{"id":4209,"date":"2018-04-17T09:52:45","date_gmt":"2018-04-17T08:52:45","guid":{"rendered":"http:\/\/ar17.iiasa.ac.at\/?p=4209"},"modified":"2018-04-23T08:59:12","modified_gmt":"2018-04-23T07:59:12","slug":"vegetation-dynamics","status":"publish","type":"post","link":"http:\/\/ar17.iiasa.ac.at\/vegetation-dynamics\/","title":{"rendered":"Shedding light on vegetation dynamics"},"content":{"rendered":"

[et_pb_section bb_built=”1″ fullwidth=”on” _builder_version=”3.0.100″ next_background_color=”#000000″][et_pb_fullwidth_image admin_label=”Top image ||| FINAL” src=”http:\/\/ar17.iiasa.ac.at\/wp-content\/uploads\/sites\/3\/2018\/04\/shutterstock_80185144_crop.jpg” _builder_version=”3.0.106″ module_alignment=”center” custom_margin=”|||” custom_padding=”|||” animation_style=”fade” animation_direction=”left” animation_duration=”600ms” box_shadow_style=”preset3″ box_shadow_color=”#6b6b6b” custom_css_main_element=”max-height: 260px” global_module=”995″ saved_tabs=”all” animation_starting_opacity=”15%” show_in_lightbox=”off” url_new_window=”off” use_overlay=”off” \/][\/et_pb_section][et_pb_section bb_built=”1″ _builder_version=”3.0.47″ prev_background_color=”#000000″][et_pb_row custom_padding=”0px|||” custom_margin=”0px|||” _builder_version=”3.0.101″][et_pb_column type=”2_3″][et_pb_post_title admin_label=”Title of the post or the page” meta=”off” featured_image=”off” _builder_version=”3.0.106″ title_text_color=”#0c71c3″ title_line_height=”1.4em” custom_margin=”0px|||” custom_padding=”0px|||” animation_style=”fade” animation_direction=”bottom” animation_intensity_flip=”43%” global_module=”237″ saved_tabs=”all” locked=”off” title=”on” date_format=”M j, Y” text_color=”dark” text_background=”off” author=”on” date=”on” categories=”on” comments=”on” featured_placement=”below” \/][et_pb_divider admin_label=”Divider (horizontal line new)” color=”#adadad” show_divider=”on” divider_position=”center” height=”0px” _builder_version=”3.0.106″ max_width=”95%” module_alignment=”left” animation_style=”fade” animation_direction=”left” global_module=”1357″ saved_tabs=”all” \/][et_pb_text admin_label=”Teaser text” _builder_version=”3.0.106″ text_font=”|700|||||||” animation_style=”fade” animation_direction=”bottom” global_module=”270″ saved_tabs=”all” animation_duration=”500ms” animation_starting_opacity=”21%” background_layout=”light”]<\/p>\n

Plants are of central importance to terrestrial ecosystems and play a key role in the global carbon cycle. In 2017, the IIASA Evolution and Ecology Program showed how the inclusion of evolutionary and eco-physiological principles enables improved vegetation models.<\/strong><\/p>\n

[\/et_pb_text][et_pb_text admin_label=”CONTENT OF THE PAGE | EDIT HERE” _builder_version=”3.0.106″ animation_style=”fade” animation_direction=”bottom” global_module=”272″ saved_tabs=”all” custom_margin=”|||” custom_padding=”|||” animation_duration=”500ms” animation_starting_opacity=”20%” background_layout=”light”]<\/p>\n

Hundreds of thousands of species of plants currently cover the surface of the earth. They are essential for terrestrial ecosystems and the ecosystem services they provide. By producing oxygen while absorbing atmospheric carbon dioxide, plants form an integral part of the global carbon cycle. With human activities now rapidly changing the conditions to which plant species are adapted, there is an urgent need to understand how changing environmental conditions will affect vegetation cover across the globe.<\/p>\n

Broadly speaking, current models that aim to predict plant diversity fall into either of two classes. These are neutral models of biodiversity that predict vegetation structure by assuming that community composition arises through random processes, or niche-based models that predict vegetation structure by assuming competition between plant species. Until now, only the neutral models have been able to give reasonable predictions of species diversity, with the niche-based models typically giving rise to only a few coexisting species.<\/p>\n

