Rising global temperatures are suggested to be drivers of shifts in tree species ranges. The resulting changes in community composition may negatively impact forest ecosystem function. However, long-term shifts in tree species ranges remain poorly documented. We test for shifts in the northern range limits of 16 temperate tree species in Quebec, Canada, using forest inventory data spanning three decades, 15° of longitude and 7° of latitude. Range shifts were correlated with climate warming and dispersal traits to understand potential mechanisms underlying changes. Shifts were calculated as the change in the 95th percentile of latitudinal occurrence between two inventory periods (1970-1978, 2000-2012) and for two life stages: saplings and adults. We also examined sapling and adult range offsets within each inventory, and changes in the offset through time. Tree species ranges shifted predominantly northward, although species responses varied. As expected shifts were greater for tree saplings, 0.34 km yr , than for adults, 0.13 km yr . Range limits were generally further north for adults compared to saplings, but the difference diminished through time, consistent with patterns observed for range shifts within each life stage. This suggests caution should be exercised when interpreting geographic range offsets between life stages as evidence of range shifts in the absence of temporal data. Species latitudinal velocities were on average <50% of the velocity required to equal the spatial velocity of climate change and were mostly unrelated to dispersal traits. Finally, our results add to the body of evidence suggesting tree species are mostly limited in their capacity to track climate warming, supporting concerns that warming will negatively impact the functioning of forest ecosystems.

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&lt;p&gt;Modern-day southern Ethiopia exhibits a complex mosaic of vegetation types. These types range from desert scrubland along the shores of Lake Turkana, to woodlands and wooded grasslands in the Omo-River-Lowlands and Chew Bahir catchment, and Afromontane forests of the Ethiopian Highlands. Over the past 20 ka, this region has experienced a variable climate, from the dry Last Glacial Maximum (25-18 ka BP) to the wet African Humid Period (15-5 ka BP), and back to present-day dry conditions. These oscillations likely had an impact on the biosphere and its human inhabitants. The biosphere, especially climate-induced changes in vegetation, in turn have a feedback effect on the local climate &amp;#8211; and must therefore be considered in climate models and hydro-balance models. However, there are hardly any data on changes in vegetation during the dry-humid-dry transition of the AHP that could be used to parameterize such models.&lt;/p&gt;&lt;p&gt;As a contribution to an enhanced understanding of the role that paleo-vegetation could have played during those transitions, we present here a new comprehensive vegetation model. This study links a Predictive Vegetation Model (PVM) with the available vegetation-proxy records from southern Ethiopia, including a new phytolith record from Chew Bahir. The PVM uses an 18-year averaged time series of the Global Precipitation Measurement as well as SRTM elevation data to predict an 18-year averaged time series of MODIS landcover and vegetation parameters using boosted regression trees. We linked the PVM and resulting surface parameters (moisture availability, surface drag coefficient, albedo) with an existing hydro-balance model of the southern Ethiopian Rift to calculate precipitation during the AHP and hence also model the paleo-vegetation during this period. Available paleo-vegetation data including a new grass phytolith record from the sediments of an 11 m-meter long sediment core from the margin of paleo-Lake Chew Bahir were then used to compare model and proxy results. Being able to validate our new model data with actual vegetation proxy data for the first time enables us to gain valuable insights into the paleo-dimension of the vegetation mosaic of southern Ethiopia, a possible habitat of early&lt;em&gt; Homo sapiens&lt;/em&gt;.&lt;/p&gt;

Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types.