latest hugo release for build

.github/workflows/deploy-blogdown.yaml

@@ -34,6 +34,7 @@ jobs:
         shell: Rscript {0}
 
       - name: Install hugo
+        # run: blogdown::install_hugo("0.125.4")
         run: blogdown::install_hugo("0.104.3")
         shell: Rscript {0}
 

README.md

@@ -62,4 +62,4 @@ Building the website and serving it can be done in R.
 ```r
 blogdown::serve_site()
 ```
-This returns a message containing the server address which can be opened with any web browser (copy and paste it into the address line of the browser). 
+This returns a message containing the server address which can be opened with any web browser (copy and paste it into the address line of the browser). 

content/project/MIND/index.md

@@ -66,11 +66,11 @@ The overarching aim of MIND is to develop new model and data fusion approaches t
 
 A postdoc position to work with me on this project (see below 'Postdoc: Scaling from trees to the forest') is open now. A link to the ad and application is [here](https://apply.refline.ch/845721/7239/pub/1/index.html). 
 
-<!-- ![MIND Eccellenza across scales](/img/dreamstime_m_76702519_verysmall.jpg) -->
+<!-- ![MIND Eccellenza across scales](dreamstime_m_76702519_verysmall.jpg) -->
 
-<!-- ![](/img/tree_root_allocation2_small.jpg) -->
+<!-- ![](tree_root_allocation2_small.jpg) -->
 
-![MIND Eccellenza across scales](/img/mind_scales.png)
+![MIND Eccellenza across scales](mind_scales.png)
 
 
 ## Key questions
@@ -89,7 +89,7 @@ Our primary target is to better understand and model **allocation**, that is the
 
 What we can observe at the small scale (from experiments, forest inventories, flux measurements, etc.) determines what is happening at the global scale. By addressing allocation from all angles, and by systematically integrating models and observational data, MIND targets a better understanding of the controls on allocation and improves our predictive ability to simulate  general patterns in allocation and its response to shifting in resource limitations and how this affects ecoystem dynamics and the terrestrial carbon balance.
 
-![MIND Eccellenza across scales](/img/mind_scales_obs.png)
+![MIND Eccellenza across scales](mind_scales_obs.png)
 
 ## Approach
 
@@ -112,7 +112,7 @@ This project will offer three fully funded positions:
 
 ... addressing three objectives:
 
-![](/img/mind_scales_projectorg.png)
+![](mind_scales_projectorg.png)
 
 ### PhD 1: The nature of nutrient limitation 
 
@@ -122,7 +122,7 @@ Research will address three key questions:
 - How does soil fertility influence the carbon cycle?
 - What controls the rates of N fixation and loss?
 
-![](/img/mind_scales_nutrients.png)
+![](mind_scales_nutrients.png)
 
 We will use the Global Change Manipulation Experiments database (under development, collaboration with [Dr. Sara Vicca, PLECO, University of Antwerp](https://www.uantwerpen.be/en/staff/sara-vicca/)) and explore the observational constraints on alternative model formulations for simulating coupled carbon and nitrogen dynamics in terrestrial ecoystems. This work will make important advances on several fronts: Generating fundamental insights into ecosystem dynamics and biogeochemical cycles, exploring new territory in model-data fusion on the basis of observations from manipulation experiments, and developing a reusable and scalable model testbed as the basis of future community-driven model intercomparisons.
 
@@ -145,7 +145,7 @@ Our target questions are:
 - How can we estimate forest growth from individual-based data (forest inventories and tree ring records)?
 - How can we combine multiple data streams to detect allocation changes in forests today?
 
