors which favor phlebotomine vectors. Here we report an integrated approach to identifying areas at risk of cutaneous leishmaniasis by applying spatial analysis methods to niche model results, and local surveillance data, in two locations in Colombia with differing vector ecology. The objective was to identify townships in which later phases of the project could implement community-based surveillance to obtain direct estimates of under-reporting.

MATERIALS AND METHODS

The study was carried out in one municipality in each of two departments of the Andean region of Colombia: Pueblo Rico in Risaralda, and Rovira in Tolima. Niche mapping by maximum entropy, based on published and unpublished existing locations of Pintomyia (Pifanomyia) longiflocosa and Psychodopygus panamensis, and using variables on land cover, climate and elevation. Field catches were done in each municipality to test predictions of high relative probability of presence. The niche model results were included as a predictor in a conditional autoregressive spatial model, in which the outcome variable was the number of cases by township, as detected by passive surveillance.

RESULTS

Having rarefied 173 geolocated records, 46 of Pi. longiflocosa and 57 of Ps. panamensis were used for the niche modelling. At the national level, both species had high relative probability of presence on parts of the slopes of the three Andean cordilleras. Pi. longiflocosa also has a high relative probability of presence in the higher parts of the Magdalena valley, as does Ps. panamensis in some areas close to the Caribbean coast. At the local level, field catches confirmed that Pi. longiflocosa was the most abundant species in Rovira, and likewise Ps. panamensis in Pueblo Rico. The spatial regression showed that the incidence of ACL, according to surveillance, was positively, but not statistically significantly, associated with the relative probability of presence from the risk model.

CONCLUSIONS

These niche maps bring together published and unpublished results on phlebotomine species which are important vectors in Colombia. Maps of the fitted values of incidence were used to guide the selection of townships in which further phases of the study will attempt to quantify the extent of under-estimation of ACL incidence.

Kernel density estimation in mixture models with known mixture proportions

In this article, we consider the density estimation for data with a mixture structure, where the component densities are assumed unknown, but for each observation, the probabilities of its membership to the subpopulations are known or estimable from other resources. Data of this kind arise from practice and have wide applications. Motivated from the classical kernel density estimation method for a single population, we propose a weighted kernel density estimation method to estimate the component density functions nonparametrically. Within the framework of the EM algorithm, we derive an algorithm that computes our proposed estimates effectively. Via extensive simulation studies, we demonstrate that our methods outperform the existing methods in most occasions. We further compare our methods with existing methods by real data examples.

Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

Landscape does matter: Disentangling founder effects from natural and human-aided post-introduction dispersal during an ongoing biological invasion

Environmental features impacting the spread of invasive species after introduction can be assessed using population genetic structure as a quantitative estimation of effective dispersal at the landscape scale. However, in the case of an ongoing biological invasion, deciphering whether genetic structure represents landscape connectivity or founder effects is particularly challenging. We examined the modes of dispersal (natural and human-aided) and the factors (landscape or founders history) shaping genetic structure in range edge invasive populations of the Asian tiger mosquito, Aedes albopictus, in the region of Grenoble (Southeast France). Based on detailed occupancy-detection data and environmental variables (climatic, topographic and land-cover), we modelled A. albopictus potential suitable area and its expansion history since first introduction. The relative role of dispersal modes was estimated using biological dispersal capabilities and landscape genetics approaches using genome-wide SNP dataset. We demonstrate that both natural and human-aided dispersal have promoted the expansion of populations. Populations in diffuse urban areas, representing highly suitable habitat for A. albopictus, tend to disperse less, while roads facilitate long-distance dispersal. Yet, demographic bottlenecks during introduction played a major role in shaping the genetic variability of these range edge populations. The present study is one of the few investigating the role of founder effects and ongoing expansion processes in shaping spatial patterns of genetic variation in an invasive species at the landscape scale. The combination of several dispersal modes and large proportions of continuous suitable habitats for A. albopictus promoted range filling of almost its entire potential distribution in the region of Grenoble only few years after introduction.