# Applied bioeconomic models for the management of invasive species

Edeline Eric¹, Fourcy Damien¹, Barbot Marine¹, Paillisson Jean-Marc², Petit Eric¹, Damien Jean-Patrice³, Toupin Olivier³, Loussouarn Albin⁴, Barloy Dominique¹, Figuière Charles⁵, Thomas Alban⁶, Courtois Pierre⁷

1. DECOD (Ecosystem Dynamics and Sustainability), INRAE, IFREMER, Institut Agro, Rennes, France.
2. University of Rennes, CNRS, ECOBIO [(Ecosystèmes, biodiversité, évolution)] - UMR 6553, Rennes, France.
3. Parc Naturel Régional de Brière, 214 rue du Chef de l'île, 44 720 Saint-Joachim, France.
4. Syndicat du Bassin Versant du Brivet, 5 Rue des Frères Lumière, 44160 Pontchâteau, France.
5. Aix-Marseille Université, UMR AMSE (CNRS, EHESS, Ecole Centrale de Marseille, Université d'Aix-Marseille), 5 boulevard Maurice Bourdet, CS 50498, 13205 Marseille Cedex 01, France.
6. Paris-Saclay Applied Economics, Université Paris-Saclay, INRAE, AgroParisTech, 91120 Palaiseau, France.
7. CEE-M, Université de Montpellier, CNRS, INRAE, Institut Agro, 34000 Montpellier, France.

* Corresponding author: eric.edeline@inrae.fr

## The problem:

* Damage costs due to invasive species increase faster than the budgets allocated to managing biological invasions (Diagne et al. 2021).

* Managers need quantitative tools to optimise when, where and how to implement management actions.

* Our aim: develop a new class of management-oriented bioeconomic models based on the data collected in wildlife-management structures.

## The Methods:

* Water primerose *Ludwigia grandiflora* invaded the marsh of the Brière Natural Regional Park (Fig. 1) during the mid 1990's.

* Since 2000: management through inventory surveys followed by manual uprooting operations.

<figure><img src="image_1.png" /><figcaption>**Fig. 1.** The Brière marsh. Top-left inset: Location of the Brière marsh in France. Main panel: The Marsh was split into **5 different regions** (Bougherara et al. 2022). Bottom-right inset: Regions differ in how the people use them, and then have different economic values.</figcaption></figure>

**Data:**

* Spatial inventory data for the 2000-2022 period: **4 Invasion states** by primerose; 0: No invasion, 1: sparse cover, 2: discontinuous cover, 3: continuous cover (Fig. 2).

* Manual uprooting operations with data on spatial reach, salary costs, other costs for the 2000-2022 period.

* 5 Regions differing in their economic value (Fig. 1).

**Data processing with GIS:**

* Whole marsh cut into a grid of 100 x 100 m square cells (1 ha).

* In each grid cell each year: computation of mean invasion state, Uprooting Effort (m² treated) and management costs (€).

**Data modelling:**

Bayesian bioeconomic model:

* **Economic module:** cost-effort relationship depending on Invasion states and Regions.

* **Ecological module:** dynamic multi-state occupancy where next year's transition probabilities between Invasion states depend on Regions (Fig. 1) and Uprooting Effort.

<figure><img src="image_2.png" /><figcaption>**Fig. 2.** Spatial inventory of a continuous primerose cover in the Brière marsh.</figcaption></figure>

## Preliminary results:

* Management costs are Invasion state- and Region-dependent (Fig. 3).

* Invasion states ans Regions interact on management costs.

* Transition probabilities to lower Invasion states are both Region- and Effort-independent at the scale of a 1-ha grid cell (Fig. 4).

* Uprooting is useful only at a tiny spatial scale (navigation channel) and does not impact Invasion state at the grid-cell level.

* From Invasion state 3 (continuous cover), primerose cover is unlikely to decrease at all.

* From Invasion states 2 and 1 (discontinuous and sparse covers, respectively), some recovery to the next lower invasion state is possible.

<figure><img src="image_3.png" /><figcaption>**Fig. 3.** Cost-effort relationship. Symbols represent the raw data. Lines represent median posterior estimates with 95% credible intervals from the economic module of the Bayesian bio-economic model.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 4.** Effects of uprooting effort on probability of decreasing primerose cover the next year. Lines represent median posterior estimates with 95% credible intervals from the ecological module of the Bayesian bio-economic model.</figcaption></figure>

## Discussion:

* GIS digitalization is a key step of the approach (and a big work!).

* Wildlife structures should invest in accurate GIS monitoring for their ecological, effort and economic data.

* Next version of ecological module will include region-specific effects of Uprooting Effort, dispersal- and climate-dependency of Invasion-state dynamics.

**Acknowledgements:** This work was supported by the INRAE BIOSEFAIR metaprogram (SPABIO project).

ce PARIS-SACLAY
APPLIED ECONOMICS

INRAE

decod

SBVB
Syndicat du Bassin Versant du Brivet

<figure><img src="image_5.png" /></figure>

ECOBIO
Rennes

Aix*Marseille
université
Socialement engagée

<figure><img src="image_6.png" /></figure>

**References**

* Diagne, C., Leroy, B., Vaissière, A.-C., Gozlan, R. E., Roiz, D., Jarić, I., Salles, J.-M., Bradshaw, C. J. A. and Courchamp, F. 2021. High and rising economic costs of biological invasions worldwide. - Nature 592: 571–576.

* Bougherara, D., Courtois, P., David, M. and Weill, J. 2022. Spatial preferences for invasion management: a choice experiment on controlling *Ludwigia grandiflora* in a French regional park. - Biological Invasions 24: 1973–1993.