Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula

Invasive species are known to influence the structure and function of invaded ecological communities, and preventive measures appear to be the most efficient means of controlling these effects. However, management of biological invasions requires use of adequate tools to understand and predict invasion patterns in recently introduced areas. The present study: (1) estimates the potential geographic distribution and ecological requirements of the Argentine ant (Linepithema humile Mayr), one of the most conspicuous invasive species throughout the world, in the Iberian Peninsula using ecological niche modeling, and (2) provides new insights into the process of selection of consensual areas among predictions from several modeling methodologies. Ecological niche models were developed using 5 modeling techniques: generalized linear models (GLM), generalized additive models (GAM), generalized boosted models (GBM), Genetic Algorithm for Rule-Set Prediction (GARP), and Maximum Entropy (Maxent). Models for the eastern and western portions of the Iberian Peninsula were built using subsets of occurrence and environmental data to investigate the potential for ecological niche differences between the invading populations. Our results indicate geographic differences between predictions of different approaches, and the utility of ensemble predictions in identifying areas of uncertainty regarding the species’ invasive potential. More generally, our models predict coastal areas and major river corridors as highly suitable for Argentine ants, and indicate that western and eastern Iberian Peninsula populations occupy similar environmental conditions.

Do Ranger Stations Deter Poaching Activity in National Parks in Thailand?

As protected areas become more accessible via transportation networks, fragmentation, and encroachment from the borders, carnivores in these areas frequently decline. To counter these pressures, patrolling and active wildlife enforcement are widely accepted as fundamental conservation strategies. Using the case example of Khao Yai National Park (KYNP) and data from a camera trap survey, we modeled and evaluated the effectiveness of ranger stations in reducing human access and illegal activities, and in increasing prey and predator presence. This type of data and analysis is needed to monitor and evaluate enforcement effectiveness and develop adaptive management strategies. At KYNP, we used camera‐trapping data as a proxy to evaluate whether or not a positive impact of ranger stations on wildlife distribution could outweigh edge effects from human disturbance. We assessed factors affecting the distribution of poachers and wildlife using Maxent. Our analysis was based on 217 camera trap locations (6260 trap nights) and suggests that ungulates and poachers persist nearby ranger stations. Rangers should increase patrolling efforts of border areas; however, increasing wildlife patrolling in inaccessible areas with mobile range units may be more effective than establishing more ranger stations along park boundaries.     

全球气候变化下贵州省青冈林的潜在生境动态

为了解贵州省青冈林在全球气候变化下的潜在分布特征,基于现状分布数据,结合当前气候数据和未来气候变化情景(RCP8.5情景,2070-2099年)构建Maxent潜在分布模型,预测贵州省青冈林的潜在分布变化。结果表明,最冷季均温(bio11)、最冷月最低温度(bio6)和年均降水量(bio12)为控制贵州省青冈林潜在生境的主导气候因子;RCP8.5情景下贵州省青冈林的潜在分布面积相较当前气候条件增加,中度适宜生境增加19 419 km2,高度适宜生境增加9 944 km2;中度适宜生境平均海拔较当前气候条件上升126 m,高度适宜生境平均上升85 m。总的来说,贵州省青冈林对全球气候变化的响应不十分敏感。     

基于Maxent模型的双药芒不同时期潜在分布研究

双药芒（Miscanthus nudipes）是一种适应寒冷和干旱环境的芒属植物,具备驯化成具有生态修复、观赏等多功能植物的潜力。在双药芒现有分布记录的基础上,采用Maxent模型构建其在末次间冰期、末次盛冰期、现代以及未来（2070年）的潜在分布格局,在评价环境因子对其分布模型影响的前提下,定量分析不同生态气候情景下双药芒适生区域的变化。结果表明：（1）温度变化方差在440-645,最冷月份最低温在-8.5-0℃,年温变化范围在22-30℃,5月降水量在70-115 mm,海拔范围在1630-3750 m,6月太阳辐射小于15800 kJ m^-2 d^-1时适宜双药芒的生长和分布。其中,温度变化方差是影响双药芒地理分布格局的关键性气候限制条件。（2）双药芒现代适生区面积为632184.45 km²,主要分布在四川省、云南省、西藏自治区、贵州省、陕西省、甘肃省、重庆市、湖北省和河南省。四川省中部地区、云南的北部和西藏的东部是分布中心。（3）从末次间冰期到未来2070年低排放情景下潜在适生面积将缩减到546745.02 km²,缩减面积为94867.23 km²,占当前生境的15.00%。（4）从末次间冰期至末次盛冰期,分布中心点向东南方向发生大幅度迁移,从末次盛冰期至未来,分布中心趋于稳定。总体来看未来双药芒分布中心有向冰期分布中心迁移的趋势。以上研究为研究芒属植物的起源及进化提供了参考。     

