Toward a Monte Carlo approach to selecting climate variables in MaxEnt

Potentially suitable geographical area for Pulsatilla chinensis Regel under current and future climatic scenarios based on the MaxEnt model

Climate change has significantly impacted the distribution patterns of medicinal plants, highlighting the need for accurate models to predict future habitat shifts. In this study, the Maximum Entropy model to analyze the habitat distribution of Pulsatilla chinensis (Bunge) Regel under current conditions and two future climate scenarios (SSP245 and SSP585). Based on 105 occurrence records and 12 environmental variables, precipitation of the wettest quarter, isothermality, average November temperature, and the standard deviation of temperature seasonality were identified as key factors influencing the habitat suitability for P. chinensis. The reliability of the model was supported by a mean area under the curve (AUC) value of 0.916 and a True Skill Statistic (TSS) value of 0.608. The results indicated that although the total suitable habitat for P. chinensis expanded under both scenarios, the highly suitable area contracted significantly under SSP585 compared to SSP245. This suggests the importance of incorporating climate change considerations into P. chinensis management strategies to address potential challenges arising from future ecosystem dynamics.

Assessment of suitable habitat of Semen Armeniacae Amarum. in China under different climatic conditions by Internal Transcribed Spacer 2 and Maxent model

Semen Armeniacae Amarum is a Chinese medicine. The Chinese Pharmacopoeia stipulates that the dried ripe seeds of these four plants (Prunus armeniaca L. var. ansu Maxim., Prunus sibirica L., Prunus mandshurica (Maxim.) Koehne, and Prunus armeniaca L.) can all be used as Semen Armeniacae Amarum. Amygdalin is widely recognized as a key quality marker for standardizing Semen Armeniacae Amarum. It exhibits notable antitussive and antiasthmatic effects, and is believed to relieve cough by modulating the activity of the respiratory center. Its diverse pharmacological properties position it as a potential lead compound in drug discovery and the development of novel therapeutics. Climate change has a significant impact on distribution of the aforementioned species and the accumulation of their bioactive components. In this study, the distribution site information of all four plant species was collected through field surveys and online data surveys. Using the Internal Transcribed Spacer 2 (ITS2), the attribution of bitter almonds in each species from different geographical region was identified and the amygdalin content was measured. The maximum entropy model was coupled with the stepwise regression algorithm to evaluate the potential impact of future climate on the quality of amygdalin. The results showed that the 26 samples collected from different producing areas were all identified as PS. Under various Representative Concentration Pathway (RCP2.6, RCP4.5, and RCP8.5), the projected future distribution ranges of Prunus sibirica L. (PS) and Prunus armeniaca L. (PA) are predicted to contract, whereas the range of Prunus mandshurica (Maxim.) Koehne (PK) is projected to expand slightly. The distribution range of Prunus armeniaca L. var. ansu Maxim. (PM) is expected to either expand or contract, depending on specific scenarios and timeframes. Specifically, an expansion is projected under RCP2.6 in both the 2050s and 2070s, and under RCP8.5 in the 2050s. Conversely, a contraction is projected under RCP4.5 in the 2050s and 2070s, and under RCP8.5 in the 2070s. From the perspective of secondary metabolism, amygdalin content exhibits a strong positive correlation with temperature and precipitation. These findings provide valuable guidance for optimizing traditional medicine supply chains and formulating targeted conservation strategies for medicinal resources.

On further specimens of Dussumier's Mud Snake Dieurostus dussumierii (Duméril, Bibron & Duméril, 1854) with notes on its taxonomy, type material, and natural history (Reptilia: Serpentes: Homalopsidae)

We present new findings on Dussumier's Mud Snake Dieurostus dussumierii based on recent fieldwork conducted in and around Vembanad Lake (Kumarakom) in Kerala, Southwest India. We describe a series of 10 voucher specimens, eight females and two males, ranging from juveniles (207 mm) to adults (835 mm). We report new data on microhabitat associations, fossorial haunts, sympatric aquatic snakes (Fowlea cf. piscator, Cerberus rynchops), and intraspecific morphological variations in this species. We also illustrate and describe an overlooked, historical, non-type specimen of this species collected over a century ago. This work assembles the largest dataset of preserved voucher specimens used to characterize D. dussumierii, since its description 170 years ago.

Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science

Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for MaxEnt models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical MaxEnt models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth MaxEnt models. Our proposed first-order algorithms leverage the Kullback-Leibler divergence to train large-scale and non-smooth MaxEnt models efficiently. For MaxEnt models with discrete probability distribution of n elements built from samples, each containing m features, the stepsize parameter estimation and iterations in our algorithms scale on the order of O(mn) operations and can be trivially parallelized. Moreover, the strong ℓ1 convexity of the Kullback-Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the art by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.

Forest Orchids under Future Climate Scenarios: Habitat Suitability Modelling to Inform Conservation Strategies

Orchidaceae is one of the largest and most diverse families of flowering plants in the world but also one of the most threatened. Climate change is a global driver of plant distribution and may be the cause of their disappearance in some regions. Forest orchids are associated with specific biotic and abiotic environmental factors, that influence their local presence/absence. Changes in these conditions can lead to significant differences in species distribution. We studied three forest orchids belonging to different genera (Cephalanthera, Epipactis and Limodorum) for their potential current and future distribution in a protected area (PA) of the Northern Apennines. A Habitat Suitability Model was constructed for each species based on presence-only data and the Maximum Entropy algorithm (MaxEnt) was used for the modelling. Climatic, edaphic, topographic, anthropogenic and land cover variables were used as environmental predictors and processed in the model. The aim is to identify the environmental factors that most influence the current species distribution and the areas that are likely to contain habitats suitable for providing refuge for forest orchids and ensuring their survival under future scenarios. This will allow PA authorities to decide whether to invest more resources in conserving areas that are potential refuges for threatened species.

Distribution model and prediction of the tree fern Alsophila costularis Baker (Cyatheaceae) in China

Climatic change is a challenge for plant conservation due to plants' limited dispersal abilities. The survival and sustainable development of plants directly depend on the availability of suitable habitats. In this study, we employed an optimized MaxEnt model to evaluate the relative contribution of each environmental variable and predict the suitable habitat for Alsophila costularis under past, current, and future periods, which is an endangered relict tree fern known as a living fossil. For the Last Glacial Maximum (LGM) and Mid-Holocene scenarios, we adopted two atmosphere-ocean general circulation models: CCSM4 and MIROC-ESM. The BCC-CSM2-MR model was used for future projections. The results revealed that temperature annual range (Bio7) contributed most to the model construction with an optimal range of 13.74-22.44°C. Species distribution modeling showed that current suitable areas were mainly located in most areas of Yunnan, most areas of Hainan, most areas of Taiwan, southeastern Tibet, southwestern Guizhou, western Guangxi, southern Sichuan, and southern Guangdong, with an area of 35.90 × 104 km2. The suitable habitat area expanded northward in Yunnan from the Last Interglacial to the LGM under the CCSM4 model, while a significant contraction toward southwestern Yunnan was found under the MIROC-ESM model. Furthermore, the potential distributions during the Mid-Holocene were more widespread in Yunnan compared to those under current period. It is predicted that in the future, the range will significantly expand to northern Yunnan and western Guizhou. Almost all centroids of suitable habitats were distributed in southeastern Yunnan under different periods. The stable areas were located in southwestern Yunnan in all scenarios. The simulation results could provide a theoretical basis for the formulation of reasonable conservation and management measures to mitigate the effects of future climate change for A. costularis.

The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Modelling Red-Crowned Parrot (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) distributions in the Rio Grande Valley of Texas using elevation and vegetation indices and their derivatives

