The ecological niche of Echinococcus multilocularis in North America: understanding biotic and abiotic determinants of parasite distribution with new records in New Mexico and Maryland, United States

Abstract

Understanding the factors shaping the niche of parasites and its expression over geographical space and through time continues to be a modern scientific challenge with the results of research in this area directly influencing both theoretical and applied biology. This is especially important for proactive management of zoonotic parasites such as Echinococcus multilocularis, the etiologic agent of alveolar echinococcosis. Echinococcus multilocularis has a Holarctic distribution; with its geographic range and prevalence increasing recently in areas of the western Palearctic, while its distribution dynamics are poorly understood in the Nearctic. In this paper, we use an ecological niche modeling (ENM) approach to: i) estimate the current spatial distribution of suitable conditions for the parasite in the Nearctic. ii) Evaluate the abiotic and biotic factors influencing the species distribution. iii) Assess the potential impact of climatic change on the distribution of this species in the Nearctic. Additionally, we report two new occurrence records of this parasite that significantly expands its known geographic range. We reviewed the occurrence records of E. multilocularis for the Nearctic. This was complemented by two new records of the species from Maryland and New Mexico identified using morphology and multivariate morphometrics of the rostellar hooks. From these data we created two ENMs using the software Maxent. The first ENM included climatic variables, while the second included the same abiotic data plus biotic information consisting of four host community-related data sets. We evaluated model performance and variable importance to explore the relation of these variables to the parasite niche. Finally, we projected the resulting niche model onto future climate change scenarios. We found that an important portion of the Nearctic has suitable conditions for E. multilocularis with adequate habitat in the West and East of the continent where the parasite has not been detected. We also found that the proposed biotic variables improve the model performance and provide unique information, while the most critical abiotic variable was related to the amount of solar radiation. Finally, under the future emission scenarios explored, the distribution of suitable habitat for the parasite is predicted to increase by 56 % to 76 %. We obtained a robust model that provides detail on the distribution of suitable areas for E. multilocularis, including areas that have not been explored for the presence of the parasite. The host community variables included in this study seem a promising way to include biotic data for ecological parasite niche modeling.El estudio de los factores que moldean el nicho de los parásitos y como este se expresa en la distribución espacial y temporal de estos organismos es un reto de importancia para la biología aplicada y teórica. Esta información puede ser de especial importancia para parásitos zoonóticos tales como Echinococcus multilocularis, el cestodo causante de echinococcosis alveolar. Este paras...

Multiscale ecological drivers of Echinococcus multilocularis spatial distribution in wild hosts: A systematic review

“…Positive (+ corr) and negative (− corr) correlations of ecological drivers with the occurrence, prevalence or intensity of infection with E. multilocularis are reported, together with asymptotic, unimodal or other (Assoc) relationships. Numbers indicate the following references: 1 Staubach et al, 2001 , 2 Stieger et al, 2002 , 3 Hansen et al, 2004 , 4 Pleydell et al, 2004 , 5 Miterpakova et al, 2006 , 6 Fuglei et al, 2008 , 7 Casulli et al, 2010 , 8 Raoul et al, 2010 , 9 Stien et al, 2010 , 10 Burlet et al, 2011 , 11 Robardet et al, 2011 , 12 Tolnai et al, 2013 , 13 Guerra et al, 2014 , 14 Takeuchi-Storm et al, 2015 , 15 Miller et al, 2017 , 16 Otero-Abad et al, 2017 , 17 Bastien et al, 2018 , 18 Botero-Cañola et al, 2019 , 19 Mori et al, 2019 , 20 Kotwa et al, 2020 , 21 Avcioglu et al, 2021 , 22 Cenni et al, 2023 , 23 Mori et al, 2023 . The spatial scale(s) of the studies that report an association with a given factor is/are also reported.…”

“…Percentage values were then obtained by normalization. Variable Botero-Cañola et al, 2019 Cenni et al, 2023 Average daily solar radiation 7.2 – Precipitation of wettest month 0.6 2.5 Precipitation of driest month 0.2 – Precipitation seasonality – 6.8 Max temperature of warmest month 9.4 – Mean temperature of coldest quarter – 59.2 Temperature annual range 1.0 1.5 Precipitation seasonality 3.7 – Definitive host proportion 22.0 – Definitive host richness 34.3 – Intermediate host richness 17.4 – Intermediate host proportion 4.3 – Forest cover – 11.0 Urban cover – 8.5 Open areas – 3.5 Roughness – 6.6 Topographic Position Index – 0.3

Fig. 2 Ecological drivers of Echinococcus multilocularis spatial distribution in wild hosts across multiple spatial scales: (A) continental, (B) country, (C) regional, (D) local.

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“…A negative relationship between the driver and prevalence in definitive hosts was found for average precipitation of the wettest month, average precipitation of the driest month and average maximum daily temperature of the warmest month, whereas a positive relationship was detected for average annual precipitation. When modelling the distribution of E. multilocularis at a continental scale in North America, Botero-Cañola et al, 2019 , found that average daily solar radiation and the maximum temperature of the warmest month were the two most relevant predictors according to the jackknife test. Solar radiation was related to the probability of occurrence with a peak of suitability for the parasite at intermediate levels, whereas a positive relationship with the maximum temperature of the warmest month was detected ( Botero-Cañola et al, 2019 ).…”

How climate, landscape, and economic changes increase the exposure of Echinococcus Spp.

“…There are several limitations to our study including the lack of future land cover types involved, which is inherent limitations of MaxEnt predicts. One is the assumption that the parasite niche will not evolve, and the other is the lack of wildlife host distribution data, but the host distribution will inevitably redistribute with climate change [ 18 ]. Nevertheless, it still has positive predictive significance.…”

“…Environmental niche models (ENMs) are powerful tools that can enhance the insight of the potential and actual distribution of species in question, as well as the way in which environmental factors may influence a species [ 17 ]. Furthermore, these models can be applied to forecast the influences of motivations of global environmental change, such as climatic or landcover alterations on the distribution of parasites [ 16 , 18 ]. Hence, integrating information about pure and projected environmental variations might allow us to prioritize inspecting activities to exploit the possibility of expecting the advent of pathogens, specifical mediators of endemic, emergent, and re-emergent zoonotic diseases in novel areas [ 18 ] .…”