Ciência habilitada por dados de espécimes

Renjana, E., E. R. Firdiana, M. H. Angio, L. W. Ningrum, I. Q. Lailaty, A. Rahadiantoro, I. Martiansyah, et al. 2024. Spatial habitat suitability prediction of essential oil wild plants on Indonesia’s degraded lands. PeerJ 12: e17210. https://doi.org/10.7717/peerj.17210

Background Essential oils are natural products of aromatic plants with numerous uses. Essential oils have been traded worldwide and utilized in various industries. Indonesia is the sixth largest essential oil producing country, but land degradation is a risk to the continuing extraction and utilization of natural products. Production of essential oil plants on degraded lands is a potential strategy to mitigate this risk. This study aimed to identify degraded lands in Indonesia that could be suitable habitats for five wild native essential oil producing plants, namely Acronychia pedunculata (L.) Miq., Baeckea frutescens L., Cynometra cauliflora L., Magnolia montana (Blume) Figlar, and Magnolia sumatrana var. glauca (Blume) Figlar & Noot using various species distribution models. Methods The habitat suitability of these species was predicted by comparing ten species distribution models, including Bioclim, classification and regression trees (CART), flexible discriminant analysis (FDA), Maxlike, boosted regression trees (BRT), multivariate adaptive regression splines (MARS), generalized linear models (GLM), Ranger, support vector machine (SVM), and Random Forests (RF). Bioclimatic, topographic and soil variables were used as the predictors of the model habitat suitability. The models were evaluated according to their AUC and TSS metrics. Model selection was based on ranking performance. The total suitable area for five native essential oil producing plants in Indonesia’s degraded lands was derived by overlaying the models with degraded land locations. Results The habitat suitability model for these species was well predicted with an AUC value >0.8 and a TSS value >0.7. The most important predictor variables affecting the habitat suitability of these species are mean temperature of wettest quarter, precipitation seasonality, precipitation of warmest quarter, precipitation of coldest quarter, cation exchange capacity, nitrogen, sand, and soil organic carbon. C. cauliflora has the largest predicted suitable area, followed by M. montana, B. frutescens, M. sumatrana var. glauca, and A. pedunculata. The overlapping area between predictive habitat suitability and degraded lands indicates that the majority of degraded lands in Indonesia’s forest areas are suitable for those species. Conclusion The degraded lands predicted as suitable habitats for five native essential oil producing plants were widely spread throughout Indonesia, mostly in its main islands. These findings can be used by the Indonesian Government for evaluating policies for degraded land utilization and restorations that can enhance the lands’ productivity.

Ramírez-Barahona, S. 2024. Incorporating fossils into the joint inference of phylogeny and biogeography of the tree fern order Cyatheales R. Warnock, and M. Zelditch [eds.],. Evolution. https://doi.org/10.1093/evolut/qpae034

Present-day geographic and phylogenetic patterns often reflect the geological and climatic history of the planet. Neontological distribution data are often sufficient to unravel a lineage’s biogeographic history, yet ancestral range inferences can be at odds with fossil evidence. Here, I use the fossilized birth–death process and the dispersal–extinction cladogenesis model to jointly infer the dated phylogeny and range evolution of the tree fern order Cyatheales. I use data for 101 fossil and 442 extant tree ferns to reconstruct the biogeographic history of the group over the last 220 million years. Fossil-aware reconstructions evince a prolonged occupancy of Laurasia over the Triassic–Cretaceous by Cyathealean tree ferns, which is evident in the fossil record but hidden from analyses relying on neontological data alone. Nonetheless, fossil-aware reconstructions are affected by uncertainty in fossils’ phylogenetic placement, taphonomic biases, and specimen sampling and are sensitive to interpretation of paleodistributions and how these are scored. The present results highlight the need and challenges of incorporating fossils into joint inferences of phylogeny and biogeography to improve the reliability of ancestral geographic range estimation.

Minghetti, E., P. M. Dellapé, M. Maestro, and S. I. Montemayor. 2024. Evaluating the climatic suitability of Engytatus passionarius Minghetti et al. (Heteroptera, Miridae) as a biological control agent of the invasive stinking passion flower Passiflora foetida L. in Australia through ecological niche models. Biological Control 191: 105461. https://doi.org/10.1016/j.biocontrol.2024.105461

Passiflora foetida is a climbing vine, native to the Neotropical Region that is causing major economic and ecological damage in Australia, where it is rapidly spreading. Traditional control options, such as cutting, manual uprooting, and herbicide applications are only effective for local management. Currently, the plant bug Engytatus passionarius is the most promising biological control agent. Specificity tests performed in its native range in Argentina suggest it is highly specific to the plant, and it has not been observed in the field associated with other plants. As climate determines the establishment of insects, knowing if the environmental conditions suit their requirements is key to introducing a species in a region. Also, an overlap between the climatic niches of species is an indicator of similar requirements. To explore the possibilities of a successful establishment of E. passionarius in Australia, ecological niche models (ENM) were built for the plant bug and for the vine and their overlap was measured. The ENM projected to Australia recognized suitable environmental conditions for the establishment of E. passionarius in several regions where P. foetida is present, both for current and future scenarios. Moreover, the niche of the plant bug is almost completely overlapped with that of the vine. All the aforementioned evidence seems to indicate that E. passionarius has a good chance to become an effective biological control agent of P. foetida.

