George Karsten

Abstract Low temperatures largely determine the geographic limits of plant species by reducing survival and growth. Interspecific differences in the geographic distribution of mangrove species have been associated with cold tolerance, with exclusively tropical species being highly cold-sensitive and subtropical species being relatively cold-tolerant. To identify species-specific adaptations to low temperatures, we compared the chilling stress response of two widespread Indo-West Pacific mangrove species from Rhizophoraceae with differing latitudinal range limits—Bruguiera gymnorhiza (subtropical range limit) and Rhizophora apiculata (tropical range limit). For both species, we measured the maximum photochemical efficiency of photosystem II (Fv/Fm) as a proxy for the physiological condition of the plants and examined gene expression profiles during chilling at 15 °C and 5 °C. At 15 °C, B. gymnorhiza maintained a significantly higher Fv/Fm than R. apiculata. However, at 5 °C, both species displayed equivalent Fv/Fm values. Thus, species-specific differences in chilling tolerance were only found at 15 °C, and both species were sensitive to chilling at 5 °C. At 15 °C, B. gymnorhiza down-regulated genes related to the light reactions of photosynthesis and up-regulated a gene involved in cyclic electron flow regulation, while R. apiculata down-regulated more RuBisCo related genes. At 5 °C, both species repressed genes related to CO2 assimilation. The down-regulation of genes related to light absorption and up-regulation of genes related to cyclic electron flow regulation are photoprotective mechanisms that likely contributed to the greater PSII photochecmical efficiency of B. gymnorhiza at 15 °C. The results of this study provide evidence that the distributional range limits and potentially the expansion rates of plant species are associated with differences in the regulation of photosynthesis and photoprotective mechanisms under low temperatures.

The first comprehensive species distribution models for orchid, its fungal symbionts and pollinator are presented. To evaluate impact of global warming on these organisms three different projections and four various climate change scenarios were analysed. The niche modelling was based on presence-only records of Limodorum abortivum , two species of Russula and three insects pollinating orchid ( Anthophora affinis, Bombus terrestris, Rhodanthidium septemdentatum ). Two sets of orchid predictions were examined—the first one included only climatic data and the second one was based on climate data and data on future distribution of orchid fungal symbionts. Overall, a poleward range shift is predicted to occur as a result of climate change and apparently global warming will be favorable for L. abortivum and its potential geographical range will expand. However, due to the negative effect of global warming on fungal symbionts of L. abortivum , the actual extension of the suitable niches of the orchid will be much limited. Considering future possibility of cross-pollination, the availability of A. affinis for L. abortivum will decrease and this bee will be available in the worst case scenarios only for 21% of orchid populations. On the other hand, the overlap of orchid and the buff-tailed bumblebee will increase and as much as 86.5% of plant populations will be located within B. terrestris potential range. Also the availability of R. septemdentatum will be higher than currently observed in almost all analysed climate change projections. This study showed the importance of inclusion of ecological factors in species distribution models as the climate data itself are not enough to estimate the future distribution of plant species. Moreover, the availability of pollen vectors which is crucial for long-term survival of orchid populations should be analysed in context of climate changes.

The region of central Veracruz is considered a biodiversity hotspot due to its high species richness and environmental heterogeneity, but only 2% of this region is currently protected. This study aimed to assess the current protected area system’s effectiveness and to identify priority conservation areas for expanding the existing protected area system. We used the distribution models of 1186 species from three kingdoms (Animalia, Plantae, and Fungi) together with ZONATION software, a conservation planning tool, to determine areas that could help expand the current network of protected areas. We applied three different parametrizations (including only species, using the boundary quality penalty, and using corridor connectivity). We found that protecting an additional 15% of the area would increase, between 16.2% and 19.3%, the protection of the distribution area of all species. We propose that the regions with a consensus of the three parametrizations should be declared as new protected areas to expand 374 km2 to the 216 km2 already protected. Doing so would double the protected surface in central Veracruz. The priority areas identified in this study have more species richness, carbon stock values, natural vegetation cover, and less human impact index than the existing protected areas. If our identified priority areas are declared protected, we could expect a future recovery of endangered species populations for Veracruz. The proposed new protected areas are planned and designed as corridors connecting currently isolated protected areas to promote biodiversity protection.

Aims Human-induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence–habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location Virtual Southeast Asia. Methods Using 100 virtual species, we simulated ARCs with 100-year land-use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land-use variable (conventional modelling protocols) and with a temporally dynamic land-use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results Greater ARC resulted in greater occurrence–habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence–habitat mismatching prevented models with the contemporary land-use variable from predicting anthropogenic-related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land-use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land-use variable consistently outperformed models without. Main conclusions We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three-step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations.