Texas Rio Grande Valley Red-crowned Parrots (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) primarily occupy vegetated urban rather than natural areas. We investigated the utility of raw vegetation indices and their derivatives as well as elevation in modelling the Red-crowned parrot's general use, nest site, and roost site habitat distributions. A feature selection algorithm was employed to create and select an ensemble of fine-scale, top-ranked MaxEnt models from optimally-sized, decorrelated subsets of four to seven of 199 potential variables. Variables were ranked post hoc by frequency of appearance and mean permutation importance in top-ranked models. Our ensemble models accurately predicted the three distributions of interest ([Formula: see text] Area Under the Curve [AUC] = 0.904-0.969). Top-ranked variables for different habitat distribution models included: (a) general use-percent cover of preferred ranges of entropy texture of Normalized Difference Vegetation Index (NDVI) values, entropy and contrast textures of NDVI, and elevation; (b) nest site-entropy textures of NDVI and Green-Blue NDVI, and percent cover of preferred range of entropy texture of NDVI values; (c) roost site-percent cover of preferred ranges of entropy texture of NDVI values, contrast texture of NDVI, and entropy texture of Green-Red Normalized Difference Index. Texas Rio Grande Valley Red-crowned Parrot presence was associated with urban areas with high heterogeneity and randomness in the distribution of vegetation and/or its characteristics (e.g., arrangement, type, structure). Maintaining existing preferred vegetation types and incorporating them into new developments should support the persistence of Red-crowned Parrots in southern Texas.

Effects of climate-change scenarios on the distribution patterns of Castanea henryi

Castanea henryi, with edible nuts and timber value, is a key tree species playing essential roles in China's subtropical forest ecosystems. However, natural and human perturbations have nearly depleted its wild populations. The study identified the dominant environmental variables enabling and limiting its distribution and predicted its suitable habitats and distribution. The 212 occurrence records covering the whole distribution range of C. henryi in China and nine main bioclimatic variables were selected for detailed analysis. We applied the maximum entropy model (MaxEnt) and QGIS to predict potentially suitable habitats under the current and four future climate-change scenarios. The limiting factors for distribution were accessed by Jackknife, percent contribution, and permutation importance. We found that the current distribution areas were concentrated in the typical subtropical zone, mainly Central and South China provinces. The modeling results indicated temperature as the critical determinant of distribution patterns, including mean temperature of the coldest quarter, isothermality, and mean diurnal range. Winter low temperature imposed an effective constraint on its spread. Moisture served as a secondary factor in species distribution, involving precipitation seasonality and annual precipitation. Under future climate-change scenarios, excellent habitats would expand and shift northwards, whereas range contraction would occur on the southern edge. Extreme climate change could bring notable range shrinkage. This study provided a basis for protecting the species' germplasm resources. The findings could guide the management, cultivation, and conservation of C. henryi, assisted by a proposed three-domain operation framework: preservation areas, loss areas, and new areas, each to be implemented using tailor-made strategies.

Habitat distribution change of commercial species in the Adriatic Sea during the COVID-19 pandemic

The COVID-19 pandemic has led to reduced anthropogenic pressure on ecosystems in several world areas, but resulting ecosystem responses in these areas have not been investigated. This paper presents an approach to make quick assessments of potential habitat changes in 2020 of eight marine species of commercial importance in the Adriatic Sea. Measurements from floating probes are interpolated through an advection-equation based model. The resulting distributions are then combined with species observations through an ecological niche model to estimate habitat distributions in the past years (2015–2018) at 0.1° spatial resolution. Habitat patterns over 2019 and 2020 are then extracted and explained in terms of specific environmental parameter changes. These changes are finally assessed for their potential dependency on climate change patterns and anthropogenic pressure change due to the pandemic. Our results demonstrate that the combined effect of climate change and the pandemic could have heterogeneous effects on habitat distributions: three species (Squilla mantis, Engraulis encrasicolus, and Solea solea) did not show significant niche distribution change; habitat suitability positively changed for Sepia officinalis, but negatively for Parapenaeus longirostris, due to increased temperature and decreasing dissolved oxygen (in the Adriatic) generally correlated with climate change; the combination of these trends with an average decrease in chlorophyll, probably due to the pandemic, extended the habitat distributions of Merluccius merluccius and Mullus barbatus but reduced Sardina pilchardus distribution. Although our results are based on approximated data and reliable at a macroscopic level, we present a very early insight of modifications that will possibly be observed years after the end of the pandemic when complete data will be available. Our approach is entirely based on Findable, Accessible, Interoperable, and Reusable (FAIR) data and is general enough to be used for other species and areas.

Automatic variable selection in ecological niche modeling: A case study using Cassin's Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max's Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin's Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim's Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species' bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA's Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.