Data and code for: A delayed response in the area-concentrated search can improve foraging success

Area-concentrated search (ACS) is a simple movement rule implying that an animal searches for resources using a 'state-dependent correlated random walk'. Accordingly, a forager increases its searching intensity by reducing the directionality of movement ('intensive search mode' or ISM) when it detects a resource item, but if it searches unsuccessfully for a while, it returns to a more straight-line movement to search for new resource locations elsewhere ('extensive search mode' or ESM). We propose a modified ACS, called delayed-response ACS (dACS), which would be more efficient in resource collection than standard ACS. Instead of immediately switching from ESM to ISM when encountering a resource, as is done in standard ACS, an individual foraging in the dACS mode delays this switch by 'x' steps so it continues moving in a straight line for a while before switching to ISM. Our results show that an individual with a suitable delay parameter 'x' for the dACS achieves substantially higher f...,   For the simulations we created infinite landscapes with a single resource cluster. The cluster size and resource density varied among different scenarios. We utilized the Mat\'{e}rn Cluster Point Process using R version 3.5.3 and the ‘spatstat’ library version 1.58-2 to create resource clusters as a continuous spatial point pattern (Baddeley and Turner 2005). To understand the effects of the two landscape parameters, patch radius $r$ and resource density $u$ on foraging success (see below), we created landscapes with either the cluster radius fixed at $r=40$ and resource density $u$ set to 0.04, 0.16, or 0.64 resource items per unit area respectively, or with resource density fixed at $u=0.16$ and cluster radius varied from 20 to 40 and 80 (measured in step length). The expected number of active resource items per cluster ($\bar R$) is consequently calculated as  $\bar R=g_i^2 \pi \times u$. 
   As movement rule we implemented an area-concentrated search where..., , ## Data and code from: A delayed response in the area-concentrated search can improve foraging success

[https://doi.org/10.5061/dryad.vx0k6dk2r](https://doi.org/10.5061/dryad.vx0k6dk2r)

### Files and variables

#### File: Experiment\_1\_Fixed\_Patch\_Size\_-\_Varied\_Density.txt

**Description:** Scenarios with fixed cluster radius at 40 units and varied resource density (0.04, 0.16, 0.64 resources per unit area)

##### Variables

* Resource density (0.04, 0.16, 0.64 resources per unit area)
* Half-saturation constant (10, 20, 40, 80)
* Delay parameter (0, 2, 4, 6, . . . , 100 steps)
* Description for the header of data file
* radius --> Size of cluster radius (spatial units)
* halfsat --> Half-saturation constant
* N_active --> The number of active resource point in each scenario
* res_density --> Resource density (resources per unit area)
* angle --> Initial movement angle
* N_immi --> Number of immigrants into a patch (the number of patch border crossings)
* N_encount...,

区域集中搜索（Area-concentrated search，ACS）是一项简洁的运动规则，其核心逻辑为动物采用"状态依赖关联随机游走"搜寻资源。据此，觅食者若检测到资源单元，会通过降低运动方向性来提升搜索强度（即"强化搜索模式（Intensive Search Mode，ISM）"）；但若持续搜索无果，则会回归直线运动，前往其他区域搜寻新的资源点位（即"广域搜索模式（Extensive Search Mode，ESM）"）。

我们提出了一种改进型ACS，命名为延迟响应型ACS（delayed-response ACS，dACS），该算法在资源收集效率上优于标准ACS。与标准ACS中遇到资源后立即从广域搜索模式切换至强化搜索模式不同，采用dACS模式的觅食者会将这一切换延迟"x"步，即先继续直线运动一段时间，再切换至强化搜索模式。研究结果表明，为dACS设置合适的延迟参数"x"的个体，其觅食成功率显著更高……

本次模拟我们构建了仅包含单个资源簇的无限景观。不同实验场景下，簇的大小与资源密度各不相同。我们使用R语言版本3.5.3及‘spatstat’软件包版本1.58-2的马特恩聚类点过程（Matérn Cluster Point Process），将资源簇构建为连续空间点格局（Baddeley与Turner，2005）。为探究斑块半径$r$与资源密度$u$这两个景观参数对觅食成功率的影响（详见下文），我们设置了两类景观场景：一类固定斑块半径$r=40$，将资源密度$u$分别设为每单位面积0.04、0.16或0.64个资源单元；另一类固定资源密度$u=0.16$，将斑块半径设置为20、40与80（单位为步长）。每个资源簇的活跃资源单元期望数量$ar{R}$可通过如下公式计算：$ar{R}=g_i^2 pi imes u$。

我们所采用的运动规则为区域集中搜索，具体如下……

## 数据与代码来源：《区域集中搜索的延迟响应可提升觅食成功率》
[https://doi.org/10.5061/dryad.vx0k6dk2r](https://doi.org/10.5061/dryad.vx0k6dk2r)

### 文件与变量

#### 文件：Experiment_1_Fixed_Patch_Size_-_Varied_Density.txt
**描述：** 固定斑块半径为40单位、资源密度各异的实验场景（资源密度分别为0.04、0.16、0.64个资源单元/单位面积）

##### 变量
* 资源密度（0.04、0.16、0.64个资源单元/单位面积）
* 半饱和常数（half-saturation constant，10、20、40、80）
* 延迟参数（0、2、4、6、……、100步）
* 数据文件表头说明：
* radius → 斑块半径（空间单位）
* halfsat → 半饱和常数
* N_active → 各场景下的活跃资源点数量
* res_density → 资源密度（资源单元/单位面积）
* angle → 初始运动角度
* N_immi → 迁入斑块的个体数（即斑块边界穿越次数）
* N_encount……