气候变化情景下祁连圆柏在青海省的适宜分布区预测

祁连圆柏具有良好的水土保持功能,是青海省高寒干旱地区造林绿化的优良乡土树种之一,预测未来气候变化情景下祁连圆柏在青海省的潜在地理分布将为祁连圆柏的经营管理和引种栽培提供理论指导。本研究基于实地调查和资料搜集获得88个有效地理分布样点,利用Maxent模型和ArcGIS空间分析技术对当前气候条件下祁连圆柏在青海省的潜在地理分布进行模拟,综合Jackknife检验和相关系数,分析影响祁连圆柏潜在分布的主导限制因子,同时结合第六次国际耦合模式比较计划(CMIP6)的气候模式数据,预测祁连圆柏在3种(SSP126、SSP245、SSP585)气候变化情景下2061—2080年潜在适生区的变化。结果表明:Maxent模型受试者工作特征曲线下面积(AUC)都大于0.92,具有较好的预测能力。在当前气候条件下,祁连圆柏的适宜分布区主要位于青海省东部,总适宜区面积占比为11.2%,影响其地理分布的主导因子是海拔、年均降水量、极端最低温和坡度,累计贡献率为85.9%。未来3种气候情景对祁连圆柏适宜区的影响存在差异,SSP245气候情景的适宜区面积将会缩减,SSP126和SSP585气候情景下则会不同程度地扩张,SSP126气候情景的扩张最明显,其扩张区域主要位于泽库县、河南蒙古族自治县中北部和祁连县东南部地区。在未来3种气候情景下,祁连圆柏适宜分布区逐渐向高海拔地区迁移,但在经纬度方向分布变化较小,适宜区总体稳定。     

未来气候变化对孑遗植物鹅掌楸地理分布的影响

为了解未来气候波动对鹅掌楸(Liriodendron chinense)潜在适生区的影响,利用最大熵模型(Maxent)和地理信息系统(ArcGIS)软件,结合物种地理分布点信息,对鹅掌楸当前适生区分布进行了模拟和划分,同时预测了2061-2080年间气候变化条件下鹅掌楸的潜在适生分布区变化,进而分析影响鹅掌楸地理分布的主要气候因子。结果表明,Maxent模型预测的准确性较高,受试者工作特征曲线(ROC)的曲线下面积(AUC)均大于0.9。在未来4种不同的气候变化场景下(RCP 2.6、RCP4.5、RCP 6.0和RCP 8.5)鹅掌楸的地理分布发生变化,在RCP 4.5情景下,鹅掌楸适生面积明显增加;RCP 8.5情景下适生面积明显减少,尤其在贵州地区以及贵州、重庆与湖南的交界处。因此,鹅掌楸适生区分布的几何中心不变,而适生面积随着温室气体浓度的增加呈现先增加后减少的趋势。昼夜温差月均值、最湿季降雨和最干季降雨是影响鹅掌楸地理分布的主要气候因子,其累计贡献率达77.1%。     

Evaluating the invasion risk of longhorn crazy ants (Paratrechina longicornis) in South Korea using spatial distribution model