Ract, C., N. D. Burgess, L. Dinesen, P. Sumbi, I. Malugu, J. Latham, L. Anderson, et al. 2024. Nature Forest Reserves in Tanzania and their importance for conservation S. S. Romanach [ed.],. PLOS ONE 19: e0281408. https://doi.org/10.1371/journal.pone.0281408

Since 1997 Tanzania has undertaken a process to identify and declare a network of Nature Forest Reserves (NFRs) with high biodiversity values, from within its existing portfolio of national Forest Reserves, with 16 new NFRs declared since 2015. The current network of 22 gazetted NFRs covered 948,871 hectares in 2023. NFRs now cover a range of Tanzanian habitat types, including all main forest types—wet, seasonal, and dry—as well as wetlands and grasslands. NFRs contain at least 178 of Tanzania’s 242 endemic vertebrate species, of which at least 50% are threatened with extinction, and 553 Tanzanian endemic plant taxa (species, subspecies, and varieties), of which at least 50% are threatened. NFRs also support 41 single-site endemic vertebrate species and 76 single-site endemic plant taxa. Time series analysis of management effectiveness tracking tool (METT) data shows that NFR management effectiveness is increasing, especially where donor funds have been available. Improved management and investment have resulted in measurable reductions of some critical threats in NFRs. Still, ongoing challenges remain to fully contain issues of illegal logging, charcoal production, firewood, pole-cutting, illegal hunting and snaring of birds and mammals, fire, wildlife trade, and the unpredictable impacts of climate change. Increased tourism, diversified revenue generation and investment schemes, involving communities in management, and stepping up control measures for remaining threats are all required to create a network of economically self-sustaining NFRs able to conserve critical biodiversity values.

Munna, A. H., N. A. Amuri, P. Hieronimo, and D. A. Woiso. 2023. Modelling ecological niches of Sclerocarya birrea subspecies in Tanzania under the current and future climates. Silva Fennica 57. https://doi.org/10.14214/sf.23009

The information on ecological niches of the Marula tree, Sclerocarya birrea (A. Rich.) Horchst. subspecies are needed for sustainable management of this tree, considering its nutritional, economic, and ecological benefits. However, despite Tanzania being regarded as a global genetic center of diversity of S. birrea, information on the subspecies ecological niches is lacking. We aimed to model ecological niches of S. birrea subspecies in Tanzania under the current and future climates. Ecological niches under the current climate were modelled by using ecological niche models in MaxEnt using climatic, edaphic, and topographical variables, and subspecies occurrence data. The Hadley Climate Center and National Center for Atmospheric Research's Earth System Models were used to predict ecological niches under the medium and high greenhouse gases emission scenarios for the years 2050 and 2080. Area under the curves (AUCs) were used to assess the accuracy of the models. The results show that the models were robust, with AUCs of 0.85–0.95. Annual and seasonal precipitation, elevation, and soil cation exchange capacity are the key environmental factors that define the ecological niches of the S. birrea subspecies. Ecological niches of subsp. caffra, multifoliata, and birrea are currently found in 30, 22, and 21 regions, and occupy 184 814 km2, 139 918 km2, and 28 446 km2 of Tanzania's land area respectively, which will contract by 0.4–44% due to climate change. Currently, 31–51% of ecological niches are under Tanzania’s protected areas network. The findings are important in guiding the development of conservation and domestication strategies for the S. birrea subspecies in Tanzania.

Zhang, H., W. Guo, and W. Wang. 2023. The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models. Ecology and Evolution 13. https://doi.org/10.1002/ece3.10747

How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.

Rodríguez-Merino, A. 2023. Identifying and Managing Areas under Threat in the Iberian Peninsula: An Invasion Risk Atlas for Non-Native Aquatic Plant Species as a Potential Tool. Plants 12: 3069. https://doi.org/10.3390/plants12173069

Predicting the likelihood that non-native species will be introduced into new areas remains one of conservation’s greatest challenges and, consequently, it is necessary to adopt adequate management measures to mitigate the effects of future biological invasions. At present, not much information is available on the areas in which non-native aquatic plant species could establish themselves in the Iberian Peninsula. Species distribution models were used to predict the potential invasion risk of (1) non-native aquatic plant species already established in the peninsula (32 species) and (2) those with the potential to invade the peninsula (40 species). The results revealed that the Iberian Peninsula contains a number of areas capable of hosting non-native aquatic plant species. Areas under anthropogenic pressure are at the greatest risk of invasion, and the variable most related to invasion risk is temperature. The results of this work were used to create the Invasion Risk Atlas for Alien Aquatic Plants in the Iberian Peninsula, a novel online resource that provides information about the potential distribution of non-native aquatic plant species. The atlas and this article are intended to serve as reference tools for the development of public policies, management regimes, and control strategies aimed at the prevention, mitigation, and eradication of non-native aquatic plant species.

