Alien plant species: environmental risks in agricultural and agro-forest landscapes under climate change

Alien plant species have been essential for farming and agro-forestry systems and for their supply of food, fiber, tannins, resins or wood from antiquity to the present. They also contributed to supporting functions and regulating services (water, soil, biodiversity) and to the design of landscapes with high cultural and scenic value. Some of those species were intentionally introduced, others arrived accidentally, and a small proportion escaped, naturalized and became invasive in natural ecosystems—these are known as invasive alien species (IAS). Here, invasive means that these species have some significant negative impact, either by spreading from human-controlled environments (e.g. fields, gardens) to natural ecosystems, where they can cause problems to native species, or to other production systems or urban areas, impacting on agricultural, forestry activities or human health. Socio-environmental impacts associated with plant invasions have been increasingly recognized worldwide and are expected to increase considerably under changing climate or land use. Early detection tools are key to anticipate IAS and to prevent and control their impacts. In this chapter, we focus on crop and non-crop alien plant species for which there is evidence or prediction of invasive behaviour and impacts. We provide insights on their history, patterns, risks, early detection, forecasting and management under climate change. Specifically, we start by providing a general overview on the history of alien plant species in agricultural and agroforestry systems worldwide. Then, we assess patterns, risks and impacts resulting from alien plants originally cultivated and that became invasive outside cultivation areas. Afterwards, we provide several considerations for managing the spread of invasive plant species in the landscape. Finally, we discuss challenges of alien plant invasions for agricultural and agroforest systems, in the light of climate change.Joana R. Vicente was supported by POPH/FSE and FCT (Post-Doc grant SFRH/BPD/84044/2012). Ana Sofia Vaz was supported by FSE/MEC and FCT (Ph.D. grant PD/ BD/52600/2014). Ana Isabel Queiroz supported by FCT—the Portuguese Foundation for Science and Technology [UID/HIS/04209/2013 and IF/00222/2013/CP1166/CT0001]. This work received financial support from the European Union (FEDER funds POCI-01-0145-FEDER-006821) and National Funds (FCT/MEC, Fundação para a Ciência e Tecnologia and Ministério da Educação e Ciência) under the Partnership Agreement PT2020 UID/BIA/50027/201

Future breeding and foraging sites of a southern edge population of the locally endangered Black Guillemot Cepphus grylle

Capsule: One of the southernmost populations of the Black Guillemot Cepphus grylle is currently endangered, and the risk may be exacerbated by climate change. Aims: We evaluated the future vulnerability of the Black Guillemot by predicting the impact of climate change on the geographic distribution of its breeding and foraging range in the Baltic Sea. Methods: We used MaxEnt, a species distribution modelling technique, to predict the current and future breeding grounds and foraging sites. Results: We found that although the foraging range is expected to increase in the southern Baltic Sea in future, these areas will no longer be suitable as breeding grounds due to a changing climate, creating a spatial mismatch. Conclusion: Our predictions indicate where threats to the species may be most severe and can be used to guide conservation planning. We advocate conservation measures which integrate potential future threats and focus on breeding sites across the current and future potential geographic range of the Black Guillemot

Future breeding and foraging sites of a southern edge population of the locally endangered Black Guillemot <i>Cepphus grylle</i>

<p><b>Capsule:</b> One of the southernmost populations of the Black Guillemot <i>Cepphus grylle</i> is currently endangered, and the risk may be exacerbated by climate change.</p> <p><b>Aims:</b> We evaluated the future vulnerability of the Black Guillemot by predicting the impact of climate change on the geographic distribution of its breeding and foraging range in the Baltic Sea.</p> <p><b>Methods:</b> We used MaxEnt, a species distribution modelling technique, to predict the current and future breeding grounds and foraging sites.</p> <p><b>Results:</b> We found that although the foraging range is expected to increase in the southern Baltic Sea in future, these areas will no longer be suitable as breeding grounds due to a changing climate, creating a spatial mismatch.</p> <p><b>Conclusion:</b> Our predictions indicate where threats to the species may be most severe and can be used to guide conservation planning. We advocate conservation measures which integrate potential future threats and focus on breeding sites across the current and future potential geographic range of the Black Guillemot.</p

Dynamic modelling in research and management of biological invasions

Invasive species are increasing in number, extent and impact worldwide. Effective invasion management has thus become a core socio-ecological challenge. To tackle this challenge, integrating spatial-temporal dynamics of invasion processes with modelling approaches is a promising approach. The inclusion of dynamic processes in such modelling frameworks (i.e. dynamic or hybrid models, here defined as models that integrate both dynamic and static approaches) adds an explicit temporal dimension to the study and management of invasions, enabling the prediction of invasions and optimisation of multi-scale management and governance. However, the extent to which dynamic approaches have been used for that purpose is under-investigated. Based on a literature review, we examined the extent to which dynamic modelling has been used to address invasions worldwide. We then evaluated how the use of dynamic modelling has evolved through time in the scope of invasive species management. The results suggest that modelling, in particular dynamic modelling, has been increasingly applied to biological invasions, especially to support management decisions at local scales. Also, the combination of dynamic and static modelling approaches (hybrid models with a spatially explicit output) can be especially effective, not only to support management at early invasion stages (from prevention to early detection), but also to improve the monitoring of invasion processes and impact assessment. Further development and testing of such hybrid models may well be regarded as a priority for future research aiming to improve the management of invasions across scales