Santa Monica Mountains Mountain Lions

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1 collaborator

Pete Aniello (Author)

WHAT IS IT?

A model of the Santa Mondica Mountains mountain lion population, and the effect of having a major highway separating lion habitat. This is based on an actual use case (see link in the Credits and References section).

HOW IT WORKS

Lions from both sides of the highway try to migrate to the other side of the highway in a weighted random manner. There's a relatively high chance they'll be run over on the highway. There is an option to create a corridor that allows movement from north to south and vice-versa, while protecting lions from death on the highway. There is also a chance the lions will breed and produce new lions, determined by the repro-success slider (newly "born" lions turn red).

HOW TO USE IT

Setup and Go, or optionally, Add Corridor, change initial number of lions, and or change reproductive success rate, then Setup and Go. A monitor and a graph keep track of the lion population. The model ends when there are no more lions left, or when the lions overpopulate (count > 1000).

THINGS TO NOTICE

Adding the corridor generally extends the time until lion "extinction" by about 30 percent. The reproductive success seems to have more effect than the initial number of lions.

THINGS TO TRY

Try running multiple times, both with and without the corridor. There is a BehaviorSpace experiment included that runs the model 20 times each, with the corridor and without it. Try different repro-success (reproductive success) settings.

EXTENDING THE MODEL

Could potentially add a prey species, such as deer, which the lions would then gain energy from. Deer would be equally vulnerable to road traffic. Also, the corridor could be widened, or more than one corridor could be used. Actual cars could be added, although the chances of dying on the road would or should be the same.

NETLOGO FEATURES

The model uses weighted random direction, where southern lions are slightly more likely to move north, and northern lions are slightly more likely to move south.

RELATED MODELS

Wolf Sheep Predation is somewhat similar.

CREDITS AND REFERENCES

See this story in the LA Times: http://www.latimes.com/local/lanow/la-me-ln-wildlife-corridor-20160422-story.html

Comments and Questions

Please start the discussion about this model! (You'll first need to log in.)

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extensions [rnd]

to setup
  clear-all
  ask patches [ setup-world ]
  add-lions
  reset-ticks
end 

to go
  move-lions
  ask turtles [reproduce-lions]
  tick
  if count turtles = 0 [stop] ;;stop when all lions are dead
  if count turtles >= 1000 [stop] ;;stop when lions overpopulate
end 

to setup-world ;; patch procedure...road gets black patches, all other areas are green
  ifelse pycor < 2 and pycor > -2
    [ set pcolor black ]
    [ set pcolor green ]

  ifelse add-corridor? [
    if pxcor >= -2 and pxcor <= 2 [ set pcolor green ]
    if pxcor = -3 or pxcor = 3 and pycor > -2 and pycor < 2 [ set pcolor red]
  ]
  []
end 

to-report random-between [ min-num max-num ] ;;process for reporting a random number between two numbers
    report random-float (max-num - min-num) + min-num
end 

to add-lions ;;add lions, but don't start any of them on the road
  create-turtles num-lions / 2 ;;south lions
    [set color 45
    set shape "cat"
    setxy random-xcor random-between min-pycor -2]
  create-turtles num-lions / 2 ;;north lions
    [set color 45
    set shape "cat"
    setxy random-xcor random-between max-pycor 2]
end 

to move-lions
  ask turtles [
    ifelse random 10 > 7 ;;30% chance of choosing a weighted random angle, or else choose random angle
      [set heading choose-angle]
      [right random 360]
    forward 1
    if pcolor = black [
      if random 10 > 5 [
        die ;;lions have a 50% chance of dying on each road patch
      ]
    ]
    if pcolor = red [ ;;lions bounce off of the guard rails
      right 180
      forward 1
    ]
  ]
end 

to-report choose-angle ;;process for reporting weighted random angle
  let values [0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90	91	92	93	94	95	96	97	98	99	100	101	102	103	104	105	106	107	108	109	110	111	112	113	114	115	116	117	118	119	120	121	122	123	124	125	126	127	128	129	130	131	132	133	134	135	136	137	138	139	140	141	142	143	144	145	146	147	148	149	150	151	152	153	154	155	156	157	158	159	160	161	162	163	164	165	166	167	168	169	170	171	172	173	174	175	176	177	178	179	180	181	182	183	184	185	186	187	188	189	190	191	192	193	194	195	196	197	198	199	200	201	202	203	204	205	206	207	208	209	210	211	212	213	214	215	216	217	218	219	220	221	222	223	224	225	226	227	228	229	230	231	232	233	234	235	236	237	238	239	240	241	242	243	244	245	246	247	248	249	250	251	252	253	254	255	256	257	258	259	260	261	262	263	264	265	266	267	268	269	270	271	272	273	274	275	276	277	278	279	280	281	282	283	284	285	286	287	288	289	290	291	292	293	294	295	296	297	298	299	300	301	302	303	304	305	306	307	308	309	310	311	312	313	314	315	316	317	318	319	320	321	322	323	324	325	326	327	328	329	330	331	332	333	334	335	336	337	338	339	340	341	342	343	344	345	346	347	348	349	350	351	352	353	354	355	356	357	358	359	360]
  let probN [0.02	0.012	0.01	0.009	0.008	0.008	0.008	0.007	0.007	0.007	0.006	0.006	0.006	0.006	0.006	0.005	0.005	0.005	0.005	0.005	0.005	0.004	0.004	0.004	0.004	0.004	0.004	0.004	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.001	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.003	0.003	0.003	0.003	0.003	0.004	0.004	0.004	0.004	0.004	0.004	0.004	0.005	0.005	0.005	0.005	0.005	0.005	0.006	0.006	0.006	0.006	0.006	0.007	0.007	0.007	0.008	0.008	0.008	0.009	0.01	0.012	0.02]
  let probS [0.001	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.0025	0.0025	0.0025	0.0025	0.0025	0.0025	0.0025	0.0025	0.0025	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.004	0.004	0.004	0.004	0.004	0.004	0.004	0.005	0.005	0.005	0.005	0.005	0.005	0.006	0.006	0.006	0.006	0.006	0.007	0.007	0.007	0.008	0.008	0.009	0.01	0.012	0.016	0.021	0.016	0.012	0.01	0.009	0.008	0.008	0.007	0.007	0.007	0.006	0.006	0.006	0.006	0.006	0.005	0.005	0.005	0.005	0.005	0.005	0.004	0.004	0.004	0.004	0.004	0.004	0.004	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.0025	0.0025	0.0025	0.0025	0.0025	0.0025	0.0025	0.0025	0.0025	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.002	0.001]
  let probabilities ifelse-value (pycor < 0) [probN] [probS] ;;northern lions biased south, southern lions biased north
  let pairs (map list values probabilities)
  report first rnd:weighted-one-of-list pairs [[p] -> last p]
end 

to reproduce-lions
  let mate one-of other turtles-here
  if mate != nobody [
   if random 100 <= repro-success [ ;;chance of successful reproduction if another lion is here at the same patch
      hatch 1 [right random 360 forward 5 set color red] ;;moving 5 helps prevent overclustering of newly hatched lions
    ]
  ]
end

There is only one version of this model, created over 7 years ago by Pete Aniello.

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