Habitat Suitability Index (HSI) derived from Species Distribution Model (SDM) has been used to infer or predict local demographic properties such as abundance for many species. Across species studied, HSI has either been presented as a poor predictor of abundance or as a predictor of potential rather than realized abundance. The main explanation of the lack of relationship between HSI and abundance is that the local abundance of a species varies in time due to various ecological processes that are not integrated into correlative SDM. To better understand the HSI-abundance relationship, in addition to the study of the association between HSI and mean abundance, we explored its variation over time. We used data from 10-years monitoring of a Houbara bustard (Chlamydotis undulata undulata) population in Morocco. From various occurrence data we modelled the HSI. From (independent) count data we calculated four local abundance indices: mean abundance, maximum abundance, the temporal trend of abundance and the coefficient of variation of abundance over the study period. We explored the relationship between HSI and abundance indices using linear, polynomial and quantile regressions. We found a triangular relationship between local abundance (mean and maximum) and HSI, indicating that the upper limit of mean and maximum abundance increased with HSI. Our results also indicate that sites with the highest HSI were associated with least variation in local abundance, the highest variation being observed at intermediate HSI. Our results provide new empirical evidence supporting the generalization of the triangular relationship between HSI and abundance. Overall, our results support the hypothesis that HSI obtained from SDMs can reflect the local abundance potentialities of a species and emphasize the importance of investigating this relationship using temporal variation in abundance.
Large brown macroalgae are foundational threatened species in coastal ecosystems from the subtropical northeastern Atlantic, where they have exhibited a drastic decline in recent years. This study describes the potential habitat of Gongolaria abies-marina, its current distribution and conservation status, and the major drivers of population decline. The results show a strong reduction of more than 97% of G. abies-marina populations in the last thirty years and highlight the effects of drivers vary in terms of spatial heterogeneity. A decrease in the frequency of high waves and high human footprint are the principal factors accounting for the long-term decline in G. abies-marina populations. UV radiation and sea surface temperature have an important correlation only in certain locations. Both the methodology and the large amount of data analyzed in this study provide a valuable tool for the conservation and restoration of threatened macroalgae.
Although density-dependent processes and their impacts on population dynamics are key issues in ecology and conservation biology, empirical evidence of density-dependence remains scarce for species or populations with low densities, scattered distributions, and especially for managed populations where densities may vary as a result of extrinsic factors (such as harvesting or releases). Here, we explore the presence of density-dependent processes in a reinforced population of North African Houbara bustard (Chlamydotis undulata undulata). We investigated the relationship between reproductive success and local density, and the possible variation of this relationship according to habitat suitability using three independent datasets. Based on eight years of nests monitoring (more than 7000 nests), we modeled the Daily Nest Survival Rate (DNSR) as a proxy of reproductive success. Our results indicate that DNSR was negatively impacted by local densities and that this relationship was approximately constant in space and time: (1) although DNSR strongly decreased over the breeding season, the negative relationship between DNSR and density remained constant over the breeding season; (2) this density-dependent relationship did not vary with the quality of the habitat associated with the nest location. Previous studies have shown that the demographic parameters and population dynamics of the reinforced North African Houbara bustard are strongly influenced by extrinsic environmental and management parameters. Our study further indicates the existence of density-dependent regulation in a low-density, managed population.
Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions.
Araújo et al. have published a response to our piece ‘Species distribution models are inappropriate for COVID-19’1 entitled ‘Ecological and epidemiological models are both useful for SARS-CoV-2’2, in which they defend the idea that ecological models are likely to identify the signature of climate drivers in the R0 of COVID-19 transmission.
Species distribution models are a powerful tool for ecological inference, but not every use is biologically justified. Applying these tools to the COVID-19 pandemic is unlikely to yield new insights, and could mislead policymakers at a critical moment.
The ongoing pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing significant damage to public health and economic livelihoods, and is putting significant strains on healthcare services globally. This unfolding emergency has prompted the preparation and dissemination of the article “Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate” by Araújo and Naimi (2020). The authors present the results of an ensemble forecast made from a suite of species distribution models (SDMs), where they attempt to predict the suitability of the climate for the spread of SARS-CoV-2 over the coming months. They argue that climate is likely to be a primary regulator for the spread of the infection and that people in warm-temperate and cold climates are more vulnerable than those in tropical and arid climates. A central finding of their study is that the possibility of a synchronous global pandemic of SARS-CoV-2 is unlikely. Whilst we understand that the motivations behind producing such work are grounded in trying to be helpful, we demonstrate here that there are clear conceptual and methodological deficiencies with their study that render their results and conclusions invalid.
The goals of this study are to provide a map of actual habitat suitability (1), describe the relationships between abiotic predictors and the saguaro distribution at regional extents (2), and describe the potential effect of climate change on the spatial distribution of the saguaro (3).
Here we investigated the demographic history of the willow grouse (Lagopus lagopus), rock ptarmigan (Lagopus muta), and black grouse (Tetrao tetrix) through the Late Pleistocene using two complementary methods and whole genome data. Species distribution modeling (SDM) allowed us to estimate the total range size during the Last Interglacial (LIG) and Last Glacial Maximum (LGM) as well as to indicate potential population subdivisions.