McCulloch-Jones, E. J., T. Kraaij, N. Crouch, and K. T. Faulkner. 2023. Assessing the invasion risk of traded alien ferns using species distribution models. NeoBiota 87: 161–189. https://doi.org/10.3897/neobiota.87.101104

Risk analysis plays a crucial role in regulating and managing alien and invasive species but can be time-consuming and costly. Alternatively, combining invasion and impact history with species distribution models offers a cost-effective and time-efficient approach to assess invasion risk and identify species for which a comprehensive risk analysis should take precedence. We conducted such an assessment for six traded alien fern species, determining their invasion risk in countries where they are traded. Four of the species (Dicksonia antarctica, Dryopteris erythrosora, Lygodium japonicum, and Phlebodium aureum) showed limited global distributions, while Adiantum raddianum and Sphaeropteris cooperi had broader distributions. A. raddianum, however, was the only species found to pose a high invasion risk in two known trade countries – the USA and Australia – and requires a complete risk analysis to determine the appropriate regulatory responses. Dicksonia antarctica, Phlebodium aureum (for New Zealand), and Dryopteris erythrosora (for the USA) posed a medium risk of invasion due to the lack of evidence of impacts, and a complete risk analysis is thus deemed less crucial for these species in these countries. For other species, suitable environments were not predicted in the countries where they are traded, thus the risk of invasion is low, and a complete risk analysis is not required. For species in countries where suitable environments are predicted but no trade information or presence data are available, risk assessments are recommended to better determine the risk posed. Despite the relatively limited potential global distribution of the studied ferns relative to other major plant invaders (e.g., Pinus spp. and Acacia spp.), their history of invasion, documented impacts in pristine environments, and high propagule pressure from trade warrants concern, possibly necessitating legislative and regulatory measures in environmentally suitable regions.

Pang, S. E. H., J. W. F. Slik, D. Zurell, and E. L. Webb. 2023. The clustering of spatially associated species unravels patterns in tropical tree species distributions. Ecosphere 14. https://doi.org/10.1002/ecs2.4589

Complex distribution data can be summarized by grouping species with similar or overlapping distributions to unravel spatial patterns and separate trends (e.g., of habitat loss) among spatially unique groups. However, such classifications are often heuristic, lacking the transparency, objectivity, and data‐driven rigor of quantitative methods, which limits their interpretability and utility. Here, we develop and illustrate the clustering of spatially associated species, a methodological framework aimed at statistically classifying species using explicit measures of interspecific spatial association. We investigate several association indices and clustering algorithms and show how these methodological choices drive substantial variations in clustering outcomes and performance. To facilitate robust decision‐making, we provide guidance on choosing methods appropriate to one's study objective(s). As a case study, we apply our framework to modeled tree distributions in Borneo and subsequently evaluate the impact of land‐cover change on separate species groupings. Based on the modeled distribution of 390 tree species prior to anthropogenic land‐cover changes, we identified 11 distinct clusters that unraveled ecologically meaningful patterns in Bornean tree distributions. These clusters then enabled us to quantify trends of habitat loss tied to each of those specific clusters, allowing us to discern particularly vulnerable species clusters and their distributions. This study demonstrates the advantages of adopting quantitatively derived clusters of spatially associated species and elucidates the potential of resultant clusters as a spatially explicit framework for investigating distribution‐related questions in ecology, biogeography, and conservation. By adopting our methodological framework and publicly available codes, practitioners can leverage the ever‐growing abundance of distribution data to better understand complex spatial patterns among species distributions and the disparate effects of global changes on biodiversity.

Wilf, P., and R. M. Kooyman. 2023. Do Southeast Asia’s paleo‐Antarctic trees cool the planet? New Phytologist. https://doi.org/10.1111/nph.19067

Many tree genera in the Malesian uplands have Southern Hemisphere origins, often supported by austral fossil records. Weathering the vast bedrock exposures in the everwet Malesian tropics may have consumed sufficient atmospheric CO2 to contribute significantly to global cooling over the past 15 Myr. However, there has been no discussion of how the distinctive regional tree assemblages may have enhanced weathering and contributed to this process. We postulate that Gondwanan‐sourced tree lineages that can dominate higher‐elevation forests played an overlooked role in the Neogene CO2 drawdown that led to the Ice Ages and the current, now‐precarious climate state. Moreover, several historically abundant conifers in Araucariaceae and Podocarpaceae are likely to have made an outsized contribution through soil acidification that increases weathering. If the widespread destruction of Malesian lowland forests continues to spread into the uplands, the losses will threaten unique austral plant assemblages and, if our hypothesis is correct, a carbon sequestration engine that could contribute to cooler planetary conditions far into the future. Immediate effects include the spread of heat islands, significant losses of biomass carbon and forest‐dependent biodiversity, erosion of watershed values, and the destruction of tens of millions of years of evolutionary history.