The longhorn crazy ant (Paratrechina longicornis) is a globally distributed ant species with a high invasion risk, suggesting the need to use species distribution modeling to evaluate its potential distribution. Therefore, this study aimed to predict the potential distribution of longhorn crazy ants in response to climate change by using CLIMEX and Maxent and identifying the climatic factors that influence their habitat. Then, the model outcomes were used to construct an ensemble map to evaluate invasion risk in South Korea. The results indicated that temperature-related variables mainly affect the distribution of the longhorn crazy ant, and the two models showed consensus regions in South America, Africa, Australia, and Southeast Asia. Due to climate change, it was expected that the northern limit would somewhat rise. In South Korea, high-risk areas were predicted to be located along the coasts, but they would expand as a consequence of climate change. Since the invasion of longhorn crazy ants has occurred via commercial trades, a relatively high risk in coastal areas demands a high level of attention. We expect that this study will provide initial insight into selecting areas for longhorn crazy ant quarantine with ensemble species distribution modeling.     

基于广义线性模型和最大熵模型的黑龙江省林火空间分布模拟

林火分布模型是在较大区域上描述林火空间分布的强有力工具,并可以确定影响林火分布的控制因子.本研究基于黑龙江省1996-2006年的历史火烧记录数据,分别采用广义线性模型和最大熵模型分析了地形、人类活动和土地覆被类型等环境控制因子对黑龙江省林火空间分布的影响,并比较了模型预测精度、评价环境变量重要性及预测火点概率分布图等.结果表明:两个模型的预测精度达中等水平,而最大熵模型的预测精度要略高于广义线性模型.总体而言,与人类活动相关的变量是林火分布模型最佳的环境变量,地形变量次之.尽管两个模型在预测精度和环境变量重要性方面都有很大的相似性,但最大熵模型产生的火点概率图空间格局与广义线性模型产生的明显不同.本研究说明,为了更加精确地确定森林火灾发生的热点地区,应该采用不同模型进行比较,或者有选择性地进行组合以产生综合的预测结果,从而为森林防火工作提供更加合理高效的建议.     

Incorporating bioclimatic and biogeographic data in the construction of species distribution models in order to prioritize searches for new populations of threatened flora

The aim of this study was to analyse the usefulness of incorporating bioclimatic and biogeographic data into digital species prediction and modelling tools in order to identify potential habitats of rare or endangered flora taxa. Species distribution models (SDMs) were obtained using the Maximum entropy algorithm. Habitat suitability maps were based on sites of known occurrence of studied species. The study showed that highly reliable habitat prediction models can be obtained through the inclusion of bioclimatic and biogeographic maps when modelling these species. The resultant SDMs are able to fit the search area more closely to the characteristics of the species, excluding the percentage of highly suitable areas that are located far from the known distribution of the taxon, where the probability of finding the plant is low. Therefore, it is possible to overcome one of the most commonly encountered problems in the construction of rare or threatened flora taxa SDMs, derived from the low number of initial citations. The resulting SDMs and the vegetation map enable prioritization of the search for new populations and optimization of the economic and human resources used in the collection of field data.     

Spatially explicit predictions of blood parasites in a widely distributed African rainforest bird

Critical to the mitigation of parasitic vector-borne diseases is the development of accurate spatial predictions that integrate environmental conditions conducive to pathogen proliferation. Species of Plasmodium and Trypanosoma readily infect humans, and are also common in birds. Here, we develop predictive spatial models for the prevalence of these blood parasites in the olive sunbird (Cyanomitra olivacea). Since this species exhibits high natural parasite prevalence and occupies diverse habitats in tropical Africa, it represents a distinctive ecological model system for studying vector-borne pathogens. We used PCR and microscopy to screen for haematozoa from 28 sites in Central and West Africa. Species distribution models were constructed to associate ground-based and remotely sensed environmental variables with parasite presence. We then used machine-learning algorithm models to identify relationships between parasite prevalence and environmental predictors. Finally, predictive maps were generated by projecting model outputs to geographically unsampled areas. Results indicate that for Plasmodium spp., the maximum temperature of the warmest month was most important in predicting prevalence. For Trypanosoma spp., seasonal canopy moisture variability was the most important predictor. The models presented here visualize gradients of disease prevalence, identify pathogen hotspots and will be instrumental in studying the effects of ecological change on these and other pathogens.