-![](/img/mind_scales_objectiveC.png)
+![](mind_scales_objectiveC.png)
 
 How does climate change alter forest dynamics and what are implications for the global carbon cycle and local ecosystem services? Increasing CO2, trends in nutrient inputs from atmospheric deposition, extending growing seasons and changing drought patterns affect tree growth and forest biomass in myriad ways. One of the open key questions is whether stimulated growth in a high-CO2 world is compensated by an accelerated tree life cycle and higher mortality.
 

content/project/photocold/index.md

@@ -40,10 +40,9 @@ url_video: ""
 slides: ""
 ---
 
-
 Ecosystem-level photosynthesis (gross primary production: GPP) can be predicted using remotely sensed information on vegetation cover, local measurements of solar radiation and relatively simple models for the efficiency of photosynthesis (Ryu et al., 2019; Stocker et al., 2019). But one aspect of model-data mismatch stands out: The early-season increase in GPP is typically simulated to be around a month too early at some sites but not at others (Fig. 1). It will be your challenge to find out why and how to resolve this model deficiency.
 
-![](/img/photocold.png)
+![](photocold.png)
 
 *Fig. 1 Mean seasonal cycle of GPP aggregated for 14 sites where without early season overestimation of modelled GPP (a) and 13 sites with an apparent overestimation of early season modelled GPP (b).*
 

content/project/photocold_d/index.md

@@ -0,0 +1,51 @@
+---
+# Documentation: https://wowchemy.com/docs/managing-content/
+
+title: "Photosynthese und Spätfrost"
+summary: "Kalte Winter verzögern den Beginn von Photosynthese im Frühling"
+authors: [Benjamin Stocker, Yunpeng Luo]
+tags: []
+categories: []
+date: 2021-09-25T11:56:58+02:00
+
+# Optional external URL for project (replaces project detail page).
+external_link: ""
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Custom links (optional).
+#   Uncomment and edit lines below to show custom links.
+# links:
+# - name: Follow
+#   url: https://twitter.com
+#   icon_pack: fab
+#   icon: twitter
+
+url_code: ""
+url_pdf: ""
+url_slides: ""
+url_video: ""
+
+# Slides (optional).
+#   Associate this project with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
+#   Otherwise, set `slides = ""`.
+slides: ""
+---
+
+Die Kohlenstoffaufnahme von Wäldern in unseren Breiten ist stark an die saisonalen Schwankungen von Sonnenlicht und die Phänologie gekoppelt. Sobald im Frühling die Sonneneinstrahlung zunimmt und Blätter ausgetrieben werden, nimmt die Aufnahme von CO2 durch die Photosynthese rasch zu. Wir konnten zeigen, dass diese Zunahme an verschiedenen Standorten stark verzögert ist. Trotz rasch ansteigender Sonneneinstrahlung und grüner Vegetation, verzögert sich der saisonale Anstieg der CO2-Aufnahme an bestimmten Standorten um mehrere Wochen. Wir fanden heraus, dass diese Verzögerungen damit zu tun haben, dass an diesen Standorten Spätfrost besonders häufig auftritt – in Nordamerika und Regionen mit kontinentalem Klima, weniger aber in Regionen mit temperiertem, maritimem Klima, z.B. in Nordwesteuropa. Tiefe Temperaturen und hohe Sonneneinstrahlung kann für die Blattphysiologie schädlich sein. Die verzögerte Re-aktivierung der Photosynthese an diesen Standorten ist wahrscheinlich eine Anpassung zur Vermeidung von schädlichen meteorologischen Bedingungen.
+
+![](photocold.png)
+
+*Fig. 1 Saisonale Schwankung der CO2 Aufnahme durch die Photosynthese. Unterschieden sind (b) Daten von 14 Standorten, an welchen eine verspätete Zunahme der Photosynthese in Vergleich des zu erwartenden Flusses (rot) beobachtet wird (Beobachtungen schwarz). In (a) ist die mittlere jährliche Schwankung an 13 Standorten gezeigt, wo dieses Phänomen nicht auftritt.*
+
+### Publizierte Studie
+
+Luo, Y., Gessler, A., D’Odorico, P., Hufkens, K., & Stocker, B. D. (2023). Quantifying effects of cold acclimation and delayed springtime photosynthesis resumption in northern ecosystems. New Phytologist, 240(3), 984–1002. https://doi.org/10.1111/nph.19208