To show how enhanced biological realism can improve predictions of plant diversity and potentially reconcile these two approaches, researchers from the Evolution and Ecology Program developed an eco-evolutionary vegetation model that builds on established eco-physiological and evolutionary principles [1]. By moving beyond the often simplistic assumptions of traditional niche-based models, the study showed how plant diversity can be predicted from knowledge of local environmental conditions. Moreover, by accounting for evolution in two important functional traits, a large neutral range of trait combinations emerges across which species have equal fitness, arguably reconciling neutral and niche-based theory.<\/p>\n

\"\"<\/a>

Examples of successional vegetation dynamics under two different environmental settings, corresponding to temperate forests (left panel) and tropical rain forests (right panel). Early fast-growing species quickly establish themselves following a disturbance and are later outcompeted by slower-growing, more efficient species. All shown species\u2014or plant functional types\u2014are not just assumed from the outset, but emerge intrinsically as a predictive and empirically testable result of the model.<\/p><\/div>\n

As the compositions of plant communities arise intrinsically from the model, rather than being assumed from the outset, the novel approach taken in this study (i.e., working from eco-evolutionary first principles) has the potential to improve species-conservation efforts, land-use policies, and forest-management practices. In addition, it can also help to improve contemporary dynamic global vegetation models that are used to predict the impacts of global and regional climate change. The potential value is especially large for improving predictions of future vegetation structures under environmental conditions that do not currently exist anywhere on earth. The IIASA cross-cutting project on Dynamic vegetation models: The next generation<\/a> is capitalizing on these opportunities.<\/p>\n

[\/et_pb_text][et_pb_text admin_label=”References Title | not edit” _builder_version=”3.0.101″ text_font=”|700|||||||” header_font=”|700|||||||” animation_style=”fade” animation_direction=”bottom” global_module=”304″ saved_tabs=”all”]<\/p>\n

References<\/h3>\n

[\/et_pb_text][et_pb_divider admin_label=”Divider (horizontal line new)” color=”#adadad” show_divider=”on” divider_position=”center” height=”0px” _builder_version=”3.0.106″ max_width=”95%” module_alignment=”left” animation_style=”fade” animation_direction=”left” global_module=”1357″ saved_tabs=”all” \/][et_pb_text admin_label=”References | edit” _builder_version=”3.0.106″ animation_style=”fade” animation_direction=”bottom” global_module=”306″ saved_tabs=”all” background_layout=”light”]<\/p>\n

[1] Falster DS, Br\u00e4nnstr\u00f6m \u00c5, Westoby M, Dieckmann U (2017). Multi-trait successional forest dynamics enable diverse competitive coexistence<\/a>. Proceedings of the National Academy of Sciences of the USA<\/em> 114: 2719\u20132728.<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″][et_pb_button admin_label=”Program website” button_url=”http:\/\/ar17.iiasa.ac.at\/eep\/” button_text=”Evolution and Ecology” button_alignment=”left” _builder_version=”3.0.106″ custom_margin=”|||” custom_padding=”|||” custom_button=”on” button_text_size=”16″ button_text_color=”#0c71c3″ button_bg_color=”#ffffff” button_border_width=”1px” button_border_color=”#0c71c3″ button_border_radius=”35px” button_icon=”%%24%%” button_icon_color=”#0c71c3″ button_icon_placement=”left” button_on_hover=”off” button_text_color_hover=”#0c71c3″ button_bg_color_hover=”rgba(12,113,195,0.35)” button_border_color_hover=”rgba(12,113,195,0.83)” button_border_radius_hover=”35px” animation_style=”fade” custom_css_main_element=”width: 100%;||” global_module=”317″ saved_tabs=”all” button_text_shadow_vertical_length=”0.1em” button_text_shadow_blur_strength=”0.1em” animation_direction=”right” background_layout=”light” url_new_window=”off” animation_duration=”450ms” animation_starting_opacity=”7%” box_shadow_horizontal=”1px” box_shadow_vertical=”1px” box_shadow_blur=”5px” box_shadow_spread=”0px” \/][et_pb_divider admin_label=”Divider 15 px e.g. under 1 button” _builder_version=”3.0.100″ global_module=”1055″ saved_tabs=”all”]<\/p>\n

 <\/p>\n

[\/et_pb_divider][et_pb_text admin_label=”Collaborators title (do not edit)” _builder_version=”3.0.101″ animation_style=”fade” animation_direction=”right” global_module=”286″ saved_tabs=”all”]<\/p>\n

Collaborators<\/h3>\n

[\/et_pb_text][et_pb_text admin_label=”Collaborators LINKS edit” _builder_version=”3.0.106″ animation_style=”fade” animation_direction=”right” global_module=”295″ saved_tabs=”all” background_layout=”light”]<\/p>\n