The early Eocene (∼56‐48 million years ago) is characterised by high CO2 estimates (1200‐2500 ppmv) and elevated global temperatures (∼10 to 16°C higher than modern). However, the response of the hydrological cycle during the early Eocene is poorly constrained, especially in regions with sparse data coverage (e.g. Africa). Here we present a study of African hydroclimate during the early Eocene, as simulated by an ensemble of state‐of‐the‐art climate models in the Deep‐time Model Intercomparison Project (DeepMIP). A comparison between the DeepMIP pre‐industrial simulations and modern observations suggests that model biases are model‐ and geographically dependent, however these biases are reduced in the model ensemble mean. A comparison between the Eocene simulations and the pre‐industrial suggests that there is no obvious wetting or drying trend as the CO2 increases. The results suggest that changes to the land sea mask (relative to modern) in the models may be responsible for the simulated increases in precipitation to the north of Eocene Africa. There is an increase in precipitation over equatorial and West Africa and associated drying over northern Africa as CO2 rises. There are also important dynamical changes, with evidence that anticyclonic low‐level circulation is replaced by increased south‐westerly flow at high CO2 levels. Lastly, a model‐data comparison using newly‐compiled quantitative climate estimates from palaeobotanical proxy data suggests a marginally better fit with the reconstructions at lower levels of CO2.

Background and research aims. The Andean páramo is renowned for its unique biodiversity and sensitivity to environmental threats. However, vulnerability assessments remain scarce, which hinders our capacity to prioritize and apply efficient conservation measures. To this end, we established the Red List of the páramo vascular flora from the Nevados National Natural Park and proposed conservation strategies for its threatened species. Methods. We performed International Union for Conservation of Nature (IUCN) Red List assessments by evaluating Criterion B, including sub-criteria B1–Extent of Occurrence and B2–Area of Occupancy, and using a systematic geographic-ecological approach for conditions a (Location analysis) and b (Continuing decline). We then executed a Conservation Gap Analysis to prioritize species for in- situ and/or ex-situ conservation. Results. Summing our 233 evaluated species with previous assessments, we completed the Red List of 262 páramo species and encountered 3% Threatened (7 VU, one EN), 44% Not Threatened (65 LC, 50 NT), and 53% Data Deficient. We acknowledged Lupinus ruizensis as Endangered and Aequatorium jamesonii, Carex jamesonii, Elaphoglossum cuspidatum, Miconia latifolia, Miconia alborosea, Pentacalia gelida, and Themistoclesia mucronata as Vulnerable. Conclusion. The eight threatened species should be included as target species in the PNN Nevados management plan 2023–2028 and regarded as national conservation priorities. Implications for Conservation. We recommend in-situ conservation for Medium-Priority species A. jamesonii, E. cuspidatum, and T. mucronata with thorough monitoring, paired with sub-population transfers for High-Priority species C. jamesonii. For the endemic L. ruizensis and P. gelida, we suggest combined in-situ/ex-situ strategies taking advantage of national germoplasm collections, like the seed bank of the Bogotá Botanical Garden José Celestino Mutis.

Abstract. Statistical climate reconstruction techniques are fundamental tools to study past climate variability from fossil proxy data. In particular, the methods based on probability density functions (or PDFs) can be used in various environments and with different climate proxies because they rely on elementary calibration data (i.e. modern geolocalised presence data). However, the difficulty of accessing and curating these calibration data and the complexity of interpreting probabilistic results have often limited their use in palaeoclimatological studies. Here, I introduce a new R package (crestr) to apply the PDF-based method CREST (Climate REconstruction SofTware) on diverse palaeoecological datasets and address these problems. crestr includes a globally curated calibration dataset for six common climate proxies (i.e. plants, beetles, chironomids, rodents, foraminifera, and dinoflagellate cysts) associated with an extensive range of climate variables (20 terrestrial and 19 marine variables) that enables its use in most terrestrial and marine environments. Private data collections can also be used instead of, or in combination with, the provided calibration dataset. The package includes a suite of graphical diagnostic tools to represent the data at each step of the reconstruction process and provide insights into the effect of the different modelling assumptions and external factors that underlie a reconstruction. With this R package, the CREST method can now be used in a scriptable environment and thus be more easily integrated with existing workflows. It is hoped that crestr will be used to produce the much-needed quantified climate reconstructions from the many regions where they are currently lacking, despite the availability of suitable fossil records. To support this development, the use of the package is illustrated with a step-by-step replication of a 790 000-year-long mean annual temperature reconstruction based on a pollen record from southeastern Africa.

The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…

Mangrove forests provide a wide range of ecosystem services, yet they are declining rapidly due to climate change and human activities. Identification of conservation priority targets across spatial and temporal scales may assist in planning and decision making, especially in areas having rich mangr…

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